What problems do we solve?

Handing the questions your support team answers dozens of times a day to an assistant grounded in your own documents. Reading incoming invoices, contracts and order emails automatically instead of typing them into a system. Camera-based quality control on a production line. That's the kind of AI work we build — for measurable business outcomes, not for the spotlight.

Let's be honest: not every workflow is a fit for AI. Adding it to processes with zero error tolerance, or to processes that already work well, usually adds cost. If our first conversation ends with "you don't need AI here," we'll say exactly that.

What we build

• Assistants grounded in your data — answers come from your documents and data (RAG), not from the model's memory; every answer cites its source.
• Document processing automation — invoices, contracts, forms: reading, classification, summarization and hand-off to your systems.
• AI inside your existing software — smart search, recommendations, drafting — added to the product you already run.
• Computer vision — counting, detection, quality control in production and retail settings.

We're neutral on models: the architecture avoids locking you into one vendor, the use case picks the model, and cost is calculated up front.

How we work

1. Discovery (1 week) — use case, data inventory, success metric. This is where "how accurate does it need to be to be useful?" gets answered.
2. Pilot (2-6 weeks) — a narrow proof running on your real data, measured against an evaluation set: accuracy, hallucination rate, cost per request. If the numbers miss the target, we recommend stopping — that's what pilots are for.
3. Production (2-4 months) — production architecture, access control, monitoring and cost guards; integration with your systems.
4. Improvement — regular measurement and tuning rounds on real usage data.

Frequently asked questions

Does the model "learn" our data? Can it leak?

No. With RAG your data never enters model training; it's passed as context at query time, subject to access permissions. Enterprise APIs don't train on your data — and where needed we pin down data processing terms contractually and design the KVKK/GDPR side together.

How do you control hallucinations?

Three layers: answers generated only from your data (RAG), source citations on every answer, and measurement against an evaluation set before anything ships. An assistant that says "I don't know" where it should is better than a confident-sounding one that's wrong.

How does cost work?

Two items: our development effort (written proposal after discovery) and usage-based model cost (per request, billed monthly). We measure the second with real numbers during the pilot and reduce it in production with caching and model tiering. Monthly model cost is a design input, not a surprise.

Our data is a mess — can we still do this?

That's the starting point of most projects. The pilot phase surfaces the data cleanup you actually need; if it's substantial, we build that first (see Data & Analytics). Be skeptical of any proposal promising big AI results before the data is ready — including ours.

Tell us which process you want to accelerate; we'll design a fitting pilot.