ABOUT US
Flaflow builds custom workflow software for B2B verticals in days rather than months. We can do that because we spent our time building the primitives underneath first — and a language of our own to express them in.
Every project leaves behind reusable components. Enough of them in one vertical, and we stop doing projects and launch a platform.
THE TREND
Anyone who has used an LLM to code can see it. The hard, expensive part of shipping software — writing it — collapsed.
That breaks the traditional SaaS model. Building a product in one vertical and charging large margins only worked while the barrier to entry was high. It isn't anymore.
BEFORE
Engineering is the fixed cost. You pick one vertical big enough to pay for it, and defend the margin.
AFTER
Engineering is cheap. You serve many verticals at once, each with software built specifically for it.
WHAT WE BUILT
Vertical SaaS is remarkably standard in its building blocks. Call APIs to move data. Parse the output and apply business logic. Store and retrieve it. Present it well. Handle accounts, payments, and login. We built primitives for all five.
Retrieve and submit data. Whether you are in health insurance or manufacturing, you still need timeouts, retries, and failure handling — so we solved it once.
Standard functions to parse API output with LLMs, including documents and images, then run it through the business rules of the vertical.
Database operations with schemas, migrations, and access control already handled.
Frontends stopped being the bottleneck. Frontier models one-shot a working interface that we then refine against the customer process.
Accounts, login, permissions, payments, subscriptions. Every app needs them and none should be rebuilt per project.
Together, these let us deploy a new workflow SaaS in days instead of months.
WHAT MAKES US DIFFERENT
This is the decision everything else follows from. Our end users are domain experts, not engineers — the person who knows how wine fraud actually works, or how a staffing desk actually runs. They cannot debug a stack trace, and they should never have to.
A general-purpose language like Python lets you express anything, including a great deal that is subtly wrong. That flexibility is the right trade for engineers and the wrong one for everybody else. So we built a language that deliberately gives up flexibility to buy correctness.
Strongly typed with well-defined input and output schemas. If two steps don't fit together, that is caught when the workflow is written — not in production, in front of a customer who has no idea what went wrong.
A constrained language has a far smaller space of valid programs, and validity is machine-checkable. Models generate against it far more reliably than against general-purpose code, which is a large part of why we move as fast as we do.
Workflows break into modular steps that are semantically interpretable out of the box. Unlike vibe-coded apps, where logic is tangled and hard to repurpose, ours is legible as a description of the process it automates.
THE OPPORTUNITY
AI programming unlocks the ability to build profitably where nobody could before.
Each with a market cap of roughly $100M to $1B. Real businesses with real problems and no software, because no venture-scale winner could ever come out of them.
BEFORE
The fixed cost of hiring engineers is high, so you pick one vertical that can make a lot of money at large margins.
AFTER
A fully customized SaaS app takes days and little engineering effort, so tackling many verticals at once is very profitable.
IN PRACTICE
We work with a wine company with 2,000+ employees doing ~$500M/year that does photo verification to prevent fraud and employs more than 20 full-time staff to review images manually. The problem is common across wine and other traditional industries, but the market is only a few billion dollars — too small for a VC-scale winner, so there is no current solution.
IN PRACTICE
We have an LOI with a $6B/year wine distributor to add AI automations to their internal workflows.
Even in VC-backable verticals, most teams run on software built for someone slightly different. We win on price and specificity at the same time.
BEFORE
Raise tens of millions, produce one generic product, sell it to hundreds of different customers.
AFTER
Pick many specific niches inside the vertical and make specific products for each.
IN PRACTICE
A former recruiting agency owner shifting into SaaS. The CRMs they have been using are designed for in-house talent teams and don't fit — they want a CRM built for staffing agencies placing non-tech roles in middle America.
IN PRACTICE
A publicly traded marketing agency expanding what they sell — we build the automations behind new services they offer their own clients. Alongside a pest control expert replacing a 10+ year old door-to-door app, and a senior home care company with 200+ branches nationwide.
GO TO MARKET
The same three moves in every vertical we enter. Each stage pays for the next one, and the primitives left behind are what make the next stage possible.
We find operators who know a vertical cold and partner with them. They own sales and domain expertise — they already have the relationships and the credibility. We own the technology. Neither side is doing the thing it is bad at.
Every engagement in that vertical leaves behind reusable, strongly typed components. Once we have enough of them, we are not doing custom projects anymore — we have the substrate for a platform in that vertical, and we launch it.
People inside the vertical build their own workflows on top of what we made. Our language is designed for exactly this: non-technical domain experts composing correct software without us in the loop.
We are at stage one today, deliberately. Eventually users will edit and modify these workflows with our internal tools — but right now the fastest way to market is for us to use our own tools, working directly with design partners one vertical at a time.
LONG TERM DEFENSIBILITY
Because our language forces components to be typed, modular, and semantically interpretable, everything we ship is reusable by construction rather than by discipline. As we build more software we recycle components across verticals, and that becomes the moat.
We are already seeing it. Some of the early workflows we made for recruiting customers are now running for our marketing agency customer.
We collect how experts in different fields actually complete their tasks, and cross-apply it. Just as there is a moat in the data used to pretrain LLMs, there is a moat in understanding B2B workflows well enough to move them between industries. Our system is designed to capture that as we build.
At some point we will create software faster and more accurately than anyone starting fresh, because the library underneath is already most of the answer.