KisStartup

Nine Vietnamese scientists have been officially selected by the Royal Academy of Engineering (RAEng) to join LIF Global 2026 – Leaders in Innovation Fellowships. This marks the second consecutive year that KisStartup serves as the national partner of the programme, and the 10th year Vietnam has participated in this prestigious global initiative.

LIF Global – A rare opportunity for scientists to step out of the laboratory

LIF Global focuses on building entrepreneurial capacity, providing mentorship, and expanding networks for scientists who possess research outcomes with prototypes and strong commercialization potential.
Over 8 months, participants engage in approximately 20 days of online training with international experts and 14 days in the United Kingdom, immersing themselves in one of the world’s leading innovation and startup ecosystems.

What makes LIF Global unique is that it:

    •    Does not require equity or intellectual property rights,
    •    Covers almost all participation costs,
    •    Helps scientists not only “bring research to market” but also build sustainable, environmentally responsible, and socially meaningful business models.

In 2025, ten Vietnamese scientists were selected for the programme. Seven of them travelled to the UK and returned with concrete outcomes: expanded international networks, invitations to open overseas branches, investment opportunities, additional operational funding, and well-defined next steps for commercialization and global market expansion.

Building on that foundation, the selection of nine Vietnamese scientists for LIF Global 2026 once again highlights the quality and depth of Vietnam’s research community—especially in biotechnology, climate-smart agriculture, biomedical science, road safety, and climate data services.

Congratulations and Expectations
KisStartup proudly congratulates:
    •    Luu Nguyen Phu Thuong – Van Lang University
    •    Anh Tuan Tran – Lemit Foods
    •    Mai Linh Dinh – Hanoi University of Science and Technology
    •    TRAN TU – Ho Chi Minh City University of Technology
    •    Phuong-Thao Tran – Hanoi University of Pharmacy
    •    Hoang Nguyen – University of Science, VNU-HCM
    •    Hieu Tran-Van – University of Science, VNU-HCM
    •    Thi Mai Huong To – University of Science and Technology of Hanoi (USTH)
    •    Thi Kim Cuc Nguyen – Thuyloi University

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About the LIF Programme
The Leaders in Innovation Fellowships (LIF), run by the Royal Academy of Engineering, supports talented entrepreneurs in transforming engineering innovations into sustainable businesses that tackle environmental, economic, and social challenges. The programme equips entrepreneurs with startup and growth skills, facilitates investment readiness, and connects them to a global network of researchers, investors, and industry leaders.
The programme charges no fees and requires no equity or intellectual property rights.

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About KisStartup
KisStartup is a pioneer in Vietnam in the commercialization of scientific research, connecting scientists, businesses, and international organizations. Since 2025, KisStartup has partnered with the Royal Academy of Engineering to advance the LIF Global programme in Vietnam. In 2025, ten Vietnamese projects were selected, with seven successfully completing the programme.
We are committed to serving as an effective bridge between Vietnamese scientists and the UK innovation ecosystem, helping elevate Vietnam’s research commercialization to the international stage.

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LIF Programme Contact in Vietnam
Email: hello@kisstartup.com | minh@kisstartup.com | phong.kisstartup@gmail.com
Hotline: +84 879 300 303 (Mr. Phong)

1. Anh Tuan Tran – Lemit Foods
Lemit Foods: Upcycled Protein – The Net-Zero Jackfruit Solution
Lemit tackles the problem of 382,500 tons of unripe jackfruit wasted annually and the rising demand for sustainable protein by creating an “Upcycled Protein” platform. Their solution turns unripe jackfruit into fermented jackfruit powder for B2B applications and ready-to-cook B2C product lines. The model both reduces emissions (by 60–90% compared to meat) and increases farmers’ income by 20–25%, while aiming to build a real-time MRV (measurement–reporting–verification) system for carbon impact.
LIF Global will support Lemit in refining its international expansion strategy, positioning within global alternative-protein supply chains, and forming partnerships with major food manufacturers—where net-zero commitments, inclusive business models, and impact data become competitive advantages.

2. Mai Linh Dinh – Hanoi University of Science and Technology
BioGel Solus – Edible Hydrogel Coating from Banana Trunks
BioGel Solus transforms banana trunks—typically considered agricultural “waste”—into biodegradable, edible hydrogel films that coat fruit and extend shelf life by up to two times, reducing post-harvest loss at low cost. Production residue becomes slow-release fertilizer, forming a truly zero-waste model.
The commercial potential lies in the multi-billion-dollar markets of post-harvest solutions and biomaterials. The product targets smallholder farmers and export-oriented supply chains—where extended shelf life, reduced plastic use, and improved income are all critical. BioGel is a strong example of how green material technology can directly support agriculture and food security.

3. Thi Kim Cuc Nguyen – Thuyloi University
Koji Technology – Koji Fermentation for Specialty Coffee and Circular Economy
Thi Kim Cuc Nguyen’s Koji fermentation solution uses Aspergillus oryzae to elevate coffee flavor—producing notes of chocolate, peach, and apple, and increasing SCA scores by around 10%. The process also generates valuable byproducts such as kombucha and compost, minimizing waste. The project already has a patent application, has been presented at VAST, and has been tested with farmers—many of whom are ethnic minority women.
The potential of Koji Technology goes beyond better-tasting coffee. It lies in building an inclusive specialty coffee value chain where farmers capture more value from each coffee cherry, women are economically empowered, and SDG goals 1, 3, 5, and 12 are embedded in a meaningful way.

4. Hoang Nguyen – University of Science, VNU-HCM
CymbionX – Beneficial Microorganisms & Biostimulants from Food Waste
CymbionX combines mycorrhizal fungi and biostimulants extracted from food waste to create a microbial formula with highly infective AMF spores of micrometer size. The product is easy to apply through liquid solutions, drone spraying, or drip irrigation.
Amid rising chemical fertilizer costs and increasing pressure to reduce agricultural emissions, CymbionX offers a new class of biological agriculture solution: improving plant and soil health, reducing reliance on chemical fertilizers, and enabling access to demanding markets like North America and Europe—where microbial products linked with emissions data and soil-health metrics are in high demand.

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About the LIF Programme
The Leaders in Innovation Fellowships (LIF), run by the Royal Academy of Engineering, supports talented entrepreneurs in transforming engineering innovations into sustainable businesses that tackle environmental, economic, and social challenges. The programme equips entrepreneurs with startup and growth skills, facilitates investment readiness, and connects them to a global network of researchers, investors, and industry leaders.
The programme charges no fees and requires no equity or intellectual property rights.

---

About KisStartup
KisStartup is a pioneer in Vietnam in the commercialization of scientific research, connecting scientists, businesses, and international organizations. Since 2025, KisStartup has partnered with the Royal Academy of Engineering to advance the LIF Global programme in Vietnam. In 2025, ten Vietnamese projects were selected, with seven successfully completing the programme.
We are committed to serving as an effective bridge between Vietnamese scientists and the UK innovation ecosystem, helping elevate Vietnam’s research commercialization to the international stage.

