Services
Build grounded, sovereign GenAI — not chatbot demos.
Six engagement tracks mapped to AI, Enterprise AI, and Medical AI outcomes for regulated organisations.
Enterprise Knowledge Graph
Ontology design, entity & relation extraction, GraphRAG retrieval over Neo4j / pgvector — turn siloed documents and ERP records into a reasoning layer your LLMs can trust.
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Model Context Protocol (MCP) Servers
Production-grade MCP servers exposing your enterprise tools, data sources and APIs to LLM clients — with auth, audit, evals and policy guardrails.
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On-Premise Generative-AI Automation
Agentic workflows that read, decide and act inside your perimeter — document review, complaints triage, inspection automation, BI copilots.
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On-Premise / Sovereign LLM Platform
vLLM, Ollama, LLaMA, DeepSeek, Mistral on your own GPUs — sized, hardened, observable, and integrated with your IAM and SIEM.
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AI Strategy & Governance
Use-case discovery, ROI modeling, Responsible-AI policies, vendor selection and a 90-day implementation plan tailored to regulated environments.
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MLOps / LLMOps
Evaluation harnesses, guardrails, observability, CI/CD for prompts and models, drift & cost monitoring — production discipline for GenAI.
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How an engagement runs
- Discovery (1–2 weeks) — interviews, data audit, use-case prioritisation, ROI model.
- Architecture (1–2 weeks) — reference architecture, security & data-residency design, eval plan.
- Pilot (4–8 weeks) — one high-value workflow shipped to production users with metrics.
- Scale (ongoing) — platform hardening, MCP catalog, KG expansion, team enablement.