Human capital stays in charge
People set goals, connect dots across departments, and decide what matters. Agents amplify that judgment — they do not replace the learning loop.
LoopCompound™ · Build · Steer · Compound
Own your learning loop — not just another model subscription
Own the learning loop. Governed operational data mesh for AI agents — LoopCompound™ · Build · Steer · Compound.
I/O Mesh gives your company a governed, department-owned data mesh for AI agents — so institutional knowledge compounds from live operations, not from another model subscription. With LoopCompound™ (Build · Steer · Compound), you own the learning loop: agents act on real work context, teams steer with portal and policy, and you measure lift before you scale spend — workflow advantage you keep when models change.
I/O Mesh is the context plane behind production agents — not a chat UI, not a memory SDK alone, and not another model subscription. Domain teams publish dept.* data products from live SaaS (incidents, tickets, CRM, PRs); agents consume them through policy-gated MCP tools; optional Agentic Memory Palace adds institutional recall with department boundaries. LoopCompound™ (Build · Steer · Compound) maps nested learning loops to self-serve portal surfaces so you prove workflow lift before you scale inference spend.
For platform & architecture
I/O Mesh is the governed operational data mesh for production AI agents. Live SaaS, docs, and metrics land as department-scoped dept.* data products — not a company-wide vector dump. Agents consume them through policy-gated MCP tools on fabric you control, with optional Agentic Memory Palace for institutional recall. LoopCompound™ (Build · Steer · Compound) maps those surfaces so you prove workflow lift on your ops facts before you scale inference spend.
Ops connectors and fail-open publish are GA; knowledge and analytical mesh layers expand under Beta.
- Usage-based meters — pay for fabric you use, not seat theater
- Optional Agentic Memory Palace when you need long-term institutional recall
- Join the waitlist to get notified when self-serve opens
Compound what your company already knows
You can offload a task to a model. You cannot offload your learning. The durable advantage is a loop where workflows, domain knowledge, and private evals make agents better on your outcomes — not someone else’s public benchmark.
Token capital you own
Turn incidents, deals, tickets, and PRs into reusable agent context with audit and tenancy — expertise that survives model swaps.
Private evals, real lift
Measure recall and workflow lift on your data. Prove agents find the right facts before you scale spend on inference.
A frontier without an ecosystem is not stable — value should compound inside every company, not only inside a few shared models.
Methodology · LoopCompound™
Build · Steer · Compound
For CTOs: three nested loops on one fabric — Build agents on live ops facts, Steer with portal context and optional memory, Compound with usage proof and private evals.
~minutes · Agentic execution
Build Loop
Governed autonomy — agents iterate on real operational facts, not sandbox prompts.
~hours · Developer steering
Steer Loop
Context advantage — owners steer with dashboards and memory, not QA ticket farming.
~days–weeks · Firm compounding
Compound Loop
Firm IQ compounds — prove workflow lift from real usage before you scale agent spend.
An operational data mesh agents can actually consume
I/O Mesh starts where agents need freshness — operational facts from SaaS tools on dept.* streams — then extends the same tenancy to documents and analytical live views without a company-wide vector dump. Compare us for agent production readiness, not warehouse SQL alone.
Operational mesh
GACore path for production agents: live events from how work runs — incidents, tickets, CRM, PRs, chat — with link enrichment and fail-open publish so hot-path ingest is not blocked by memory.
Knowledge mesh
BetaDocs and wiki as dept.* products with the same tenancy as ops events — so agents cite runbooks that match the live incident, not a stale shared chunk index. Connector coverage is expanding under Beta.
Analytical bridge
BetaDual-use live views: metrics and warehouse context as governed output ports for agents — freshness SLOs and catalog discovery without per-agent SQL sprawl. Warehouse drivers expand under Beta.
Product modules
One governed fabric for ops context, mesh routing, GTM loops, and automation — shipped in the customer portal, not a separate admin console.
Operational data mesh
Domain-owned dept.* streams from ops, docs, and warehouse connectors — link enrich, catalog contracts, and MCP compose on one fabric.
- Six ops connectors plus Notion, Confluence, Drive, SharePoint, dbt, warehouse CDC, and embeddings
- Self-serve data product catalog with versioned contracts and lineage
- FAIR metadata, DOI handles, and cross-layer compose via MCP
Integrations · Data products catalog
Mesh routing console
Broker admins configure streams, processors, subject scopes, and policy preview from settings — no separate admin console required.
