Diagnostic and business case
The hardest question is not which model, it is which process is worth automating first.
We map the candidates, do the maths and rank them by payback before any code is written.
Free One hourWe adopt AI step by step: take one process, carry it to launch and measure the result. Then the next process, until the routine work stops eating the week.
Book a free diagnosticThe hardest question is not which model, it is which process is worth automating first.
We map the candidates, do the maths and rank them by payback before any code is written.
Free One hourSlide decks do not tell you whether a model will cope with your actual documents.
We build a working prototype on a slice of your data, usually within a week.
Proof One weekA model on its own changes nothing until it reaches the systems people work in.
We build the solution and wire it into your CRM, ERP, storage and internal tools.
Integration APITools nobody was taught to use quietly go unused after the first month.
We run a pilot group, tune on their feedback and train the rest of the team.
Adoption TrainingAI is rarely rolled out across a whole company at once. It usually starts in one department, where the effect is most obvious and easiest to measure.
The first line answers the same questions over and over.
The repetitive volume goes to a bot, the people keep the cases that need judgement.
Support SalesDocuments are assembled and checked by hand, slowly and unevenly.
Drafting, extraction and checks are automated, with a human approving the result.
Documents FinanceData lives in five systems and nobody has the whole picture in time.
Agents collect, reconcile and report on a schedule, flagging what changed.
Analytics ReportingCompliance is checked by sampling, so most of what happens is never reviewed.
Computer vision reviews the whole feed and raises alerts as events happen.
Manufacturing QualityGartner
Harvard Business Review
Yakov & Partners
The list is open: we connect anything with an API. For services that need a richer contract we build connectors on the Model Context Protocol, the standard way for a model to reach outside systems.
What we work with
We keep it lawful and sensible: data is processed to your compliance requirements, and the model and the hardware are chosen for the task, without overpaying for tokens or idle servers.
Documents and conversations never leave your perimeter. That keeps the solution compatible with GDPR, HIPAA and internal security policies.
We deploy on your servers or in your cloud, whichever your data policy requires. We size the hardware for the load or fit into what you already run.
Cloud LLMs or open-source models inside your perimeter. We test candidates on your own scenarios during the first stage and pick by result, not by hype.
One hour on your processes, free of charge, ending with a ranked list of candidates.
A working proof on a slice of your data, so the decision rests on evidence.
Development, integration with your systems and validation against the agreed metric.
A pilot group, tuning on real usage, training, then the next process.

AI Chatbot for Employee Support

Multi-agent web search

AI video processing
With one process that has a measurable cost and an owner who wants it fixed. Company-wide programmes stall; a single process that pays back in a quarter earns the budget for the next one. Choosing that first process is what the free diagnostic is for.
We agree the metric before the work starts: response time, share of tickets resolved without a human, hours spent on a document, number of errors caught. Then we measure the same metric after launch. If it did not move, that is a finding, not something to explain away.
Not to start. We own the model, the integration and the launch. If you want the capability in-house afterwards, we hand over the code, the documentation and the operating procedure, and train whoever will run it.
It depends on the process, the number of integrations and where the model runs, so we quote after the diagnostic rather than from a price list. What we can say upfront is the shape of the estimate and what would make it move in either direction.
Yes, though it usually costs more: a self-hosted model needs a GPU server, rented or bought. So while the task involves no sensitive data, most teams start on cloud LLMs. When the data is sensitive, we deploy an open-source model inside your perimeter: the model, the knowledge base and the logs never leave the company, which is what GDPR and internal security reviews ask for. We do it end to end, including the servers, the deployment and the access rules.
Business processes change and the solution grows with them: we refresh the knowledge base as your policies and products change, extend the scenarios to neighbouring tasks, and move to stronger models as they ship. You can run this yourself or hand it to us as support.

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