---

LIF Programme Contact in Vietnam
Email: hello@kisstartup.com | minh@kisstartup.com | phong.kisstartup@gmail.com
Hotline: +84 879 300 303 (Mr. Phong)

Nine Vietnamese scientists have been officially selected by the Royal Academy of Engineering (RAEng) to join LIF Global 2026 – Leaders in Innovation Fellowships. This marks the second consecutive year that KisStartup serves as the national partner of the programme, and the 10th year Vietnam has participated in this prestigious global initiative.

LIF Global – A rare opportunity for scientists to step out of the laboratory

LIF Global focuses on building entrepreneurial capacity, providing mentorship, and expanding networks for scientists who possess research outcomes with prototypes and strong commercialization potential.

Over 8 months, participants engage in approximately 20 days of online training with international experts and 14 days in the United Kingdom, immersing themselves in one of the world’s leading innovation and startup ecosystems.

What makes LIF Global unique is that it:

• Does not require equity or intellectual property rights,

• Covers almost all participation costs,

• Helps scientists not only “bring research to market” but also build sustainable, environmentally responsible, and socially meaningful business models.

In 2025, ten Vietnamese scientists were selected for the programme. Seven of them travelled to the UK and returned with concrete outcomes: expanded international networks, invitations to open overseas branches, investment opportunities, additional operational funding, and well-defined next steps for commercialization and global market expansion.

Building on that foundation, the selection of nine Vietnamese scientists for LIF Global 2026 once again highlights the quality and depth of Vietnam’s research community—especially in biotechnology, climate-smart agriculture, biomedical science, road safety, and climate data services.

Congratulations and Expectations

KisStartup proudly congratulates:

• Luu Nguyen Phu Thuong – Van Lang University

• Anh Tuan Tran – Lemit Foods

• Mai Linh Dinh – Hanoi University of Science and Technology

• TRAN TU – Ho Chi Minh City University of Technology

• Phuong-Thao Tran – Hanoi University of Pharmacy

• Hoang Nguyen – University of Science, VNU-HCM

• Hieu Tran-Van – University of Science, VNU-HCM

• Thi Mai Huong To – University of Science and Technology of Hanoi (USTH)

• Thi Kim Cuc Nguyen – Thuyloi University

---

About the LIF Programme

The Leaders in Innovation Fellowships (LIF), run by the Royal Academy of Engineering, supports talented entrepreneurs in transforming engineering innovations into sustainable businesses that tackle environmental, economic, and social challenges. The programme equips entrepreneurs with startup and growth skills, facilitates investment readiness, and connects them to a global network of researchers, investors, and industry leaders.

The programme charges no fees and requires no equity or intellectual property rights.

---

About KisStartup

KisStartup is a pioneer in Vietnam in the commercialization of scientific research, connecting scientists, businesses, and international organizations. Since 2025, KisStartup has partnered with the Royal Academy of Engineering to advance the LIF Global programme in Vietnam. In 2025, ten Vietnamese projects were selected, with seven successfully completing the programme.

We are committed to serving as an effective bridge between Vietnamese scientists and the UK innovation ecosystem, helping elevate Vietnam’s research commercialization to the international stage.

---

LIF Programme Contact in Vietnam

Email: hello@kisstartup.com | minh@kisstartup.com | phong.kisstartup@gmail.com

Hotline: +84 879 300 303 (Mr. Phong)

 

In marketing, people often begin with the question: “What story should we tell to make it compelling?” With the Lean Startup mindset, I would switch the order: “Which story has been validated to truly resonate with what customers are seeking?” When we change the question, the approach also shifts: instead of writing a grand narrative about the brand, we design small experiments with messages, usage scenarios, social proof, and calls to action; we measure responses like researchers, letting data guide us to the right story.

Three building blocks form what I call “lean storytelling”: human touchpoints, numbers that speak, and learning loops. When these pieces turn in sync, they transform ordinary words into real revenue—and more importantly, sustained trust.

Human Touchpoints: Stories Begin with Real Moments

Every product–service story should be anchored in a concrete moment: where the customer is, what they are facing, what frustration or expectation arises. At KisStartup, we often open our notebooks right after an interview to capture the customer’s “golden quote.” A homestay owner once told us: “I’m only afraid customers will say it looks nice but not like the photos.” We used that exact line as the headline for an interior service landing page: “Beautiful like the photos—only more durable.” Not fancy words, just authenticity.

Lean storytelling is not about polishing the product—it’s about returning real life to the words. If a customer says “easier on my hands,” don’t rewrite it as “optimized operation.” If they sigh with relief because they “no longer have to watch the clock to collect payments,” don’t dress it up as “improved cash flow.” Keep the raw texture of life, then add a minimal proof point: a before–after photo, an on-time invoice screenshot, a 12-second clip of real usage. The story gains weight because it preserves traces of reality.

Numbers That Speak: Every Story Needs a Measurement Lever

A good story evokes emotion. A correct story demonstrates changed behavior. Lean forces us to attach each content piece to a single measurable goal: booking a call, leaving a phone number, downloading a document, adding to cart, returning to purchase. I appreciate how small numbers lead the way.

Start with two versions of the same story: one emphasizing pain points, the other highlighting the desired future. Put them on two almost-identical landing pages—only the headline and intro differ—and split traffic for 48–72 hours. If the “pain point” version yields a 6.3% form-fill rate compared to 3.8% for the “future scenario” version, you have your first answer: customers respond more strongly to problem-solving than dreamy futures. But don’t stop there; examine the quality of the conversions—call duration, booking rate, questions asked. Numbers don’t replace listening, but they help you prioritize what to listen to.

Thin but useful indicators keep the story tied to behavior: time spent reading to 75%, save–share vs. like ratios, direct traffic percentage two weeks post-launch (a signal of brand recall), conversion rate of viewers who reach second 10 of a video, number of branded keywords searched after a campaign. You don’t need dozens of dashboards—just a few clear curves to make decisions.

The Learning Loop: Write – Measure – Adjust – Rewrite

Each story is a content MVP. Don’t wait for perfection—run through the loop instead. Day 1: publish a 250-word story about a “pain-trigger moment.” Day 3: based on responses, create a real-use video incorporating the customer’s exact quote and add subtitles. Day 6: write a 600-word case with post-use numbers. Change only one element per loop—headline, timeline, call to action, or social proof. Changing one variable at a time reveals what actually moves the needle.

Choose the right format for each story: “hear–see” fits short videos; “before–after” fits carousels; “benefit calculation” fits landing pages with savings tables. When a story shows traction—price inquiries, inbox messages for samples, email replies—archive it as a long-term asset: add it to your Customer Stories page, sales deck, or team training materials using the exact customer language.

Practice: Build a Lean Story Frame in One Afternoon

Suppose you sell a revenue–expense management solution for homestays. Start with one real person. Call them and ask three questions: “When was your last late payment?”, “How did you handle it?”, “What do you most fear repeating?” Keep the one quote that makes you sit up—use it as the opening line. Then describe the usage situation in 4–5 simple lines—no hype. Add minimal proof: a reconciled report screenshot or a message saying “no more watching the clock.” End with a small action: “Book a 15-minute demo—we’ll use your actual data.”

If possible, add a before–after metric over 14 days: “Late payments dropped from 7 to 2; average collection time decreased from 41 to 26 days.” Don’t claim “37% time saved” unless you’ve actually measured it; say exactly what’s true, then promise to update in 30 days. Marketing becomes not a promise, but a shared improvement journal.

