Data Intelligence Platform
SaaS · Czech startup · 2-person dev team
The problem
A B2B sales platform runs on the quality of its company and contact data, and the legacy harvesting service producing it was expensive to run, capped on how many records it could handle, and risky to change: nobody could tell whether a prompt change made extraction better or worse until it was live. And turning "this is our ideal customer" into an actual prospect list still meant manual research.
What we did
Rebuilt the contact harvesting service at the core of the product: an LLM extraction pipeline that turns crawled pages into structured company and contact records. Every prompt change runs against a benchmark suite before it ships, so extraction quality is measured, not guessed. Designed, implemented, and operated end to end.
On top of it, built the platform's flagship prospecting flow, the AI customer matcher: the user describes their ideal customer in a short chat, and the system finds matching companies on the open web and streams them into the pipeline. That covered the background engine that runs each search without freezing the product, tracking what each step costs, and the chat, selection, and live progress UI on top.
The result
The rebuilt harvester processes far more records at lower cost than the legacy service it replaced, at 95%+ accuracy across millions of records. The AI customer matcher became the primary way the platform's users find new leads.
An honest note
This build was delivered during an engagement outside the studio, so the client stays anonymous. That work shipped and holds up. It is here because rebuilding the data service under a live product and shipping an AI feature its users rely on is exactly the kind of foundational work we do.
The stack
More work
Have something to build?
We build products like this: fixed price, weekly milestones that pass their agreed checks or you don't pay for that milestone, and a 12-month warranty on bugs after delivery.
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