First a read of the process, then the stack and the build
We get inside your process from the source records: CRM, documents, real deals. We build a prototype on your own data and say honestly what automates and what doesn't, before you commit to a full build. The stack follows the facts, not the fashion.
Not "turnkey AI", but a read, then a decision.
"Implement AI for us" turns into an invoice for a finished solution. And whether it works or not, do you find out only once the money is spent?
Any owner of a process business knows this fork. Half the market answers it with "turnkey implementation" and a tool chosen in advance.
We go in differently. First we study the process and reconstruct how it actually runs, usually more precisely than the client describes it themselves. Then we decide which stack closes it: where a ready service is enough, where custom development is needed, and where AI is beside the point and plain automation will do. Only the risky part gets a prototype before the bill. We get inside the process and own the result, including the right to say "don't build it".
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01
A read of the process — a paid stage
We get into your system: CRM, documents, videos, real deals from application to result. We reconstruct the process from the source records and find where time leaks and where a mistake costs dearly. This is engineering work, not a free pre-sale, so the read is paid. The first short call, to see whether we fit each other, is free.
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The stack follows the facts, not the fashion
Our stack is broad: WordPress, Laravel, Python, Node.js, ClickHouse, queue-based parsers, AI and MCP, infrastructure under load. We aren't tied to one tool, so the solution is chosen after the read. Somewhere it's a ready LLM, somewhere a custom panel, somewhere a queue and a replicator, and somewhere an index in the database instead of a new server. The tool fits the task, not the other way round.
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03
A prototype on your own data
Anything whose viability isn't proven gets a prototype on your own source records, before the big spend. You handle a working thing, not a slide deck. With AI this matters most: you can't say in advance what will fly until you've built it and run it on real documents.
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An honest status and the economics
We show where AI lifts the drudgery off and where it starts to get things confidently wrong and has to be kept under manual review. We help you work out whether the idea is worth its money. If it isn't, we say so first. Talking you out of a wasted spend is our job too.
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Delivery in blocks, by priority
We build in stages: the parts that pay off sooner come first. The build is estimated in fixed hours per spec; the standing part runs on a retainer. Whoever wrote a module keeps it for years — context isn't re-learned at every request.
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Productisation: the next project is cheaper
What repeats moves into a reusable core — registry parsing, a document builder, recognition. One-off work for a single client becomes a ready piece for the next. So implementation stops being endless bespoke labour.
- Stack WordPress · Laravel + Livewire · Python · Node.js · ClickHouse / PostgreSQL / MySQL · Redis · Horizon · Docker · AI / MCP · parsers and ETL · infrastructure and DevOps
- Team A lead engineer holds the read, the architecture and the negotiations; the area specialist runs the build and stays on it. Each area kept by one person over the years.
- Model The first call is free. The read of the process is a paid stage. The build is fixed hours per spec; support is a retainer. Anything risky is prototyped before the bill.
- Format Direct work with the owner, async-first communication, NDA on request. The client stays under a code-name if we publish a case.
Proof, not promises.
This is how we already work. The certification vertical is the one we've worked deepest, but the method works on any process business.
An engineer inside the process, not another API.
The model where an engineer sits inside the client, studies the process and takes the system all the way to production was invented by Palantir, and in 2026 it's being copied by OpenAI and Anthropic — in English it's the Forward Deployed Engineer. The idea is simple: the gap between "works in the demo" and "works in production" is closed not by another API but by a person who got inside the process.
For us it isn't a fashion: one certification-analytics track we've run this way for 4.5 years, with production code on the client's data and a response to incidents overnight.
- How is this different from "turnkey AI implementation"? "Turnkey" sells a tool chosen in advance and promises a result blind. We study the process first, then choose the stack, and show the risky part with a prototype before the bill. And we keep the right to say the idea doesn't pay off.
- Why is the read paid but the first call isn't? The first short call is there to see whether we fit each other, and it's free. The read of the process is days of work with your CRM, documents and deals, that is, engineering work. It becomes the foundation of every decision that follows, so it's charged as a separate stage.
- Do you promise AI will work 100%? No, and that's more honest. With AI you can't say in advance what will fly until you've built it on real data. So the first stage comes with a prototype on your own records, not a fixed promise that "it all works out of the box".
- What stack do you use? Any of ours: WordPress, Laravel, Python, Node.js, ClickHouse, queues, AI and MCP, infrastructure. The specific one is chosen after the read, for your task, timeline and budget. Sometimes the best answer isn't AI at all, but plain automation or an index in the database.
- Is this certification-only? No. The certification vertical is the one we've worked deepest, so the cases come from there. But the method — read the process, choose the stack, prototype, honest status — works on any process business: logistics, document flow, accounting.
- Who runs the system after launch, and how is cost calculated? The same team that built it: whoever wrote a module fixes it for years. The build is estimated in fixed hours per spec; the standing work and incident response run on a monthly retainer. The bill doesn't grow quietly.
Have a process that
rests on people?
Show it to us. We'll read it, build a prototype on your own data, and say honestly where AI pays off and where it only lets you down. The first call is free; the read then runs as a separate paid stage.