OpenCentric Studio's AI practice covers the full stack — from strategy and RAG pipeline builds to LLM evaluation, agent orchestration, and AI governance. In-house advisors who've shipped AI in regulated environments.
Executive-level AI readiness assessment, use case prioritisation, build-vs-buy analysis, and a phased roadmap. Delivered as a structured briefing + document.
End-to-end RAG build: document ingestion (PDF, Office, email), chunking strategy, embedding model selection, vector store setup, and retrieval tuning.
Custom eval suite for your use case — RAGAS metrics, hallucination detection, latency benchmarks, and a repeatable CI-integrated evaluation harness.
Multi-agent architecture design using LangGraph, AutoGen, or custom orchestration. Tool definitions, memory strategy, human-in-the-loop design, and failure handling.
AI usage policy, model card templates, data residency review, PII detection controls, and EU AI Act / NIST AI RMF alignment assessment.
Review of an existing AI feature or pipeline: prompt injection risk, model output validation, rate limiting, cost controls, and observability wiring.
LLM observability with Langfuse or Phoenix — traces, token cost dashboards, latency percentiles, and alert thresholds. Delivered as a live dashboard.
Ongoing AI advisor. Monthly strategy sync, feature design reviews, model upgrade analysis, and vendor evaluation as the landscape evolves.
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