How do you stop the model from making things up?
We ground responses in your own content with retrieval, add evaluation and guardrails, and keep a human in the loop for anything high-stakes.
Services
AI agents & LLM apps
LLM features, retrieval, and autonomous agents built into your product — grounded in your data, evaluated for accuracy, and safe to put in front of users.
Where this helps
Use this when you want AI to do real work in the product — answer from your own content, take actions through tools, or automate a workflow — without shipping something that hallucinates in front of customers.
Passende Arbeiten ansehenWhat gets better
- 01
Answers grounded in your data
Retrieval and evaluation keep responses tied to your real content, so the model helps instead of guessing.
- 02
Agents that take safe actions
Tool use, guardrails, and human-in-the-loop checkpoints let an agent do work without going off the rails.
- 03
Quality you can measure
Evals, tracing, and cost controls make model behavior observable, testable, and affordable to run.
What's included
Retrieval (RAG)
Your content indexed and retrieved so answers stay grounded in real sources.
Agents & tools
Tool-using agents that take actions behind guardrails and approvals.
Evaluation & tracing
Evals, logging, and traces that make model quality visible and testable.
Safety & cost controls
Rate limits, fallbacks, and budgets that keep AI safe and affordable in production.
Good fit
AI work pays off when a model removes real effort or unlocks a feature — and when accuracy and safety are treated as requirements, not hopes.
Best when
You have proprietary content or a workflow where retrieval, automation, or an assistant creates clear value.
Not ideal when
You want AI added for the label, with no data to ground it and no way to measure whether it helps.
You leave with
A grounded, evaluated feature, prompt and retrieval infrastructure, guardrails, and cost and quality dashboards.
How we work
- 01
Entdecken
Wir kartieren das eigentliche Problem, die Rahmenbedingungen und wie Erfolg aussieht – vor der ersten Zeile Code.
- 02
Gestalten
Abläufe, Oberfläche und Architektur gemeinsam entschieden, damit die Umsetzung ohne Überraschungen kommt.
- 03
Bauen
Enge Iterationen, die du jede Woche sehen und nutzen kannst. Keine Blackboxes, kein großes Finale am Schluss.
- 04
Launchen und betreuen
Wir launchen, beobachten die Kurven und bleiben dabei, um zu härten, zu messen und weiter zu verbessern.
Service questions
The short answers buyers usually need before a scoping call.
Which models do you use?
We stay model-agnostic — typically the latest Claude, GPT, or open models — and choose per task based on accuracy, latency, cost, and privacy.
Is our data safe?
We scope access carefully, avoid training on your data, and can keep sensitive workloads inside your own infrastructure where needed.
Hast du etwas, das es wert ist, gebaut zu werden?
Erzähl uns, was du machst. Wir sagen dir – ehrlich – ob wir das richtige Team dafür sind und wie wir es angehen würden.
- Antwort innerhalb eines Werktags
- Ein konkretes Angebot – kostenlos
- Direkt zu einem Ingenieur, ohne Vertriebsschicht