AI Engineering · Made in Germany
We build AI solutions — from pipelines to agents.
From data pipelines and retrieval to copilots and autonomous peer agents: we design, build and operate production-grade AI systems — on Microsoft Copilot, LangChain, or whichever framework fits your stack.
fig. 01 — agent systemblue: structure · cyan: data · gold: human
Our quality bar
Robust. Precise. Fast. No user frustration.
An agent that guesses, stalls or frustrates its users is worse than no agent — and one that ships next year is no better. We run with a startup mentality: small senior teams, fast decisions, working software in weeks.
Checkpointed workflows, retries and graceful degradation: our agents survive restarts, timeouts and messy real-world data.
Grounded answers with citations, strict schemas for every action, and evaluation gates in CI — precision is tested, not promised.
Startup mentality: a working prototype on your data in weeks, decisions in days, no process theater — momentum is part of the deliverable.
Fast first response, an honest "I don't know" instead of confident nonsense, and a human hand-off that always works.
Peers & simulation — our difference
Subagents delegate. Peers are individuals.
Most multi-agent systems are one brain delegating to helpers. Our peer model — built in Antlet.OS, our peer platform — is different: peers are separate instances with their own session, their own rules and their own private background graph.
The dual-graph model
Every peer holds two graphs — two planes in depth: the shareable view in front, the private state behind it, coordinated on every revision.
- Individuals, not helpers — own state and lifecycle, cooperating over A2A 1.0 plus our lean semantic protocol.
- Simulation built in — a peer can run simulated: we mock ERP counterparts, partner systems or authorities and test whole journeys before go-live.
fig. 02 — the dual-graph modelfront: shared · behind: private
What we build
One engineering path, four levels of autonomy
Every reliable agent stands on reliable data. We build the whole path — and stop at the level your use case actually needs.
Data pipelines
Ingestion, ETL and mappings that turn scattered systems into AI-ready data — batch and real-time.
RAG & retrieval
Hybrid search, reranking and knowledge graphs that ground every answer in your documents.
Copilots & assistants
Assistants in Microsoft 365, Teams and your own products — right where your team works.
Autonomous agents
Multi-step agents that plan, call tools and act — with human approval where it matters.
Full AI development
The most complex custom software — built with AI
We don't just ship AI features. We build software with AI — and have for years. That is why even the most complicated custom systems land in weeks, at a quality bar a hand-only team cannot sustain.
Multi-agent build pipelines
Parallel implementation agents, adversarial review agents, automated verification — orchestrated development, not autocomplete.
Spec- and eval-gated
Specs become plans, plans become verified increments. Every change passes tests and evaluation gates before it counts as done.
Experience that compounds
Years of AI-assisted delivery across ERP, ITSM and platform builds — the same rigor that runs our own products.
RAG · Retrieval · Mappings
Answers grounded in your data — not in a model's imagination
Most AI pilots fail at retrieval, not at the model. We engineer the retrieval layer first — and measure it before anything goes live.
- Hybrid search & reranking — vector and keyword retrieval fused and reranked: the production baseline.
- GraphRAG & agentic retrieval — knowledge graphs and multi-step search where one-shot lookup stops.
- Data mappings & extraction — documents and legacy schemas turned into validated, schema-conformant data.
fig. 03 — retrieval pipelineingest · index · retrieve · ground
Use cases
Concrete work, not concepts
Three engagements that show the range — from data mappings to fully agentic operations.
Source-to-target mappings for Dynamics 365
LLM-assisted schema mapping proposes field-level source-to-target mappings, validates every record against the target schema, and routes low-confidence cases to human review — a repeatable, auditable migration pipeline instead of months of spreadsheet work.
An agent inside ServiceNow, not beside it
From intake in Teams, Copilot or a chat embedded in any app, the agent turns conversations into clean tickets, triages, retrieves from KB and runbooks, and drafts resolutions and reports — with human approval before anything is written back.
FounderOS — our own agentic product
With FounderOS — running on Antlet.OS, our own peer platform — we build an agent-driven operating layer for company builders: independent peer agents carrying the operational load. What we learn there flows straight back into client work.
Frameworks
Fluent in every major agent stack
We recommend the framework that fits your cloud, your compliance requirements and your team — not our favorite.
How we work
From first workshop to operated agents
Discover
Use-case discovery, data audit and a feasibility check — including what not to automate.
Prototype
A working prototype on your data within weeks — with an evaluation set from day one.
Productionize
Hardening, guardrails, observability and CI evals — the unglamorous work that makes agents reliable.
Operate
We run, monitor and improve your agents — or hand them over to your team, fully trained.
Outlook
We're just getting started
The peer platform and its family of domain agents keep growing — this page will keep changing with them. Stay tuned.
Antlet.OS
Our peer platform — catalog, routing, composition and audit for independent agents — heading toward a public release.
Domain peers
A growing family of ready-made peers — citable facts and evidence, legal, banking and authority counterparts.
Simulation packs
Pre-built simulated counterparts — ERP, suppliers, authorities — to test whole journeys before a single real system is touched.
Let's build your first production agent
Bring a use case and your data — we bring the engineering. In a free initial conversation we assess feasibility, risks and a realistic path to production.