Balancing Story and Data: Don’t Let Numbers Dry Out Words, or Words Blur Numbers

The trap in marketing is either “measuring by feeling” or “measuring everything.” Lean teaches focus: if this month’s objective is onboarding 20 new B2B clients, track three indicators—appointments from stories, appointment-to-trial rate, trial-to-contract rate. Let stories serve these ratios: open with copy that gets appointments, use short cases to secure trials, and simple ROI tables to close contracts. Numbers become the heartbeat of the words.

Conversely, don’t let words drown numbers. If a story performs well online but doesn’t appear in the CRM as “appointments,” ask: is the call to action clear? Is the timeline specific? Is the booking page mobile-friendly? A small tweak—from “Contact us for more info” to “Book a 15-minute demo with your data”—can move the numbers.

Ethics of Storytelling: Honesty, Respect, and Anti-Impact-Washing

The best stories are true stories. When using community data (artisans, farmers, patients…), ask permission, explain the purpose, credit contributors, share benefits. When discussing social or environmental impact, separate outputs from outcomes: training sessions don’t equal increased income; trees planted don’t equal restored biodiversity. Marketing may soar, but its wings must be stitched with honesty.

Two Examples, One Principle
Example A – “Pain Relief” Rhythm (B2B)

“Three late payments in a month made Ms. Hoa dread phone calls. ‘I hate the line “tomorrow, please,” when tomorrow never comes.’ After 14 days using automated revenue–expense tools, late payments dropped from 7 to 2, collection time decreased from 41 to 26 days. She said: ‘I no longer have to send reminders. The system does it—I don’t have to be the bad guy.’ Want to see a sample report using your data? Book a 15-minute demo—no commitments.”

Example B – “Future Vision” Rhythm (B2C)

“Minh roasts coffee and believes his beans ‘carry the highland spirit,’ yet online sales stagnate. ‘I want customers to drink it and immediately want to tell a friend.’ We suggested something simple: capture the exact dawn moment of roasting the first batch—keep the lid pop, keep the laughter. In a 58-second video, 42% watched to second 30; trial orders rose 2.1× week-over-week; 27% bought a 500g bag within 10 days. Minh said: ‘Maybe I should stop being philosophical and let customers hear real life.’ Do you have 90 spare minutes? Let’s build a 58-second story together.”

Both examples rest on one principle: real voice, real scenes, real numbers, small calls to action.

Embedding Lean Storytelling into the Organization

Once the team sees lean storytelling’s value, turn it into habit. Weekly: choose a real-life moment. Monthly: choose one vital metric. Quarterly: host a “word surgery” session to analyze high-converting stories and why. Store quotes, screenshots, and raw videos in a shared folder—name files by date–channel–goal. In just a few months, you’ll have a rich internal library enabling sales, customer service, and product teams to speak the same language: the customer’s language.

Write Less to Sell More

Lean storytelling isn’t about writing fewer words—it’s about removing words that don’t matter. When we center people, let numbers guide us, and honor the learning loop, marketing becomes less flashy, less generic, and more grounded. Customers don’t need us to be perfect—they need to feel understood, see us experiment, measure, improve. The story becomes not a poster but a handshake: warm, concise, trustworthy.

If you want one exercise for this afternoon: call a past customer, ask permission to record one honest moment of frustration—or joy—when using your product. Write 200 words around that quote, add a small proof, publish it with a specific call to action. Check numbers three days later. You may discover you weren’t missing a “big idea”—just two small metrics and one true sentence.

© Copyright belongs to KisStartup. Any copying, quoting, or reuse must cite KisStartup as the source.

 

TômTex is becoming one of the most fascinating examples of how biotechnology and the circular economy can redefine the material landscape for the fashion industry. Born from a very “everyday” question — how far can shrimp shells, crab shells, coffee grounds, and fungi go in the global value chain — TômTex has built an ambitious business model: transforming agro-marine by-products into sustainable bio-based “leather,” targeting high-end fashion, interior design, and premium packaging markets.

Business Model: From Waste to High-Value Materials
Instead of investing in new raw-material farms or costly lab-grown cultivation, TômTex starts with the massive waste streams of seafood and agriculture. Shrimp shells, crab shells, coffee grounds, and fungal by-products — typically considered environmental treatment costs for businesses — are “upcycled” into inputs for biotechnology. Low cost, stable supply, and the added benefit of waste reduction form the first layer of value.

The core lies in TômTex’s proprietary green biochemical technology: chitosan extracted from shrimp shells is processed and combined with bio-based binders and natural pigments — with no plastics and no toxic solvents. The result is a material that can be printed, embossed, and pressed into various structures, mimicking cow leather, suede, or even PVC-like surfaces while remaining fully bio-based and biodegradable. Production costs are designed to approximate — or undercut — conventional cow leather, a crucial condition for mass commercialization.

In terms of market strategy, TômTex adopts a B2B model: selling materials to fashion, interior, automotive, and premium-packaging brands, while co-creating designs with influential designers and labels. Appearing on runways, at fashion weeks, and in experimental product lines of major brands not only generates initial revenue but, more importantly, builds “social proof” that this new material is beautiful, durable, and credible enough for the premium segment. Once trust is established, the logical next step is expansion into more affordable product lines and becoming a platform-level material supplier for OEM manufacturers.

The model also opens up a “Vietnam-rooted, globally scaled” pathway: placing R&D near fashion hubs and manufacturing technology centers while gradually shifting production back to Vietnam to leverage abundant shrimp, coffee, and fungal by-products — forming a closed-loop value chain from surimi plants, shrimp-processing factories, and coffee roasters to bio-material manufacturing and fashion–interior ecosystems.

Competitive Advantages and Global Comparison
In the landscape of next-generation bio-leather startups, TômTex stands alongside MycoWorks, Bolt Threads, and Desserto — but follows a different path. Many competitors invest heavily in mycelium cultivation or synthetic spider-silk proteins in tightly controlled environments — which ensures consistency but requires high capital and operating costs. Desserto uses cactus — strong in sustainable agriculture and “green” branding — but still faces technical challenges in processing, preservation, and additives for durability.

TômTex avoids the route of “new farming” or “new cultivation” and instead builds on existing waste streams. If executed well, this model creates a cost advantage that is difficult to replicate: near-zero raw material cost, potentially even “negative cost” if seafood companies treat it as waste-processing service. Chitosan processing, formulation, and structural engineering form the hard-to-copy technological core, especially once protected by IP and refined through long-term experimentation. Strategic partnerships with seafood enterprises, such as VNF, add another “moat” in the supply chain: whoever controls stable, pre-processed waste streams gains an edge in both price and quality.

However, from a critical perspective, TômTex still faces challenges: ensuring industrial-scale consistency; meeting strict standards for mechanical strength, moisture resistance, mold resistance, and colorfastness required by fashion and automotive sectors; and avoiding “greenwashing” skepticism unless product lifecycle and end-of-life biodegradability are clearly demonstrated. Competition in the vegan-leather space is intensifying, requiring continuous innovation to avoid being leapfrogged by newer technologies.

Opportunities for Biotechnology and Fashion Materials in Vietnam
If TômTex is seen as an “open case study,” the crucial question for Vietnam is: how can Vietnam move beyond supplying shrimp shells and coffee grounds to becoming an R&D and manufacturing hub for bio-materials in the global fashion supply chain?

