02 · Intelligence
Applied AI
We integrate large language models where they earn their place — agentic workflows, retrieval, and evaluation that survive contact with production.
How we think about it
Most AI projects fail on integration, not on models. We close the gap between a capable model and a reliable feature.
What you get
- AI features users actually trust
- Automated workflows with humans in the loop
- Measurable accuracy via evals
- Cost and latency under control
Stack & methods
- LLM integration (Claude, OpenAI, open models)
- Agentic workflows & tool use
- Retrieval-augmented generation (RAG)
- Evals, guardrails, and observability