We work on existing processes, automations, PoCs, and AI systems that are manual, fragile, or hard to scale today. We validate them on real cases, integrate them into business workflows, and move them into controlled daily use.
// SOUND FAMILIAR?
You've invested budget, credibility, and time. The pressure isn't just technical — it's personal. You need a partner who understands what's at stake for you, not just for the project.
You greenlit an AI project that looked promising, but months in, it still has not changed how the business actually operates.
Your vendor delivered a demo that impressed the board, but you know it will not survive real data, operational exceptions, or daily use. And you will be the one accountable.
You are being pushed to 'scale AI' across the organisation, but the first process has not proven operational value yet. You need a concrete result before asking for more budget.
The process only keeps working thanks to manual steps, spreadsheets, human checks, and workarounds between systems that do not talk to each other. Every increase in volume brings more errors, more delays, and more operational cost — and the only apparent solution is hiring again.
// THE DIFFERENCE
We validate, integrate, and move AI systems into real use inside existing business workflows. You pay for our experience, not our time.
If your problem can be solved with traditional software, we'll tell you. We don't complicate the architecture to inflate the budget: we build only what your business actually needs.
We're not tied to a single vendor or model. We adapt to your tech stack and choose the most efficient tool for each challenge, integrating seamlessly into your existing workflows.
Everything we build — code, infrastructure, documentation — is yours from day one. Your operations never depend on us. You can walk away at any point with everything you need to run, maintain, and scale on your own.
Most people stop at the demo. We design and build systems with integrated observability, rigorous testing, and a standardised codebase: solutions that hold up in daily use.
// OUR METHOD
We test processes, automations, and AI systems against real data, edge cases, and operational constraints. If they hold, we move them toward production. If they fail, you know where to stop before investing more budget.
We analyze the process, data, manual steps, integrations, and constraints to understand where it breaks and what is worth testing.
We stress processes, automations, and AI systems with real data, edge cases, technical constraints, and operational scenarios.
We make what passed validation robust, observable, and integrated enough for daily use.
You get code, infrastructure, documentation, runbook, and knowledge transfer so your team can operate the system independently.
// HOW IT WORKS
Every engagement starts by understanding what already exists and why it is not creating operational value. What comes next depends on evidence, not enthusiasm.
We analyze the existing process, automation, or AI system, the data involved, manual steps, integrations, and operational constraints. The goal is to understand where it breaks, what is worth testing, and what should be stopped or simplified.
Deliverables
Process map · Weak points · Technical risks · Verdict: stop / simplify / validate
We test the critical part with real data, edge cases, operational exceptions, and technical constraints. If it holds, we define the production scope. If it fails, you know where to stop before investing more budget.
Deliverables
Focused Crash Test · Measured findings · Go / no-go decision · Production scope
We engineer what passed validation: architecture, data flows, integrations, testing, observability, error handling, and operational controls. The system becomes usable every day inside your business workflows.
Deliverables
Production-ready system · Integrations · Tests · Observability · Operational controls
You get the code, infrastructure, documentation, runbook, and knowledge transfer. Your team knows what is inside, how to control it, and how to maintain it without depending on us.
Deliverables
Code and infrastructure · Documentation · Runbook · Handoff · Full ownership
Want to know the next sensible step?
Describe the situation, the problem, and the constraints. We'll reply with a free initial technical review.
We read every request within 48 hours. The evaluation is reviewed directly by people who can understand the technical and operational problem. If it makes sense to go deeper, we propose a free, no-obligation qualification call.
// SELECTED WORK
Situation
An e-commerce startup had AI services already running, but the platform was too fragile to support larger customers with confidence.
Decision
The key question was not 'can the AI work?' It was whether the product was reliable enough to sell to higher-value clients without creating support risk.
Result
The business could finally sell the product with confidence. Reliability stopped being a sales risk: the team could see what was happening inside the AI flows, fix quality issues faster, and support larger clients. That stability helped unlock about €60k in new project revenue.
Situation
A US fintech startup needed to process tax returns at scale, but the workflow still depended on accountants manually operating legacy desktop software.
Decision
Before funding the full system, we validated whether an agent could reliably operate the legacy software, recover from errors, and scale beyond one machine.
Result
In three months, the workflow moved from manual work to production automation. Accountants no longer had to spend hours moving data through legacy software by hand. The company gained the capacity to process far more returns without hiring at the same rate, freeing an estimated 6,000+ hours per month.
Situation
A cultural association had thousands of digitised pages, but scholars still could not search the archive in a useful way. Previous attempts had stalled.
Decision
The decision was to validate speed, search quality, and internal maintainability before expanding the platform around the archive.
Result
A project that had been stuck for too long became usable in 30 days. Scholars could finally search more than 10,000 pages in under a second, and the association turned a static archive into a practical research tool its team could update independently.
// COMMON QUESTIONS
You do not need to know beforehand. It is one of the things we assess. If the problem is better solved with traditional software, integration, or simple automation, we say so. The goal is not to use AI, but to solve the bottleneck in the most solid way.
An initial technical diagnosis starts at €3,500–4,500. A focused validation starts at €7,500. Industrialization projects start at €25,000 based on the systems involved, integrations, and scope. We work at a fixed price: the cost is defined before we start and does not change.
A diagnosis takes 1–2 weeks. A focused validation takes 2–4 weeks. Production rollout depends on the scope, the systems involved, and data quality. We define timing after seeing the real context, not before.
No. It is often the correct starting point. In many cases the first step is not to build, but to understand where the process breaks and whether it is worth intervening. That is why we start with a focused diagnosis, not a full project.
If validation shows that proceeding does not make sense, that is still a useful result: it avoids investing in the wrong project. Our goal is not to sell development at all costs, but to help you decide whether to build, simplify, reinforce, or stop.
Yes. We're used to explaining technical tradeoffs to founders and business owners. We won't bury you in jargon. If something is complex, we'll show you why, not just tell you.
Not necessarily. We use AI only when it is the right choice. It can mean language models, OCR, computer vision, classification, or semantic search. If traditional software solves the problem better, we choose that.
// GET STARTED
Describe the situation, problem, and constraints. We'll reply with the most sensible next step: stop, simplify, run a Crash Test, or take the system to production.
We read every request within 48 hours. The evaluation is reviewed directly by people who can understand the technical and operational problem. If it makes sense to go deeper, we propose a free, no-obligation qualification call.