Who it's for
CTO / product lead at seed-stage SaaS.
Founding team — eng/product mesh from first commit
Small teams lose context across GitHub, Slack, and docs. Start with GitHub signed events under department tenancy. Slack waits. If you page, add PagerDuty next. Private evals on your facts. Create a workspace when ready.
The problem
Engineering and product context lives in GitHub, Slack, and ad-hoc docs — copilots miss blockers.
Outcome
Department-scoped operational mesh from the first commit — judgment compounds with audit boundaries, not a shared vector dump.
Multi-horizon BI
Founding-team BI starts as eng/product heartbeats: live shipping pulse, short-term blocker context, long-term decision memory.
Real-time
GitHub PR/issue events as live launch and shipping pulses. Slack waits (parked).
Short-term
Recent review queues and sprint decisions as rolling context for agents—without a company-wide vector dump.
Long-term
Optional local MIT memory compounds launch decisions and blocker narratives across sessions; private recall evals before inference spend scales.
Early-stage teams lose institutional memory every model or hire. Department-scoped streams beat shared chat dumps from week one.
Workflow map (2026)
2026 agentic stacks fail at seed stage when teams dump chat into a shared vector store. Winners start with GitHub-scoped eng/product streams. Slack waits (parked).
CTO / founding engProduct leadPlatform generalist
Build
GitHub → dept.engineering and dept.product products with fail-open publish. Slack waits (parked).
Steer
MCP compose + recall scoped to founding departments — no company-wide memory dump.
Compound
Private recall evals on launch blockers and PR review context before you scale inference.
Learning loop
Engineering and product judgment compounds from the first commit — private recall evals on launch blockers and PR review context. Slack waits (parked).
Questions agents answer
- What open tasks block the v1 launch?
- Which PRs are waiting on review before we ship?
- What did we decide in the last sprint about the billing integration?
How it works on I/O Mesh
Step 1
Connect engineering signals
Install GitHub from Integrations — signed webhook ingress publishes to dept.engineering.events.* with tenant isolation. Slack waits (parked).
Step 2
Route by department
Map org departments to dept.* subject patterns so product and engineering keep separate context with cross-link evidence when needed.
Step 3
Compound into memory
Link enrich chains PRs and issues without blocking hot-path ingest. local MIT memory recalls institutional facts across sessions — fail-open publish keeps streams live.
Step 4
Prove recall with private evals
Run memory recall benchmarks on your facts before scaling inference spend — private eval signal, not public leaderboard vanity metrics.
Departments
- Engineering
- Product
Operational mesh
What ships today
- GitHub connector with signed webhook audit — Slack waits (parked)
- dept.* streams usage-metered
- Private recall evals
Governed MCP tools
- compose_data_mesh
- recall_context
- trace_workflow_evidence
- Typical company stage
- startup
- Evaluation path
- Start with GitHub signed events under department tenancy. Slack waits. If you page, add PagerDuty next. Private evals on your facts. Create a workspace when ready.
Outcome metrics
- Leading indicator
- % tasks with agent-accessible context
- Lagging indicator
- Sprint predictability
Recommended connectors
- GitHub
Each live connector bills as a usage meter — install from Integrations after signup.
Related solutions
B2B SaaS
PLG-to-enterprise learning loops across engineering, product, sales, CS, and support — governed dept.* products agents can consume, not a company-wide vector dump.
Platform engineering and SRE
AI-SRE-ready context plane for incidents, deploys, and on-call evidence as governed dept.* products — agents investigate with lineage, not another alert chat box.
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