Building a context warehouse

If the concept of a context warehouse makes sense to you and you want to build one, this post covers the practical implementation: what to connect, in what order, what to expect at each stage, and how the system produces value from day one while compounding over months.

The good news: you don't need a data engineering team, a multi-month implementation project, or a migration from your existing tools. A context warehouse is a layer that sits on top of your existing infrastructure, ingesting from the tools you already use and producing structured, searchable context without requiring anyone to change how they work.

Step 1: Connect your sources

The context warehouse needs raw material: the discussions, decisions, code activity, and communications where your organisational knowledge is created and shared.

Start with the highest-value sources:

Slack (or your primary messaging tool). This is where most informal knowledge exchange happens: design discussions, troubleshooting threads, decision debates, context sharing. It's also the most ephemeral source, which makes capturing it the highest priority.

Meeting recordings. Meetings contain the richest context in the organisation: strategy discussions, planning sessions, client calls, architecture reviews, incident post-mortems. Without capture, this context evaporates after the meeting ends.

GitHub (or your primary code platform). PRs, commit messages, code review comments, and issue discussions contain the technical context that explains why systems are built the way they are.

Then expand to cover more ground:

Email. Client correspondence, vendor communications, internal discussions that happened in email rather than Slack.

Google Drive / Dropbox. Documents, presentations, reports, and reference materials.

CRM / project tools / design tools. Each additional source adds another layer of context that makes the warehouse more comprehensive.

Each connection takes minutes to configure. The system begins ingesting immediately.

Step 2: Enable the transformation layer

Raw Slack messages and meeting recordings are too noisy to be useful as a knowledge base. The transformation layer, self-writing documentation, converts the raw input into structured, citable documentation.

What the transformation produces:

Decision records from Slack threads and meetings where decisions were discussed and resolved. Each record captures what was decided, what alternatives were considered, and why the chosen approach was selected.

System documentation from GitHub activity. Engineering wikis that describe what each system does, how it works, and how it's changed over time, generated from PRs and code review discussions.

Meeting summaries with extracted action items, decisions, and key discussion points. The hour-long meeting becomes a five-minute read with citations back to the specific timestamps where each point was discussed.

Team-specific documentation for sales, product, engineering, and other teams, generated from each team's specific activity channels.

The transformation is continuous. As new activity flows through the connected sources, the documentation updates to reflect the current state.

Step 3: Enable the query layer

The accumulated, transformed context needs to be findable. Two query interfaces serve different needs:

Semantic search for human users. Natural language queries that find context by meaning. "Why did we choose microservices for the payments system?" finds the relevant decision record, the Slack discussion, and the meeting transcript where the decision was debated, regardless of the specific words used in any of them.

MCP for AI agents. Any compatible AI agent can query the context warehouse through the open protocol. The AI assistant can answer questions about the accumulated context, synthesise across documents, and produce responses grounded in organisational knowledge. External AI tools can also query the warehouse, which means your context infrastructure enhances every AI tool in your stack.

What to expect at each stage

Week 1. Sources connected, transformation active. The system begins capturing context from current activity. Semantic search returns results from the first few days of ingested content. Early wins: finding a Slack discussion you would have lost, or getting a meeting summary you didn't have to write.

Month 1. The warehouse has accumulated a month of context. Search results are richer because there's more content to find. The AI assistant's answers are more grounded because it has more context to draw on. The team starts searching the warehouse before asking colleagues, because the answer is often faster.

Month 3. The warehouse contains enough historical context to answer questions about decisions made weeks or months ago. Onboarding new team members is noticeably faster because they can search the warehouse for the context they need. The decision log is comprehensive enough to prevent most relitigated decisions.

Month 6+. The warehouse is a genuine institutional memory. Questions that previously required finding the person who was there and asking them can be answered by searching the warehouse. AI agents querying the warehouse produce responses that are specific to your organisation in a way that generic models can't match. The compounding advantage is visible: the warehouse at six months is qualitatively different from the warehouse at one month.

What you don't need to do

You don't need to migrate from existing tools. The context warehouse is a layer on top of your existing tools. Everyone keeps using Slack, GitHub, Google Drive, and whatever else they use. The warehouse captures context from these tools without replacing any of them.

You don't need anyone to write documentation. The self-writing documentation handles the capture and transformation. Engineers, salespeople, product managers, and everyone else continues working as they always have. The documentation is a byproduct of their work, not an additional task.

You don't need a dedicated team to maintain it. The warehouse maintains itself because the documentation is generated from live sources. When the underlying activity changes, the documentation updates. No manual maintenance required.

Frequently asked questions

How much does it cost? The context warehouse runs on top of your existing tool subscriptions. The cost of the warehouse layer itself is a fraction of a single engineering salary and produces value that scales with the size of the team and the volume of accumulated context.

What about data security? The warehouse respects the access controls of the underlying tools. Content that's restricted in Slack or Google Drive remains restricted in the warehouse. Bring-your-own-storage means the accumulated context lives in infrastructure you control, encrypted with your keys.

Can we start with just one team? Yes, and this is often the best approach. Start with the team that has the most acute knowledge management needs (typically engineering or sales), demonstrate the value, and expand to other teams. The warehouse becomes more valuable as more teams' context is included.

Related reading: What is a context warehouse?, Context warehouse vs knowledge base, Why AI agents need a context warehouse. Related pages: Self-writing docs, Connections, Search, MCP, One search.