Vietnam possesses a rare combination of assets: among the world’s top exporters of shrimp, pangasius, and coffee; a major global manufacturing base for textiles, apparel, and footwear; a network of universities and research institutes in biotechnology, biochemistry, and materials; and increasing pressure from international brands’ emission-reduction and ESG commitments. In other words, Vietnam has both the motivation and the resources.

Yet the linkages between labs, factories, and fashion brands remain weak. Many biotech research projects stop at academic publications, while textile and footwear firms mainly operate as OEMs dependent on imported materials. TômTex suggests a new model: bio-material startups positioned at the center, speaking “the language of all three sides” — understanding biological mechanisms, material technical requirements, and the aesthetic and business needs of designers and brands.

If Vietnam could build multiple “new-generation TômTex,” but with a broader range of raw materials — from shrimp and crab shells to coffee husks, cashew shells, banana trunks, coconut fibers, durian husks — the country could turn agricultural waste pressure into a national competitive advantage in green materials. This requires not only technology but also ecosystem and policy: encouraging collaborative experiments between startups, research institutes, and seafood companies; designing benefit-sharing mechanisms across the value chain; and supporting IP protection and international standard testing for new materials.

Strategic Potential for a Sustainable Strength
From a strategic perspective, TômTex presents an intriguing proposition: Vietnam can move up the global value chain not only by upgrading manufacturing capability but also by owning the next generation of foundational materials for the fashion and interior industries. If Vietnam can combine three pillars — green biotechnology, abundant agro-marine by-products, and existing fashion-design–manufacturing capabilities — the country could position itself as a regional “bio-fashion material hub.”

This requires long-term thinking: treating waste as a strategic asset; treating bio-material startups as key components of a sustainable fashion-industry strategy; and treating pioneers like TômTex as partners for learning, transfer, and co-creation — not merely as buyers of raw materials. If achieved, the journey “from shrimp shells to the fashion runway” would not only be the inspiring story of a single startup but also a story of national value-chain upgrading.

© Copyright belongs to KisStartup. Any reproduction, citation, or reuse must credit KisStartup as the source.

Nguyễn Đặng Tuấn Minh

There is an uncomfortable truth: most pitching sessions fail not because the idea is bad, but because there is no evidence of learning. Investors don’t buy blueprints; they fund disciplined learning. Lean Startup gives us the language and rhythm to turn fundraising into a real Build–Measure–Learn cycle: build a small step forward, measure with “real signals,” and learn to make the next decision. When you raise capital this way, you’re not “begging” for money; you’re inviting investors into a running loop of progress.

Over ten years of accompanying startups, KisStartup has seen both sides: deals that unlocked growth at the right moment—and opportunities lost simply because the data was hollow, the signals were noisy, or expectations were misaligned. This article synthesizes a practice-oriented perspective: what investors expect, where startups typically go wrong, and how to raise capital leanly—lean in assumptions, lean in evidence, lean in narrative.

The “marriage” between investors and startups begins with… progress

Comparing “choosing investors to choosing a life partner” isn’t just a fun metaphor. Marriage operates on trust; the strongest trust is built on repeated behavior. It’s the same in startups: professional investors rarely require a perfect solution; they look for a trajectory of progress. How do you understand the market better today than yesterday? Is this learning repeatable? Do you have the discipline to keep going when assumptions break?

From their perspective, four lenses appear frequently—not as a static checklist, but as a way to read your learning loop:

• Market: large enough size, painful enough demand, open timing window. What they want to see is behavioral proof: deposits, trial payments, pilot contracts, binding letters of intent (LOI with terms).
• Team: ability to learn fast, complementary roles, just enough consistency to avoid random pivots, and just enough humility to correct early.
• Product/Technology: what is new, what is hard to copy, and more importantly: which real pain this novelty has already touched in the field.
• Finance & Model: how you make money today, how that changes as you scale, and which assumptions have been validated with data.

If fundraising is “selling the future using present-day evidence,” then the most valuable evidence isn’t glossy slides—it’s the trace of validated learning.

Three common mistakes we encounter
1) “Market validation is the staff’s job”

Outsourced surveys, dozens of superficial interviews, reports filled with charts—but the founder has never spent an hour with a real customer. The result? Strategic decisions based on the team’s “echo,” not the customer’s voice. Lean demands the opposite: the founder must be the first person to hold raw data. You can delegate the running, but not the understanding.

Practical suggestion: for every major assumption cycle (problem, solution, pricing, channel), conduct at least 20 deep conversations led directly by a founder. Each conversation needs a timestamp, current cost, decision influencer, and a small commitment behavior after the interview (signing up for pilot, leaving payment info, refundable deposit). Without behavior, data remains… opinion.

2) “More than 100 data points is enough

The number of interviews doesn’t equal depth of learning. We’ve seen spreadsheets boasting “100 responses,” but the questions are closed, the answers are polite, and no real motivations are revealed. Investors value insight saturation, not sample count. Saturation appears when answers begin repeating within each target segment, and each segment links to a clear action implication (message change, channel shift, package restructuring, payer change).

Practical suggestion: instead of showing “100 surveys,” highlight three pivotal insights that led to three decisions and three measurable changes (e.g., CTA change increased sign-up completion from 9% to 17% in 14 days on channel X; pricing moved from A to B, paid-pilot close rate doubled; removed feature C, onboarding time dropped 30%).

3) “Not preparing traction as a learning story”

Traction is not “how much revenue”; it is the chain of evidence showing you’re approaching product–market fit. Many teams bring aggregated totals (downloads, sign-ups) and stop there. These numbers rarely convince. Investors want context: cohort return rates, B2B sales cycle length, CAC at pilot scale, funnel conversion step-by-step, willingness-to-pay after experiencing—or not experiencing—the core feature.

Practical suggestion: tell traction as a storyline:
“January: validated problem with 27 B2B customers; February: ran 5 paid pilots; March: closed first 12-month contract at USD 2,000 with renewal clause; sales cycle dropped from 78 to 49 days after changing ROI messaging; NPS for users of feature X is 46; 90-day churn 3.8% due to [reason], resolved by [action].”
Any number that doesn’t trigger a next action is decoration.

Lean fundraising: build the loop of learning – validation – raising
Define the assumptions of your fundraising cycle

The money you seek is fuel for a big experiment, not a warm blanket. State clearly: with amount Y in Z months, you will prove A–B–C at what standard (e.g., 20 B2B paid contracts at minimum USD 1,500/year; sales cycle < 60 days; CAC < 40% of first-year LTV). When standards are clear, decision branches are clear: hit → scale; miss → cut/pivot.

Create a lean Data Room

A lightweight but complete data room signals disciplined information management—a strong sign of execution capability. In practice, 8–10 documents are enough for early rounds:

• One-pager & deck (problem, solution, market, model, team, fundraising roadmap)
• Traction timeline with annotations on “pivot points”: what changed – why – results
• Customer interviews/insight summaries (include 5–7 strong verbatim quotes)
• 12–18 month plan: milestones, budget by category, key assumptions, risks & mitigations
• Unit economics table (to the extent of “knowing what you don’t know”: weak assumptions & how you're validating them)
• Framework term sheet (capital needed, use of funds, runway, milestones for next round)

“Investment is also learning”: choose investors like co-authors

Lean doesn’t encourage “taking money at all costs.” The best round is the one that adds intelligence. A simple practice: ask reverse questions. You are not in a “petition–approval” position; you’re finding a partner. Direct questions save enormous pain:

• What is your maximum check size for this round, and what role do you expect to play?
• How long is your due-diligence process, and what points can cause a stop?
• In your current portfolio, what is the most recent success/failure case and key lesson?
• Expected exit horizon? What level of operational involvement is “ideal” for you?

