AI applied to your operations — not a conference demo.
We integrate foundation models and specialized agents into real business flows. RAG with your data, fine-tuning when it pays off, serious evaluations before production, MLOps with observability and drift monitoring. CyberFort Lab — a platform we helped build — is the proof: it operates 24/7 with 9 specialized agents.
— Recurring problems
- Team "wants to use AI" without a clear use case
- Chatbot POC that works in demos but hallucinates with real customers
- Model in production with no evals — you do not know if it is worse than last month
- OpenAI / Anthropic costs spiking each time traffic grows
- Legal worried about data privacy in prompts
— What we deliver
- Honest use-case prioritization by ROI vs risk
- Agents with guardrails, evals and observability — not naïve chatbots
- RAG over your corporate data with permission segregation
- MLOps pipelines: deploy, monitoring, drift detection, rollback
- Cost optimization: model routing, caching, batch processing
- Team training in prompting, evals and agent operations
— Concrete cases where we did this
CyberFort Lab (a platform we helped build): 9 AI agents in 24/7 production running infrastructure audits, threat detection and executive reports with eIDAS signature
E-commerce: recommendation engine (vector DB + collaborative filtering) + first-line support agent with RAG over the catalog
Fintech: credit scoring with SHAP explainability — approved by superintendency, not a black box
Banking: fraud detection with drift monitoring, deterministic-rule fallback when the model is unsure
Figures and companies anonymized or public with permission. Detailed references under NDA.
— Typical stack we master
— Questions we get the most
When do you NOT recommend using AI?
When the problem is solved with deterministic rules (cheaper, more auditable). When you do not have quality data. When error cost is very high and you do not accept hallucinations. When your team cannot operate the model after handoff.
What models: OpenAI, Anthropic, open models?
All three. Anthropic Claude for complex reasoning tasks. OpenAI GPT for volume and cost. Open models (Llama, Qwen) when latency, data sovereignty or cost justify it. Smart task-type routing cuts cost 40-60%.
How do you decide an agent is production-ready?
Evaluation suite (golden set, adversarial cases, operational metrics) that runs in CI every time the prompt or model changes. An agent only ships to production if it passes the agreed threshold. And we monitor drift in production.
— How we engage on this pillar
— Industries where we apply ai most
Does your ai challenge fit what we do?
30 minutes online with a senior consultant. No sales pitch. We tell you if we fit.