Build AI that ships value — safely and responsibly.
We design, prototype and productionize RAG systems, task-driven agents and workflow automation aligned to your data, SLAs and governance. Start with measurable outcomes, end with services your teams trust on day one — and day one hundred.
- Outcome-driven — every use case has a metric, not just a demo.
- Eval-first — automated tests for accuracy, safety and cost, in CI from day one.
- Sovereign by default — Obsigen AI on our EEA stack, with on-prem as a switch.
What we ship
Six capabilities, evaluated and observed in production.
AI roadmap & discovery
Identify high-ROI use cases, classify under EU AI Act risk tiers, build an executable plan with measurable success criteria.
- Use-case shortlist with ROI scoring
- Architecture & guardrails sketch
- Risk classification per Article 6
RAG & knowledge assistants
Ground LLMs in your documents, ERP records and tickets. Row-level security on retrieval, citation-first answers, evals on every change.
- Connectors: SharePoint, Confluence, S3, SQL
- Chunking, reranking, hybrid search
- Answer-grounding metrics in CI
Agents & automation
Multi-step agent workflows that plan, call your tools and act — with tool allow-lists, audit log and human-in-the-loop where it matters.
- Obsigen AI orchestration
- Tool registries, retries, idempotency
- HITL approval steps
Vision & NLP
Detection, classification, OCR, summarization, extraction. From production-floor cameras to inbox automation, with measurable accuracy.
- Document understanding (OCR + structure)
- Quality control on the line
- Multi-language NLP (EN, PL, DE)
Data & MLOps
Pipelines that don't break in production. Evaluation suites, drift detection, model tracking and cost dashboards — boring infrastructure, reliable AI.
- Vector DBs (pgvector, Qdrant)
- Eval suites & drift alerts
- Cost & token observability
Governance & safety
EU AI Act risk classification, DPIA, policy guardrails, audit trail. Built into the system, not a slide deck attached afterwards.
- Article 6 / 9 / 13 mapping
- Policy engine & audit log
- Run-time PII redaction
Stack
Built around Obsigen AI — sovereign by default.
Most of our AI work runs on top of Obsigen AI, our European multi-agent platform. EEA storage and inference, multi-agent orchestration, tool registry, eval suites — already there. We bring the integration into your systems and the discipline that keeps it running.
- No data leaves the EEA — Warsaw storage, Norway GPU inference.
- BYO model — Obsigen open-weights, or Anthropic/OpenAI behind a proxy.
- Tool registry — 6 built-in tools plus your own, with allow-lists and audit.
Engagement
Discovery · Data · Build · Scale.
Four phases. 4–12 weeks for a typical first use case, then continuous operation if you want us to keep running it.
Discovery
Goals, users, constraints, success metrics. EU AI Act risk classification. Decision: does AI fit, or not?
Data & integration
Connect ERP/CRM/SharePoint/databases. Clean, label, chunk. Auth, ACLs and audit log wired in.
Build & eval
RAG, agents or vision pipelines — with eval suites in CI from week one. Weekly demos with real data.
Scale
Roll-out, monitoring, cost dashboards, model rotation. Quarterly evals and a defensible evidence pack.
Use cases
Where we've made AI useful.
Support copilot
Agents triage tickets, draft replies, link to KB articles. Humans approve. SLA-aware routing.
Shopping assistant
Product search, comparison and recommendation grounded in your catalogue — under your brand.
Document automation
Extract structured data from invoices, contracts and forms. OCR + LLM with confidence scoring.
ERP & ops copilot
Predictive maintenance alerts, MRP what-ifs, planner suggestions — with audit trail per action.
Compliance assistant
Map controls, draft policies, answer security questionnaires. Always cites the source document.
Quality control
Vision-based defect detection on the production line, with anomaly trending and NCR drafts.
FAQ
Questions every AI initiative starts with.
Should we even use AI here?
Sometimes no. The first deliverable of every engagement is a clear answer: which use cases are worth doing, which are better solved by a rule or a workflow, which should wait for the next model generation. We say no when the right answer is no.
What about hallucinations?
Three layers. (1) Grounded retrieval (RAG with row-level security and citations). (2) Eval suites in CI that catch accuracy regressions before deploy. (3) Run-time guardrails (PII redaction, content policies, human-in-the-loop on high-risk actions). Together they bring hallucination rates to "acceptable for production".
Will our data train someone else's model?
No. Obsigen AI runs on our EEA infrastructure with strict no-training policies. If you bring your own provider, we configure the proxy with their no-training endpoints (Anthropic, OpenAI both support this for enterprise tiers).
How fast can a first feature ship?
A scoped use case typically 4–8 weeks from discovery to production. The first 2 weeks are discovery and data — the actual model integration is fast; the surrounding engineering (auth, evals, monitoring, governance) is what takes the time.
What about EU AI Act compliance?
Built into the engagement. Discovery includes risk classification (Article 6). Build includes technical documentation (Annex IV). Scale includes post-market monitoring. By the time you go live, you have a defensible evidence pack — see AI Security for the full programme.
Can you maintain it after launch?
Yes — model and prompt drift is real. We offer a retainer with monthly eval reports, model rotation when a new release lands, and on-call for incidents. Or full handover with runbooks if you want to operate it yourself.
Bring us one AI use case.
30 minutes. We tell you whether it's worth doing, the smallest engagement that gets you a defensible answer, and what comes after.
Talk to us