Their answers reveal alignment. Good partnerships start with honest agreements.

Storytelling that makes investors want to “enter your loop”

Don’t present like a dry chronicle. Tell it like an investigation:

• We believed X.
• We defined X by behavior Y and set standard Z.
• We tried A; results diverged; root cause was B.
• We fixed C; remeasured; trajectory shifted to D.
• Now we need capital to validate E at scale F before unlocking G.

This narrative sounds honest and shows you’re steering. That builds trust.

A note for the Vietnamese market

We are fast adopters of technology, but many companies are slow to build data discipline. When raising capital, this weakness shows instantly: scattered data, non-standard definitions, no chain of decisions tied to data. Fixing it isn’t hard, but requires commitment:

• Standardize internal metric definitions (active user, MQL/SQL, churn, MRR/ARR, CAC/LTV…)
• Map data touchpoints along the customer journey and assign “ownership” for each
• Design a living traction dashboard: update 5–7 key metrics weekly with root-cause notes + actions
• Store raw customer voice; a few honest quotes often beat a hundred rows of numbers

Investors don’t demand perfection; they demand that you are becoming more correct—with evidence.

Fundraising is also a product—and Lean is how you “design” it

Treat fundraising as a “product” you must fit to a specific investor segment. Define your “customer profile” (sector, risk appetite, check size, horizon), test “distribution channels” (warm intros, demo days, angel communities, sector-focused funds), price reasonably for your stage (reflecting risk + potential, not dreams), measure “conversion rates” across steps (open email → schedule meeting → due diligence → term sheet → disbursement), and learn at each drop-off node.

Lean doesn’t guarantee you’ll secure funding; it guarantees you’ll secure yourself: knowing what you’re learning, how far you’ve learned, and what you need to learn next with new resources. When you enter the meeting with that mindset, any pitch—successful or not—becomes a profitable learning loop. Because whether you receive money or not, you walk out with better questions and clearer evidence for the next cycle.

And that is the essence of Lean Startup: not asking for permission to continue—but learning enough to continue.

© Copyright belongs to KisStartup. Any form of copying, quoting, or reuse must clearly cite KisStartup as the source.
 

Nguyễn Đặng Tuấn Minh

In 2025, KisStartup reaches the milestone of 10 years accompanying Vietnam’s innovation ecosystem. Ten years may not be long in history, but it is enough for us to witness the maturity of a new generation of entrepreneurs—those who dare to try, dare to fail, dare to learn, and dare to start again. Along this journey, we discovered a simple truth: entrepreneurship does not begin with capital—it begins with the capacity to learn.

That is why, as we look back and prepare for a new decade, KisStartup has chosen to invest in the learning capacity of the community by building NEXA15—an online learning platform that is not just a library of courses but a Lean Learning Platform, where each lesson is a Build–Measure–Learn cycle designed for learners to immediately apply in real life.

Why We Choose the Lean Path in Learning

Lean Startup teaches us that every idea only becomes valuable when tested through action. After 10 years of working with thousands of entrepreneurs, we realized that most early failures do not stem from a lack of ideas or capital—but from a lack of the right learning method.

Learners—like entrepreneurs—often fall into three traps:

1. Accumulating knowledge without taking action,
2. Acting without measuring,
3. Measuring without learning.

We aim to break those three traps. NEXA15 is designed so that every module does not end at “understanding,” but requires learners to try, measure, and learn again.

Each lesson is a small experiment—where you cannot just watch a video; you must apply and record your feedback. That is “Lean in learning”: learn just enough, act immediately, fail small, and improve fast.

Lean Learning for Everyone – Not Just Startups

Lean thinking is not limited to technology or early-stage founders. It is a way of thinking and acting in a constantly changing world.

• For students, Lean helps you self-discover, self-reflect, and find your own path.
• For SMEs, Lean helps you validate business ideas quickly, reduce investment risks, and accelerate digital transformation.
• For educators and startup support organizations, Lean provides a structured way to design training, incubation, and coaching programs grounded in real-world feedback.
• For policymakers and managers, Lean offers a way to observe societal change—not through thick reports, but through evidence gathered from the field.

Thus, NEXA15 is not “a course”—it is a practical knowledge platform designed for anyone to learn and build in their own way.

Why NEXA15 Was Created – Making Learning and Knowledge Sharing Lean

Throughout our journey, KisStartup has designed hundreds of training programs and accompanied startup teams across Vietnam. We noticed a common issue: knowledge is passed on in short workshops, but there is rarely a mechanism to help learners maintain long-term habits of learning and applying.

NEXA15 was created to fill exactly that gap.

Named after NEXA—short for Next Action, Next Learning, Next Impact—and “15” representing 15 minutes of daily learning and practice, the platform is built on three principles:

1. Learn to act, not learn to know – every course includes an Action Task tied to the learner’s real-life context.
2. Lean and measurable – short duration, focused content, and built-in progress assessment.
3. Continuously updated and co-created – content developed from the reality of Vietnamese businesses, refined through feedback from learners and mentors.

In 2025, marking our 10-year journey, we launched a series of 10 Lean courses on Thinkific—distilled from thousands of hours of teaching, research, consulting, and hands-on work.

The 10 Lean Courses on NEXA15
1. Innovation-driven Entrepreneurship – Basic & Advanced

→ Provides frameworks and tools to identify, validate, and scale business models.

2. Digital Transformation for Micro, Small, and Medium Enterprises

→ Helps SMEs understand, select, and apply digital technologies effectively and inclusively.

3. Startup Ecosystems and Global Support Models

→ Analyzes the roles of stakeholders, investors, support organizations, governments, and businesses in fostering innovation.

4. Impact Businesses & Impact Innovation

→ Guides entrepreneurs in measuring impact and building sustainable business models balancing social, environmental, and economic goals.

5. Intellectual Property in Startups

→ Helps founders understand and leverage IP as a strategic asset.

6. Entrepreneurship Based on Cultural Heritage (Basic)

→ Shows how to combine cultural value with business thinking to create unique creative products.

7. AI for Innovation – Basic & Advanced

→ Enables startups to use AI for market research, product design, and business model optimization.

8. ESG for Businesses

→ Introduces practical standards, tools, and roadmaps for SMEs to adopt ESG.

9. Data Asset Management for SMEs

→ Guides businesses in building data strategies and protecting digital assets.

10. Lean Thinking for Founders

→ Synthesizes Lean Startup, Design Thinking, Effectuation, and AI-driven Innovation—the foundation of all KisStartup programs.

Lean Learning to Create Sustainable Value

Each NEXA15 course is not just a lecture, but a real story—drawn from ten years of working alongside Vietnamese businesses, especially founders who have experienced both success and failure.

We believe that knowledge becomes meaningful only when validated through experience, and experience becomes valuable only when given time for reflection.