- Stream and processor registry with audit on create/delete
- Department subject-scope bindings with inline policy preview
- Kafka topic mappings, traffic tap, and route analytics (Governance add-on)
Settings → Mesh routing
GTM suite
Self-serve marketing ingress and pipeline tooling — custom webhooks, first-class Mautic/Matomo, campaigns, attribution, and CRM API.
- Custom webhook receivers with schema inference and test-send
- Email drip campaigns with SMTP, log, or webhook sender modes
- Campaign funnel, multi-touch attribution, and marketing dashboard
Integrations · Settings → Marketing
Automation studio
YAML DAG workflows with visual canvas, n8n handoff, and mesh publish nodes — governed batch paths without retiring your existing automations.
- Workflow templates: GTM webhook drip, n8n handoff, mesh publish
- Visual canvas with node palette and branching DAG runtime
- Listmonk and n8n completion connectors on dept.marketing.events.*
Settings → Automation studio
Enterprise identity & compliance
Sovereign learning loops for regulated evaluators — SSO, SCIM, IdP scope sync, and HIPAA marketing pack mapping.
- SSO and SCIM provisioning with org/workspace hierarchy
- Automatic IdP group → dept.* subject scope sync (Compliance add-on)
- HIPAA readiness mapping and marketing compliance pack
Settings → Security · Marketing HIPAA
Why teams choose I/O Mesh
Scoped institutional memory
Engineering, sales, and CS each keep separate context with audit boundaries — cross-link evidence without one company-wide memory dump.
Workflow-grounded context
Live incidents, tickets, CRM activities, GTM webhooks, and PRs feed agents at event time — not a stale doc index disconnected from how work actually runs.
Compounding eval signal
Built-in benchmarks track memory recall lift on your facts — private eval signal that improves as workflows generate better training context.
Native GTM & automation
Custom webhook receivers, drip campaigns, attribution, CRM API sync, YAML DAG automation studio, and n8n handoff — GTM loops on the same mesh as engineering context.
Mesh routing console
Self-serve streams, processors, subject scopes, and policy preview from the portal — Governance add-on unlocks Kafka mappings, federation routing, and visual Rego policy editing.
Enterprise identity
SSO, SCIM provisioning, and IdP group → dept.* subject scope sync — Compliance add-on aligns access with how your org is structured.
How it works
Connectors → broker → enrich → agent memory → MCP. Fail-open publish keeps agents live.
- 1. Connectors
- 2. Broker streams
- 3. Link enrich
- 4. Agentic Memory Palace
- 5. MCP tools
Why owned loops beat model subscriptions
- Engineering, sales, and CS each keep separate context with audit boundaries — cross-link evidence without one company-wide memory dump.
- Live incidents, tickets, CRM activities, GTM webhooks, and PRs feed agents at event time — not a stale doc index disconnected from how work actually runs.
- Built-in benchmarks track memory recall lift on your facts — private eval signal that improves as workflows generate better training context.
- Custom webhook receivers, drip campaigns, attribution, CRM API sync, YAML DAG automation studio, and n8n handoff — GTM loops on the same mesh as engineering context.
- Self-serve streams, processors, subject scopes, and policy preview from the portal — Governance add-on unlocks Kafka mappings, federation routing, and visual Rego policy editing.
- SSO, SCIM provisioning, and IdP group → dept.* subject scope sync — Compliance add-on aligns access with how your org is structured.
Solutions by vertical
Each industry compounds expertise differently — pick a vertical learning loop for B2B SaaS, platform SRE, RevOps, fintech, or healthtech — open a vertical map, then workflows and connectors.
B2B SaaS
Primary wedge: PLG-to-enterprise learning loops across engineering, product, sales, CS, and support — department-owned context without a company-wide vector dump.
Explore solution →Platform engineering & SRE
AI-SRE-ready context plane: incidents, deploys, and on-call evidence as governed dept.* products — agents that investigate with lineage, not another alert chat box.
Explore solution →GTM & RevOps
Revenue learning loops: pipeline, support burden, and renewal context on one fabric — agents that prep QBRs and forecast narratives from governed facts.
Explore solution →Fintech & payments
Dispute, fraud-ops, and finance close loops with traceable evidence chains — owned institutional memory and audit-friendly dept.* products for regulated evaluators.
Explore solution →Healthtech (B2B provider)
Care-adjacent CS and support for provider customers — governed escalation and account health with enterprise evaluation paths (HIPAA readiness mapping, not a clinical claims system).
Explore solution →See how teams use I/O Mesh
Browse workflow wedges into your learning loop — engineering, GTM, and operations with recommended connectors, private eval paths, and a starting plan.