Knowledge is the new architecture.
The teams winning with AI are not the ones with the best tools. They are the ones whose knowledge is structured, whose context is legible, and whose people know how to build with both.
Most organisations have a knowledge problem disguised as an AI problem. Information lives in Slack threads, in someone's inbox, in a deck on a shared drive nobody remembers naming. AI tools amplify whatever you give them. Give them chaos and they amplify chaos at speed.
The work is upstream. Structure the knowledge. Make the context portable. Then teach people to build with it.
We work with teams to map what they actually know, where it lives, and how it moves. The output is a knowledge architecture: explicit, navigable, and designed for both humans and machines to read. This is not a Notion redesign. It is a way of organising how the team thinks.
Once structure exists, we build the context layer that makes it usable with AI. Prompts that carry institutional voice. Skills that encode methodology. Retrieval systems that surface the right knowledge at the right moment. Agents that work within the team's actual operating model.
The architecture only compounds if the people using it can extend it. We train teams to build their own tools with Claude Code and to collaborate fluently with AI through Claude Cowork. The goal is independence, not dependency.
The structure comes first.
No amount of clever prompting fixes badly organised knowledge.
Voice is part of the architecture.
How a team writes, decides, and reasons is encoded in the system. Generic outputs are an architecture failure.
Build for extension.
Every artefact we ship is documented for the team to modify and extend.
Sovereignty over convenience.
We design for portability. Your knowledge architecture should outlive any single tool.
Knowledge Architecture Sprint
A focused engagement with one team. We map what you know, build the structure, ship the first context layer, and leave you with an architecture you can extend. Typical output: a working knowledge system, three to five custom skills or agents in production, and a team that knows how to maintain them.
Claude Code Training
For technical and semi-technical teams. Participants learn to build real tools with Claude Code: skills, agents, automations, custom workflows. The curriculum is built around your actual use cases, not toy examples. By day two, every participant has shipped something usable.
Claude Cowork Training
For knowledge workers who want to collaborate with AI as a thinking partner rather than a transcription tool. Cowork-specific workflows, document architecture for AI collaboration, voice and context engineering. Hands-on and practical, designed for non-developers.
Agentic Build
Custom agentic workflows built on n8n, Python, Claude or OpenAI APIs, with RAG via PostgreSQL and pgvector where retrieval matters. Scope examples: automated RFP intake, knowledge assistants, document generation pipelines, CRM automation.
- A documented knowledge architecture specific to the team
- Three to five production-ready skills, agents, or workflows
- A team trained to extend the system without us
- A maintenance model that does not require ongoing dependency
Teams between 5 and 50 people that already see AI as a strategic lever and want to move past prompt-and-pray. Founders building lean. Operations leaders running complex workflows. Consulting practices, agencies, and internal teams that produce knowledge as their output.
Selected work: Client knowledge architectures (anonymised on request)