That’s why every course integrates three components:

• Real case studies from KisStartup’s projects,
• Short action exercises for immediate application,
• Feedback and mentoring mechanisms to help learners not just “learn once,” but learn for life.

From Community – Toward Sustainable Creative Value

Over the past decade, KisStartup has been fortunate to learn from many inspiring people—passionate young founders, persistent innovators, dedicated support organizations, and investors who believe in Vietnam’s potential. They are the reason NEXA15 exists: so this community can keep learning together, sharing with one another, and continuing to grow.

Our 10-year anniversary is not the end of a journey, but the beginning of a new decade—where we look ahead to building a global learning community:

• where Vietnamese students can learn and connect with international founders,
• where mentors can share knowledge across borders,
• where every small idea can become a seed for big change.

We call this “From community—spreading into sustainable creative value.”

Looking Toward the Next Decade – Keep Learning, Keep Creating

In today’s turbulent world, entrepreneurship is not just for the young. It is the spirit of trying, relearning, and reinventing—at any age, in any field. As KisStartup enters its second decade, we hold this belief:

“The entrepreneur of the future is not the one who knows the most, but the one who learns the fastest.”

With NEXA15, we hope to offer that opportunity—to learn lean, experiment small, and generate meaningful impact.

For the past ten years, we have learned through failures.
For the next ten, we hope to learn with you—through action.

KisStartup – 10 Years of Learning, Experimenting, and Spreading Innovation.
From community – to sustainable creative value.

© Copyright belongs to KisStartup. Any form of reproduction, quotation, or reuse must credit KisStartup as the source.

Nguyễn Đặng Tuấn Minh

There is a simple truth that becomes clearer to us every year: the entrepreneurial path is not paved with roses; it is built on sharp questions, meaningful data, and many deliberate small stumbles (“cú vấp nhỏ”). When we first began working with Vietnamese entrepreneurs ten years ago, we, too, carried the common romanticism of innovation: that a good idea would naturally find its customers; that persistence was enough. Reality taught us the opposite: without data, there is no learning; without learning, persistence only deepens the mistake.

Looking back, what matters most over the decade is not the number of programs, workshops, or “successful” projects, but the moments when we paused at the right time, narrowed the experiment, reframed the question, and found our map again through small but meaningful pieces of data. These are what we call “lean failures” (thất bại tinh gọn): failing earlier, smaller, documented, and forward-facing.

Why does data—especially qualitative data (dữ liệu định tính)—matter so much?

In the startup world, people talk endlessly about numbers: installs, conversion rates, recurring revenue. They are essential—but they answer what, not why. When a curve doesn’t go the way teams expect, most increase budget or switch channels. Few sit down with real users, ask slowly, listen without interrupting, and rewrite assumptions using everyday language.

We learned that proper qualitative data is not a collection of impressions—it is discipline. Discipline in asking non-leading questions. Discipline in verbatim note-taking, separating “opinions” from “observed behavior.” Discipline in leaving the office often enough to hear the difference between someone who says “I like it” and someone who has actually paid.

Many major pivots came not from dashboards but from direct conversations: comforting a busy mother describing her 12-minute dinner routine; sitting with a building manager to hear invisible inconveniences; calling a churned customer to understand why they left. These fragments are rarely beautiful, but truthful—and when enough of them accumulate, they guide the numbers.

Good questions—and the power of a new one

Not every failure is worth learning from. Only failures grounded in a clear question leave traces of progress. After hundreds of interviews, we gradually abandoned “beautiful but useless” questions like “Do you like this idea?” Instead, questions anchored in past behavior always revealed the truth:

• “When was the last time you faced this problem? What happened?”
• “How did you solve it? How long did it take? What was the real cost?”
• “Why did you choose that approach? Who did you consult?”
• “What’s the best and worst part of your current solution?”
• “If there were a ‘good enough’ temporary fix tomorrow, what must it do first?”

These avoid prediction (often full of illusions) and focus on behavior already paid for. Every detail—a cost, a timestamp, an influencer—is actionable. We shift from “listening to comfort” to listening to decide.

Often, a single new question changes everything. “Who actually pays?” once moved a team from an impossible B2C dream to a viable B2B path. Another time, “If we sold only the strongest component, would customers buy?” unlocked an entirely new revenue line. A new question is frequently the true pivot.

A small framework to maintain interview discipline

We keep simple habits:

• Always go in pairs: one asks, one records. Attention is respect.
• Record verbatim: separate customer words, our interpretation, and new assumptions.
• Avoid interviewing friends—politeness corrupts data.
• Avoid closed questions or future hypotheticals unless tied to immediate commitment (deposit, sign-up, payment info).
• Prefer in-person interviews to surveys. Surveys are convenient but shallow; one hour face-to-face can save months of drifting.

These small practices give qualitative data enough reliability to guide decisions. When data is reliable, failures stay small.

Applying Steve Blank’s Four Steps with local pragmatism

We value Steve Blank’s Customer Development Model not as doctrine but as rhythm:

1. Customer Discovery – interviewing for problems & existing solutions; rewriting assumptions using customer language; building “meaningful—valuable—practical” tests.
2. Customer Validation – collecting behavioral evidence (deposits, paid trials, repeat use) using innovation accounting instead of vanity metrics.
3. Customer Creation – growing moderately by scaling proven behaviors, not by spreading thin out of fear of missing out.
4. Company Building – turning lessons into processes, data into reusable knowledge, adaptability into weekly habit.

In Vietnam, step 1 and 2 often merge: teams probe problems while rushing to sell. This is acceptable only if learning and selling remain clearly separated. When the boundary blurs, “beautiful data” drifts you away from reality.

Naming failures precisely

We learned to name failures correctly. “MVP failed” is not enough. We say:

• “wrong payer,”
• “misjudged problem priority,”
• “measured vanity metrics,”
• “messaged without immediate value,”
• “chose a conservative market for a solution requiring education,”
• “overbuilt features unrelated to target behavior.”

Once a failure is named precisely, the next experiment becomes obvious.

Some of the most valuable lessons come from closing projects using data—painful but peaceful. One urban farming team did so after confirming their competitive advantage was not defensible, the market small, and founder time non-scalable. They stopped early to move fast elsewhere. That failure saved them.

Lean + Data: the discipline of “just enough” 

In the AI era, data seems cheap and abundant. The temptation is to collect everything. We choose just enough: only what will be used; only what leads to decisions. Every metric ties to an assumption and a branching threshold. Qualitative data becomes our compass: weekly “learning hours” dedicated to rereading customer voices—not to tell inspiring stories, but to change our questions. When questions change, priorities change; when priorities change, products change.

A practical Customer Discovery script (Vietnamese context)

Start with their problem and current solution—not your idea:

• “When did you last face this issue? How severe was it?”
• “How did you handle it? Total cost in time/money/emotion?”
• “Why that option? Who influenced your choice?”
• “Best and worst parts of your solution?”
• “If a ‘good-enough patch’ arrives tomorrow, what must it do first?”

End with a small commitment (deposit, payment info, agreeing to next week’s pilot). If they refuse to pay a tiny cost today, don’t lull yourself with “maybe later.”

Experiment design: failing small, learning big

Each experiment should have:

• one question,
• one signal,
• one decision.

