Generic AI without ops context
Agents answer from stale dumps and public knowledge — not the live pulse of how your teams actually work.
Live self-serve · Governed context plane
Production agents need a heartbeat.Not another chat window.
I/O Mesh is the context plane behind them. Live operations, institutional memory, and patterns — department-scoped, policy-gated, compounding.
I/O Mesh is the context plane — the heartbeat after a source, a stream, and a signed event. It is not a replacement for observability (Datadog, Grafana), incident command (PagerDuty, incident.io), issue tracking (Linear), or a workspace (Notion). It is not an MCP gateway or agent control plane product. Still not another chat window, and not a company-wide vector dump.
Explore the mesh. Base from $132/mo. Self-serve after payment.
See it for
Investigate with the incident and the deploy on the same record. PagerDuty pulse first, GitHub change second. Digest cites both plus your RCA notes, or says the join is missing.
context://mesh · sre.incidents · policy-gated MCP
Heartbeat
18 / min
- 14:02:11opssre.incidents
P2 opened — checkout p95 1.8s
Signed from pager. Team tenancy: sre.oncall
- 14:02:18opseng.ops
Deploy v2.14.3 → prod-eu
Heartbeat linked to runbook RB-441
- 14:02:24opscs.tickets
Enterprise ticket · latency on checkout
Scoped to cs.enterprise — not a global index
- 14:02:31knowledgesre.incidents
Recall: similar p95 in Mar
Institutional memory of last quarter
- 14:02:39analyticsgtm.pipeline
Renewal risk +12% this week
Pattern across heartbeats
This is a demo of the console heartbeat. Your org shows this after the first signed event on a mesh stream.
Built for teams shipping production agents
- Platform Eng
- SRE
- B2B SaaS
- Fintech
- RevOps
- Product Ops
The gap
Production agents need a governed heartbeat.
I/O Mesh is a governed operational data mesh. Live SaaS, docs, and metrics land as department-scoped data products your agents consume through policy-gated tools.
Company-wide vector dumps
One big index mixes every department. No tenancy, no policy, no clear ownership when agents act.
Inference spend before proof
Teams scale model usage before proving workflow lift on real operational facts.
How it works
From signal to lasting advantage.
Build · Steer · Compound. Prove workflow lift on ops facts before you scale inference spend.
- 01
Connect a source
PagerDuty first. GitHub second. First signed event is the heartbeat.
Connect PagerDuty first when the buyer owns incidents. GitHub second for the change that usually caused the page. Slack waits. Heartbeat after a source, a stream, and a first signed event.
- 02
Create a mesh stream
A named inbox such as OPERATIONAL_EVENTS.
- 03
The first signed event becomes heartbeat
The console shows events after a signed message lands on that stream. Empty until then.
- 04
Agents attach via MCP
Copy the Agent / MCP connection when you want agents to read the pulse.
One heartbeat. Three signals.
The full organizational pulse.
Department-scoped streams — not a company-wide vector dump. Agents get the real-time pulse of work, the memory of what it meant, and the patterns that emerge.
Live operations
Tickets, incidents, deploys, and customer updates, organized by team. The primary heartbeat a reviewer can see after a source, a stream, and a signed event.
Knowledge
Docs, runbooks, and memory of past heartbeats. What previous activity meant. Reviewers can open this layer in the product; do not chip it as a readiness label.
Analytics
Patterns and trends across heartbeats so agents see beyond the moment. Same rule: describe the layer, do not chip a status.
Now
Signed events you can consume from a mesh stream.
Hours–days
Recent activity and rolling operational context.
Weeks–years
Analytical history plus free local MIT memory on your machine.
Why teams choose I/O Mesh
Always current
Agents work with the live heartbeat of your business, not outdated snapshots.
Knowledge that grows
Today’s activity strengthens tomorrow’s decisions across pulse, memory, and patterns.
Control and security
Teams keep ownership of their information. The organization still compounds intelligence.
Smart investment
Prove real gains before scaling AI use or costs.
Solutions
Open a workflow wedge. Map a learning loop.
Product-led to enterprise
B2B SaaS
Engineering, product, sales, CS, and support publish department-scoped products. Agents consume them through policy, not a shared vector index.
Explore →AI-SRE with lineage
Platform engineering and SRE
Agents investigate with the live pulse of the platform — signed events, runbooks, and the memory of last quarter’s pages — not another alert chat box.
Explore →Revenue learning loops
GTM and RevOps
Forecast narratives from governed facts.
Explore →Lean hybrid pricing
Start small. Prove value. Scale with confidence.
Base platform plus usage from $132/mo. Enterprise for multi-tenant and multi-region.
Most teams start here
$132/mo
- 1 workspace + 1 connector + 25 GiB
- Live operations mesh
- Policy-gated MCP tools
On your machine
Paid mesh is paid
- iomesh-tui, the memory kernel, and MCP run with your tools. Paid mesh is paid. This is not a freemium palace.
Evaluation
How teams evaluate I/O Mesh
Workflow maps: Incident RCA, Platform SRE, Account health, Finance analytics. Private evals on your facts under department tenancy. No public lift percent. No logos.
Incident RCA
When the page fires, the incident, the change, and the notes often live in different tools. Wire the pager pulse and the change pulse, then drop RCA notes you already wrote. If the digest cannot name all three, it says so. Private evals only. Not a PagerDuty replacement.
Platform SRE
Deploy and incident narratives on one record. Connect the service that pages you and the repo that shipped the change. Private eval: time to a linked pull request and how complete the root-cause write-up is. Not a Datadog replacement.
Account health
When CRM and support streams exist, agents read health and support burden under the same tenancy rules as ops. A catalog name is not Connected. Private evals on your facts. No renewal lift percent. No logos.
Finance analytics
Governed metric views with freshness and lineage when that path is allowed. Not unconstrained warehouse SQL inside prompts. Private evals only. No board ROI claim.
Questions
Straight answers. No chat UI, no model lock-in.
Self-serve · live today
Prove lift before you scale inference.
Base from $132/mo. Self-serve after payment. Heartbeat after source, stream, and first event.