Independent laboratory · ongoing
Make professional context durable.
NiallOS is my working laboratory for AI-assisted knowledge work. It is a local-first operating system for projects, research, documents, insights, contacts, tasks and AI workflows. It is not a chatbot. It is a structured environment for making professional context persistent.
What this page is: a method case, not a client programme. I use NiallOS every day on my own work. It is not a public product and not represented as a Cambridge client project.
The problem
Most knowledge work fails in the same place: context.
The important material is scattered across documents, folders, emails, chats, meetings and people’s heads. AI can generate fluent text, but it often lacks the project memory, source grounding and judgement needed for serious work.
NiallOS is my answer to that problem. It is the system I use every day to make context durable enough for AI to work with it.
The design bet
Professional expertise can be encoded into systems, not just prompts. The useful unit is not a clever chat interaction. It is an operating layer made from source material, expert judgement, workflow, retrieval and review.
What it does
A working layer around real projects
These are functions of the laboratory, not product features on a public roadmap.
Hold the work
Tracks projects, tasks, milestones, contacts, journal entries and decisions so the story of a piece of work does not live only in inbox threads.
Ingest and verify
Takes documents in and turns them into extracts and reusable insights that can be checked against the source, rather than summarised once and forgotten.
Search the material
Supports keyword and semantic search across source material, so retrieval is a first-class part of the work, not an afterthought in a chat window.
Keep memory structured
Stores project knowledge in a structured database rather than leaving it in chat history that expires, drifts or cannot be audited.
Reuse the method
Uses skills and quality gates for repeated work such as research synthesis, workshop design, document review and proposal writing.
Bound the agents
Packages work for AI workers with enough context to be useful, and keeps human review in the loop before anything is treated as finished.
Why it is on this site
I do not start client work from theory.
NiallOS is where I test the patterns: document ingestion, knowledge grounding, insight review, project workspaces, semantic search, agent handoff and human-in-the-loop quality control.
The same principles apply at organisational level. Capture the knowledge that matters, structure it, connect it to workflows, ground it in evidence, and make AI outputs reviewable.
What it is not
Not a chatbot. Not a SaaS product. Not a client case study.
- Not a generic productivity app. It is built around real projects, research and professional judgement.
- Not something you can log into. There is no public NiallOS service, and I am not selling the instance.
- Not a substitute for the programme pages. DELP, KFAS, ECOSOCC, Zambia and Worldreader are delivered work with public sources. This page is the laboratory behind the method.
Confidentiality: NiallOS holds working material from consulting, research and personal practice. This page describes the method. It does not publish client files, internal databases or screenshots that would expose other people’s work.
Trying to build this kind of capability?
I work with education organisations and professional teams on encoding expertise, grounding AI work in sources, and putting review around delivery.