Example: “If we insert service add-on A into one store for two weeks, will 7-day return rate increase ≥20%?” If yes, expand; if no, stop; if slight increase with same complaints, adjust one element and retry.

In multi-stakeholder markets, don’t chase the vague “first customer.” Find the first replicable cluster—a group with similar context, decision-makers, and reasons to pay. You don’t need the whole industry, just one consistent island to build a bridge.

A small promise for those exhausted

When teams are tired, they blame: “the market,” “the team,” “bad luck.” We’re allowed to be tired. But before blaming, ask whether the question was right. Many times, exhaustion comes from carrying the wrong question for too long. Change the question; energy returns.

Failure does not diminish you; silence about failure does. Tell the story with data, better questions, and humility to change beliefs when evidence speaks—that is how we continue.

After ten years, we have learned one thing: love for customers must be greater than love for the product. That love appears not in slogans but in how we listen, record, pause, pivot, and begin again with a better question.

Lean Startup does not glorify failure; it turns failure into building material for knowledge. Every piece of data—number or word—placed correctly, becomes a brick on the path. The path need not be straight; it just needs to move forward. And to move forward, we must learn.

© KisStartup. Any reproduction, citation, or reuse requires clear attribution.

Nguyễn Đặng Tuấn Minh

If Lean Startup is “the art of learning fast in uncertainty,” then AI is “the turbo engine” for that art. When the two meet, we get Lean 4.0: the Build–Measure–Learn loop accelerates exponentially, decisions rest on richer data, and core assumptions are challenged in real time. Yet this is exactly where questions of ethics, responsibility, and integrity rise: What are we learning fast for? Using whose data? With what impact on people and the environment?

This article takes a pragmatic view: AI does not replace Lean—AI makes Lean more serious. Your ability to learn from failure only improves when you turn AI into a critical ally, place it in the right steps of the learning loop, and keep data ethics as part of your innovation accounting.

Lean 4.0: From a Product Loop to a Cognitive Loop

In classical Lean, we build an MVP, “touch” the market, measure responses, and learn what’s right. In Lean 4.0, AI intervenes in all three stages:

• During Build, AI helps sketch solutions so quickly that an idea in the morning can become a functional demo by the end of the day. Copy-paste a landing page, auto-generate product descriptions, create virtual support agents—this is how a two-person team can perform the workload of a 6–8 person team.
• During Measure, AI “reads” data instead of forcing you to stare at it: it auto-classifies feedback, detects emerging themes, suggests customer segments with distinct behaviors, and alerts anomalies in funnels. Measurement is no longer manual digging; it becomes a translation from raw behavior to strategic questions.
• During Learn, AI acts as your internal challenger—posing counter-questions, simulating “what-if” scenarios, modeling the impact of changes in messaging, pricing, or channels. In other words, AI lets you rehearse failure on the table before failing in the market.

This does not make humans redundant. In fact, as manual tasks become cheaper, the quality of the team’s questions becomes the real competitive edge.

“Lean Failure” + AI: Learning More from the Same Misstep

Looking back at case studies from years of analyzing lean failures—Cyhome (multi-layered B2B, shifting markets), NemZone (pivoting from restaurants to households), or the vertical farming tower project (shutting down based on evidence)—they share one pattern: seeking behavioral truth faster than the founders’ ego. With AI, each journey could have been shorter:

• For Cyhome, instead of “walking the market” for months, AI could map stakeholders—residents’ forums, building management groups, service providers—and extract key pain points from natural-language data. The result: a positional MVP with differentiated messages and value propositions for residents, managers, and vendors—raising the chance of product-market fit on day one.
• For NemZone, AI could “read” comments, inbox messages, and orders to detect early household signals: phrases like “for my kid,” “breakfast,” “12-minute bake.” Instead of debating “healthy messaging,” the team could pivot toward convenience–speed–ready-to-eat before burning cash on new outlets.
• For the farming tower, AI-assisted patent search and novelty matching could have shown early the lack of technical defensibility. Pain arrives earlier—but cheaper: a project closed by evidence, not faith.

All of these are “lean failures”: detecting divergence early, closing learning loops quickly, and adjusting direction using meaningful data. AI simply sharpens and accelerates this rhythm.

AI as a Critical Mentor Inside Your Organization

At the team level, AI can take on three roles:

The Opening Scribe: drafting problem statements, suggesting experiment variants, scaffolding landing pages, preparing “non-leading” interview scripts. What matters is the team’s clarity: Which assumption is riskiest? What signal is strong enough to justify a pivot? What are the ethical limits of the experiment?

The Challenger: generating counterfactuals (“If assumption A were wrong, how would data look?”), running red-team simulations for messaging, forecasting PR risks of scaling fast. Using AI forces teams to write down “win–loss criteria” upfront—this is innovation accounting in discipline.

The Lesson Editor: after each loop, AI summarizes logs, tags assumptions, and links insights across teams. Knowledge no longer dies in personal files; it becomes searchable learning capital, forming the foundation for organizational learning velocity.

The key point: humans define the questions and decision thresholds. AI amplifies.

Ethics, Responsibility, and Integrity: Going Fast Without Losing the Way

Three risk zones must be addressed clearly:

Integrity of information. AI can hallucinate. If you present AI-generated content as fact, you distort your learning loop: you’re measuring user reactions to something nonexistent. The remedy: traceable labels—mark all experimental content as “simulated/ideation,” and only draw conclusions from real behaviors (purchase, usage, repeat).

Privacy and data consent. Lean 4.0 turns operational data into “the new oil,” but without explicit consent, you’re “drilling illegally.” Apply data minimization, anonymization, and provide deletion rights. Learn right—and clean.

Environmental impact. Training/deploying large models consumes energy. “Lean” without resource frugality is contradictory. Startups should favor small–medium models (SaaS/edge), controlled inference, auto-shutdown, and conscious accuracy–cost tradeoffs. Track “energy footprint” as a field in innovation accounting: how much learning is enough, at what cost?

Finding Early Adopters Is Not Enough—How AI Helps You Cross the Chasm

B2B requires early adopters, but staying there stalls growth. AI helps cross this chasm in two ways:

• Hyper-micro segmentation from interaction data to identify “replicable behavior clusters.” Instead of saying “apartment buildings,” say: “300–500 unit buildings, autonomous management boards, 25–40 age households >40%, currently using app A/B.” That is a replicable template—not just “the first customer.”
• Predicting word-of-mouth pathways through relationship graphs: who are the “spread nodes,” what conditions activate them, and what stories they repeat. No more “good luck with referrals”—design referral propagation as a feature.

Still, AI cannot replace trust. In B2B, selling the second and third time is the real proof. AI just helps you get there faster—and cheaper.

Lean 4.0 at Work: A New Learning Rhythm for Enterprises

When implementing AI with a Lean mindset, don’t begin with “Where do we apply AI?” but with “What do we need to learn in the next 30 days?” From the question comes the tool; from the tool comes the rhythm:

• Monday Learning: AI synthesizes customer signals inside and outside the company; the team reads for 15 minutes and picks one assumption to test.
• Thursday Testing: a micro-MVP goes live (message, pricing, channel variants); AI measures in real time with clean logs.
• Friday Reflection: AI prepares summaries; the team chooses whether to continue, adjust, or stop. Learning leads to action.

