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AI Integration & Knowledge Management

Make AI investments that deliver real business value

Retrieval-Augmented Generation Knowledge Management AI Governance Responsible AI

Generative AI creates real productivity gains, but only when implemented with structure and accountability. Unreflective adoption creates new risks: hallucinations presented as facts, institutional knowledge locked in black-box tools, and compliance exposure in regulated environments.

I help organisations identify where AI genuinely fits, evaluate feasibility and compliance implications, and build production-ready solutions. My approach is deliberate: I use AI tools extensively in my own work and have a practitioner’s understanding of where they accelerate and where they fail.

Knowledge management is one of the most concrete and high-value AI use cases for organisations with complex, fragmented information landscapes, making internal documentation searchable, building assistants that draw on actual company knowledge, and structuring information so it can be found and used when needed.

PackageScopeDeliverable
AI Readiness Assessment5–10 daysWritten assessment with concrete recommendations
Knowledge Management with AIRAG systems, semantic search, internal assistantsProduction-ready solution on your infrastructure
AI Integration ProjectsGenerative AI features in existing systemsFull integration including governance and monitoring

What I don’t do: Not every problem needs AI. I will tell you when a traditional solution is the better fit, and I won’t recommend lock-in to proprietary platforms without clear justification. Full accountability for what I build, no “the algorithm decided” stance.

For whom

  • Leaders evaluating where AI actually fits in their organisation
  • Companies with fragmented internal knowledge
  • Regulated environments needing AI governance and accountability
  • Teams replacing brittle automation with deliberate LLM use

What you get

  • AI readiness assessment with concrete recommendations
  • RAG-based knowledge management systems
  • Production-ready AI features with governance and monitoring
  • Decision frameworks for "AI or not?"

How we work

  1. Use-case discovery
    Where AI fits, and where it doesn't
  2. Feasibility & compliance
    Technical viability and regulatory implications
  3. Prototype
    Small, measurable, throwaway-OK
  4. Production hardening
    Governance, evals, monitoring
  5. Operate
    Continuous evaluation and drift monitoring

Engagement format

Readiness assessment (1–2 weeks) → prototype (2–4 weeks) → production · phased commitment

Related Work

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