Build Intelligent AI Agents with Context Engineering

Your AI is only as smart as what it remembers and understands in the moment.

Context engineering is the layer that decides what your AI knows when it answers: who the user is, what they said two turns ago, your business rules, what the situation calls for. A model without it answers every question from a blank slate. With it, the AI works from the full picture instead of guessing.

Retrieval finds documents. It doesn't give your AI memory, judgment, or the ability to act.

RAG pulls relevant text into a prompt, which helps with single questions. It doesn't carry state across a conversation, learn a user's history, or coordinate steps. That's why a system that demos well starts repeating itself and asking for clarification the moment real users push on it. Context engineering is the part RAG leaves out.

An assistant answers. An agent remembers, decides, and gets something done.

An assistant responds to one prompt at a time and forgets the rest. An agent holds context across the whole interaction, draws on history and your systems, and carries a task to completion. The difference shows up in multi-step work, where assistants stall and agents keep going.

Demos are single-turn. Production is messy, multi-turn, and full of edge cases.

A scripted demo never tests what happens on the fifth turn, when the AI has lost track of the conversation, or when two users want different things from the same system. Accuracy drops, answers turn generic, and people stop trusting it. The model usually isn't the problem. The missing context is.

Memory is engineered, not assumed.

We build a managed memory layer that decides what to keep, what to retrieve, and what to forget: conversation state, user preferences, and the history that makes the next answer better than the last. Each session builds on the one before instead of starting from zero.

By default, generic AI vendors want your data in their cloud, training their models. We build the opposite.

We run your AI on infrastructure you control, on-premise where you need it. Your context, your documents, and your customer data stay yours. GDPR-native, NIS2-ready, no leakage into ecosystems you never agreed to. We've worked with patient and transactional data for over a decade, the most regulated data in the EU.

One agent is easy. Getting several to work together without falling over is the hard part.

We design the orchestration so agents share context, hand off cleanly, and pull from the right source at the right time. We integrate with the systems you already run through APIs and real-time pipelines, so the AI works inside your business instead of beside it.

The right stack is the one that fits your problem and that you can still run after we leave.

We decide after Discovery. We work across commercial models like GPT and Gemini, open-source models like Llama and Mistral, frameworks like LangChain and LlamaIndex, and vector databases like Pinecone, Weaviate, and Chroma. When ownership matters, we fine-tune and host models you control. No vendor lock-in.

The most expensive AI mistakes happen before anyone writes a prompt.

For a short, fixed-price phase, we map your use case, your data sources, your integration points, and what the AI actually needs to know to be useful. You leave with a clear architecture, defined success metrics, and a realistic plan, whether or not you continue with us.

We set the targets on day one, in numbers you care about.

Before building, we agree on KPIs tied to your goals: accuracy on multi-turn tasks, time saved, queries resolved without a human, adoption. We measure against them from the first release so you see impact, not a vague promise of intelligence.

You pay when working software ships, not when hours pile up.

After Discovery, you get a phased plan with a fixed cost per milestone. Our invoices say "delivered," not "hours worked." You see exactly what you pay for and exactly what you get at each stage.

Weeks to a working agent, not a six-month implementation.

We deliver in phases, so you get a usable system early and improve it from there instead of waiting half a year for a single launch. The total depends on how many systems you integrate and how complex your context is, and we give you a real range after Discovery.

An AI system that ships and then drifts is a liability.

We monitor performance, watch for accuracy drift as your data and usage change, and keep tuning the context and the models. You get a team that stays with the system, not a handover and a goodbye.

Hiring AI engineers is slow. Generic vendors solve their lock-in, not your problem.

Experienced AI engineers take months to find and align in the DACH region, and a generic platform wants your data in its cloud and your roadmap on its terms. We bring a team that has built context-aware systems before, working on your infrastructure, with your data staying yours.

Five things, and they're the five things most AI vendors can't put on their own website.

You pay on delivery. Milestones, not hours. Discovery comes first, so we solve the real problem, not the one you assumed. The team stays, under 5% turnover on long engagements, so the engineer who designs your system is the one running it. Your data stays yours, on your infrastructure, GDPR-native and NIS2-ready. And AI is our business, so you stay focused on yours.

Thirty minutes. No pitch, no slides.

You tell us where your AI falls short and what you want it to do. You ask us anything. By the end, we both know whether context engineering fits your problem, and if it does, Discovery is the next step.

A real use case and an honest description of where your current AI breaks.

No formal documentation. If you can describe what you want the AI to do, who uses it, and where it falls short today, that's plenty for a productive first call.