I/O Mesh — Founding team — eng/product mesh from first commit

Who it's for

CTO / product lead at seed-stage SaaS.

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

dept.* stream patterns

dept.engineering.events.github

Browse data product catalog →

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. Talk to us when ready.
Pricing: Estimate usage meters and add-ons

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.

Founding team — eng/product mesh from first commit