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
- 01Team "wants to use AI" without a clear use case
- 02Chatbot POC that works in demos but hallucinates with real customers
- 03Model in production with no evals — you do not know if it is worse than last month
- 04OpenAI / Anthropic costs spiking each time traffic grows
- 05Legal worried about data privacy in prompts
What we deliver
- 01Honest use-case prioritization by ROI vs risk
- 02Agents with guardrails, evals and observability — not naïve chatbots
- 03RAG over your corporate data with permission segregation
- 04MLOps pipelines: deploy, monitoring, drift detection, rollback
- 05Cost optimization: model routing, caching, batch processing
- 06Team training in prompting, evals and agent operations
Concrete cases where we did this
- Case 01
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
- Case 02
E-commerce: recommendation engine (vector DB + collaborative filtering) + first-line support agent with RAG over the catalog
- Case 03
Fintech: credit scoring with SHAP explainability — approved by superintendency, not a black box
- Case 04
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
01When 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.
02What 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%.
03How 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? We tell you honestly.
30 minutes online with a senior consultant. No sales pitch. We tell you if we fit.