AI that fits into real decisions, with people in the loop

What it is

This service covers the full path from data to working AI. On the machine learning (ML) side, that means features, training, a model registry, deployment and monitoring, run through machine learning operations (MLOps) practices. On the generative AI (GenAI) side, it means large language models (LLMs), retrieval-augmented generation (RAG) over your documents, vector search and embeddings, prompt management, and LLMOps, the equivalent discipline for language models, to test and release changes safely.

We also design AI agents and agentic workflows, in which a model plans steps and uses tools such as search, databases and business applications to complete a task. Agents are given narrow permissions, clear limits and human-in-the-loop approval for consequential actions. Applications range from document intake and claims triage to advisor support and knowledge search.

What we deliver

• Use case selection and feasibility assessment, with data readiness, risk tiering and success criteria.
• ML pipelines with a feature store, experiment tracking, a model registry and automated release.
• RAG assistants over governed content, with source citations and access-aware retrieval.
• Agent designs with defined tools, scoped permissions, approval steps and full action logs.
• An evaluation framework with test sets, quality metrics, red-team tests and regression checks.
• Guardrails for inputs and outputs, including filtering of personal information and policy checks.
• Monitoring of quality, drift, latency, cost and usage, with alerts and rollback.
• Human-in-the-loop review workflows, documentation and training for reviewers.