Repeat for 4–6 cycles and you’ll see AI’s real impact: not a “magical revenue curve,” but a steep learning curve. And that curve pulls revenue upward—on time and with less waste.

Mini-Playbook: A Meaningful AI-Driven MVP (Few Bullets, More Discipline)

An AI-enabled MVP “goes live” only when these three questions are clear:

1. Meaningful – What assumption are you testing that, if wrong, collapses your plan? What signal is enough to conclude?
2. Valuable – What real value does the user receive during the test (time saved, convenience, emotional benefit)? No value, no real data.
3. Practical – Can you deploy and measure it within ≤2 weeks? If not, shrink it until you can—while keeping the core question intact.

Add three ethical “locks”:

• Transparency: Label all AI-generated content; no staged or fake testimonials.
• Consent: Explain what data is used for, how long it’s stored, who accesses it, and allow withdrawal.
• Energy footprint: Track training/inference costs; choose lighter solutions before heavy ones.

When the three questions and three “locks” are addressed, you have an MVP–AI that is meaningful, valuable, practical—and ethically clean.

Lean 4.0: Move Fast, Learn Deep, Stay Honest

Lean 4.0 is not “Lean plus a chatbot.” It is disciplined learning amplified: sharper questions, smaller but more frequent experiments, denser yet cleaner feedback. AI helps us fail earlier—and smarter: instead of spending months on a vague assumption, we focus on a few big questions and use AI to examine every angle before stepping into the market.

But because we move faster, we must be more honest—with data, with customers, with our ethical boundaries, and with the environmental footprint of what we build. Lean teaches us to reduce waste; in the AI era, the biggest waste is not money—it is trust.

“AI won’t make you fail less. AI makes each failure more worthwhile.”
— KisStartup, Lean 4.0 – Learning Fast in Uncertainty, Learning Clean in the Age of Machine Learning

© Copyright belongs to KisStartup. Any reproduction, citation, or reuse must clearly credit KisStartup.

 

 

(Trend, Technology, and Growth Model Analysis 2024–2025)
Compiled, analyzed, and edited by KisStartup

In the world of alternative proteins, Mosa Meat isn’t the loudest name — but it’s one of the few that has mastered turning science into product, product into regulatory standard, and standard into market. Its journey — from the world’s first cultured burger in 2013 to submitting a Novel Foods dossier to the European Commission for cultivated beef fat on January 22, 2025 — sketches a rare deeptech growth curve: slow and steady at the foundation, then accelerating precisely when society is ready to shift dietary habits.

However, to see Mosa Meat merely as a “lab burger company” misses the essence: this is an organization that knows how to commercialize science within existing legal frameworks, using regulation as a runway for its first product, and building bridges of trust toward mainstream adoption. From the historic 2013 tasting to the EU submission for cultivated beef fat in 2025, Mosa Meat connects three deeptech fragments that rarely align: science → regulation → market.

Ingredient-first: The fastest route to the dining table

Rather than launching an ambitious 100% cultivated steak, Mosa Meat adopts an ingredient-first strategy, starting with cultivated beef fat. This ingredient enhances hybrids (e.g., burgers, meatballs, bolognese), providing authentic aroma, taste, and mouthfeel at a lower regulatory threshold. By submitting fat as the first Novel Food, Mosa Meat accelerates market entry within approved frameworks. An EFSA-reviewed fat dossier also serves as a “social proof” for retailers and regulators across other regions — effectively shortening the commercialization path.

Regulation as a growth lever — not a barrier

Few startups see regulation as a growth channel. Mosa Meat does. Beyond the EU Novel Foods filing, it became the first cultivated meat company certified as a B-Corp (September 2023) — a strong ESG signal in Europe’s retail landscape. Mosa Meat also expands through multi-region regulatory pathways (EU, Switzerland, UK), leveraging “sandbox” tasting programs to collect data and trust, while cutting down isolated trial time and market education costs.

Market education through experience, not just messaging

Since the 2013 London tasting — designed as a “press conference for the palate” — Mosa Meat has consistently promoted the “see–understand–taste” model: public R&D updates, transparent process explanations, and controlled tastings with pioneering chefs. As consumer behavior shifts (2024–2025) toward short videos, on-site experience, and transparency, this model outperforms traditional advertising. The €1.5 million crowdfunding in February 2025 (raised in 24 minutes) turned supporters into early adopters and brand storytellers — converting social consensus into capital.

Scaling is not just more bioreactors — it’s unit economics

Mosa Meat’s 7,340 m² Maastricht campus, with 1,000 L bioreactors, establishes a full R&D → pilot → pre-commercial chain. But true scalability lies in cost curves and productivity, not facility size: serum-free media, higher cell productivity (g/L/h), and stable differentiation cycles. Costs have dropped ~80× since the $330,000 burger, approaching commercial viability. The company distinguishes between “image scaling” and “economic scaling”, prioritizing the latter.

Environmental impact: Potentially strong, but conditional

LCA studies show major emission, land, and water reductions if cultivated meat reaches scale and uses renewable energy. Benefits fade under fossil-heavy grids. The real sustainability lies in system design: clean energy, circular media, and waste management. Reduced livestock dependency also restores soil and biodiversity, but cultivation processes must manage chemical and material footprints carefully.

Social impact: Ethics, jobs, and culture

Cultivated meat eliminates large-scale slaughter and creates skilled biotech jobs but also requires just transitions for traditional workers. Mosa Meat’s ingredient-first, hybrid approach respects food culture — upgrading familiar dishes rather than replacing them entirely. Still, concentrated IP ownership (cell lines, media, bioreactors) risks forming “Big

Food 2.0”; open standards and fair licensing are essential for inclusive industry growth.

Transparency on long-term safety

Though EU/UK/Singapore frameworks are rigorous, long-term consumption data remain limited. Mosa Meat’s approach — proactive transparency, public LCA assumptions, and post-market monitoring — builds credibility in an evolving landscape.

Takeaways for Vietnamese Biotech Startups

• Start ingredient-first. Focus on components (cultivated fats, fermented flavor molecules) to enhance familiar dishes like phở, bún bò, or bánh mì rather than full meat replacements.
• Design legal runways early. Align datasets with EFSA/FDA standards even for local pilots; propose regulated tasting sandboxes with traceable QR data.
• Educate via controlled experiences. Combine chef-led tastings, short videos, and transparent safety sheets to let consumers see–understand–taste.
• Report unit economics quarterly. Share media cost/kg, productivity, and batch cycles to build trust with investors and regulators.
• Blend diverse capital sources. Mix climate VC, public funds, and community crowdfunding with measurable KPIs (LCA, safety, cost).
• Localize the supply chain. Develop domestic inputs for media and bioreactors to reduce cost and import dependency.

Sources:
EU Submission (22/01/2025): Mosa Meat filed Novel Foods dossier for cultivated beef fat (Mosa Meat).
B-Corp (09/2023): First cultivated meat company to achieve certification (Forbes).
Scale: 7,340 m² campus, 1,000 L bioreactor (Mosa Meat).
Market education: From London 2013 tasting to transparent R&D communications (eitfood.eu).
Crowdfunding (02/2025): €1.5M in 24 minutes; social consensus turned into market traction (Cultivated X).