Every product requirement, tech spec, service dependency, and architecture decision. All queryable by any AI agent via MCP or HTTP.
Requirements live in Notion. Specs live in Google Docs. Architecture lives in people's heads. Decisions live in Slack threads that scroll away.
Each repo has its own CLAUDE.md. Each agent gets a different slice of truth. Skills and prompts aren't shared. One team's agent doesn't know what another team's knows.
When your agent conversation ends, everything it learned is gone. The same gotcha gets re-discovered by different people every week.
Without asking 3 people and checking 4 tools. There's no single place to see all products, their status, and what's blocking launch.
Context is a single, version-controlled database that holds all of Runpod's product intelligence. Humans browse it through the Explorer. AI agents query it through MCP or HTTP. Learnings persist across sessions. The same source of truth serves everyone.
Search requirements, specs, and architecture across all products. No GitHub access needed — just a browser.
Any AI tool (Claude Code, Cursor, Codex) can query the full product context via MCP. Your agent knows what Runpod is building before you write a line of code.
Learnings, patterns, and gotchas are stored permanently. Institutional knowledge doesn't quit when people change teams.
Open the Explorer and log in with Okta. No setup needed — see every product, requirement, and spec right now.
Create a token, then run the one-liner below to connect Claude Code, Cursor, or any MCP client.
Your agent can query requirements, architecture, and learnings. Humans and agents share the same source of truth.
Tell your agent what to build. It handles the full lifecycle: requirements, specs, codebase exploration, implementation, review against acceptance criteria, and syncing back to the database.
Query product requirements and architecture directly.
Create PRDs with domains and requirements.
Write technical specs with failure modes and rollout plans.
Compile a brief from requirements, specs, and architecture.
All skills are auto-discovered via load_skill. No extra setup.
You talk to your agent. It queries Context behind the scenes and comes back with real, structured answers.
Markdown files, Notion pages, and CLAUDE.md work until they don't. Here's why structured data wins.
Each repo's CLAUDE.md has a partial picture. Agent in repo A doesn't know what repo B's agent knows.
Docs drift from reality. Nobody updates the Notion page after the design changes. The CLAUDE.md references a function that was renamed.
"Show me all P0 requirements blocking launch" requires opening 12 files. There's no way to filter, sort, or join across documents.
A requirement mentions a service by name. But there's no link — rename the service and nothing updates. No way to trace intent to code.
Agent discovers a gotcha, works around it, conversation ends. Next person hits the same wall.
Every agent in every repo queries the same database. Same requirements, same architecture, same learnings.
Every change is a commit. You can diff, branch, review, and roll back. The database is the audit trail.
"Show me all P0 requirements blocking launch" is one query. Cross-reference requirements with services, specs, and learnings instantly.
Requirements link to services via edges. Specs link to requirements. Merge gates enforce completeness — no orphans allowed.
Agent saves a gotcha. It surfaces automatically the next time anyone touches that service. Institutional memory that doesn't quit.
PRDs, requirements, acceptance criteria, and specs. Queryable via SQL.
Every service, dependency, and ownership boundary in one graph.
Search across every PRD, requirement, spec, and service.
Gotchas and patterns discovered during implementation, shared across the team.
Inspired by DO-178C (aerospace) and ISO 26262 (automotive) — industries where you must trace every requirement from business intent to running code. Context enforces the same chain for Runpod: no orphaned specs, no unlinked requirements, no surprises.
Every link in this chain is a queryable edge in the database. You can trace any requirement back to the business intent that created it, or forward to the service that implements it. Merge gates enforce completeness — you can't land a spec without linking it to requirements.
Your agent sends SQL queries. The database answers instantly.