RegenX · AgriTech · AI-native product development · 0 → 1 → scale
Rebuilding a production platform in three weeks, without adding headcount or vendors.
RegenX needed to replace its first vendor-built product with a more sophisticated platform spanning web and mobile. I redesigned the software development model around AI, embedding agronomists directly into the build loop and using real customer data to test and iterate daily. The new platform was built and released in under three weeks, using the existing team and less than $1,000 in AI model spend.
< 3 weeks
From rebuild to production release
3 products
Web platform + two mobile applications
< $1K
AI model / token spend
0
Additional hires or external vendors
The situation
A venture-backed agritech company entering its next stage of product development.
RegenX was building AI-enabled products for agricultural supply chains in Vietnam and Singapore.
Following a roughly $500,000 fundraise, the company had moved from experimentation toward commercial deployment. Our first mobile application had been built quickly with an external software vendor: approximately three months of development and $30,000 in external cost were enough to get a working product into the market.
That was appropriate for the first version.
But as the business grew, so did the product requirements. We now needed a broader platform spanning web and mobile, deeper agronomic capabilities and much faster iteration with commercial partners.
At the same time, rapidly improving AI capabilities were changing the economics of software development.
Rather than scale the original vendor model, we decided to redesign how RegenX built software.
The challenge
A more sophisticated product, with a fraction of the time and resources.
The original application had proved that we could ship a product and get it into users' hands.
The next stage was harder. We needed to expand the product substantially while remaining a small, capital-efficient startup.
01
The vendor model would not scale.
The first application took roughly three months and $30,000 to build externally.
Repeating that model every time the product evolved would make iteration slow and increasingly expensive precisely when the company needed to learn faster.
02
Agronomic correctness could not be outsourced.
The product was not simply a workflow application.
Its recommendations affected real agricultural decisions, which meant software implementation had to stay closely connected to agronomic expertise.
A technically correct feature could still produce the wrong outcome for a farmer.
03
Three product surfaces had to evolve together.
The new platform included a web application and two mobile applications serving different parts of the operating model.
Changes could not be designed independently. Data, workflows and agronomic logic had to remain coherent across the entire system.
04
The commercial timetable was measured in weeks.
Commercial partners were preparing to use the platform.
We did not have another conventional three- or six-month development cycle available to us. The product needed to reach production quickly without compromising the logic behind the recommendations it generated.
The solution
An AI-native software development model built around domain expertise.
The breakthrough was not simply using AI to write code faster.
We changed the development model itself.
Instead of separating product requirements, agricultural expertise and software implementation into sequential hand-offs, we brought them into the same daily operating loop.
01. AI-native from the beginning
Rebuilding the SDLC around AI rather than adding AI to the old process.
We adopted an AI-first software development workflow for the rebuild.
AI compressed much of the implementation cycle, allowing a small internal team to move from product decisions to working software far more quickly than the previous vendor-led model.
The objective was not to remove human judgment.
It was to automate and accelerate the parts of software development that could increasingly be handled by machines, while keeping architecture, product decisions and business logic under human control.
02. Domain experts inside the build team
Agronomists and AI engineers built the product together.
Agronomic expertise could not sit upstream in a requirements document.
We created an integrated working model in which agronomists and the people building the software worked directly together.
Recommendations produced by the system could therefore be evaluated not only for whether the software behaved as specified, but for whether the advice made sense in the field.
This dramatically shortened the distance between domain expertise and implementation.
03. Forward-deployed development
Real customer data became part of the development cycle.
Rather than waiting for periodic releases before learning whether the system worked in practice, we worked much closer to the forward-deployed model.
Customer data was used to simulate real outputs throughout development.
The team could inspect what the system would actually recommend, identify incorrect or weak behaviour, make changes and demonstrate the new version again the following day.
Development became a continuous loop between real-world inputs, domain judgment and working software.
04. Daily iteration over long development cycles
Working software replaced long specification documents.
The team worked in short build-demonstrate-correct cycles.
Instead of defining large amounts of functionality upfront and waiting weeks for implementation, we continuously turned the highest-priority operating needs into working software.
That made it possible to change direction quickly when the data or agronomists showed that an assumption was wrong, without turning every change into a new vendor project.
The result was not only faster coding. It was a much faster learning cycle.
The business outcome
From a three-month vendor cycle to a production release in under three weeks.
The first vendor-built application had required approximately three months and $30,000 of external development spend.
The replacement platform was broader — spanning web and two mobile applications — but was built with the existing team and released in under three weeks.
Under three weeks to production
The new platform went from rebuild to commercial release in less than three weeks, allowing RegenX to meet the deployment timetable agreed with its partners.
No additional team or vendor
The company did not need to hire a conventional software team or commission another external development project.
The platform was delivered using capabilities already inside the business.
Less than $1,000 in AI model spend
Direct AI model and token expenditure for the build remained below $1,000.
That changed the economics of what a small venture-backed company could reasonably build and iterate internally.
A product model that could keep evolving
More importantly, RegenX no longer depended on a months-long external development cycle every time the operating model changed.
The company had created an internal capability for continuously translating domain knowledge and customer feedback into software.
The build system
AI compressed the build cycle. Domain experts remained responsible for correctness.
The system worked because implementation, domain review and customer feedback happened inside one continuous loop rather than as separate project phases.
The real advantage was not cheaper code. It was a shorter distance between an operating problem and working software.
By combining domain experts, real customer data and AI-native engineering in the same build loop, RegenX could continuously turn field knowledge into product changes without rebuilding a traditional software organization around the process.