04 / 04CapabilityAI

AI evaluated before production, and only where it pays off.

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

Team cases

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

OpenAIAnthropicLangChainPyTorchVector DBs (Pinecone, Weaviate)ModalLlamaHugging FaceMLflowRagas

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.