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Solutions for IT & Engineering

Stop Rebuilding.
Start Governing.

AI agents that connect your internal systems, automate incident response, and give every team a shared integration fabric — with the security controls your organization actually requires.

The Engineering Infrastructure Gap

Most teams are blocked not by model quality, but by missing infrastructure — auth, governance, safe production access, and shared tool catalogs.

💥

MCP sprawl: agents drown in tool noise

Dozens of MCP servers, hundreds of tools, no coherent way to use them. Agents manually pick which server to talk to, duplicate functionality goes ungoverned, and teams keep rebuilding the same integrations from scratch.

🚨

Incident response is too slow and too manual

When something breaks, responders have to jump between monitoring, ticketing, and runbook docs before they can act. Time spent correlating is time not spent resolving.

🔒

Giving agents access to production is terrifying

There's no safe path to let agents touch production systems without opening firewall ports or giving them dangerously broad credentials. So automation stalls.

🔧

Every squad rebuilds the same integrations

OAuth wrappers, retry logic, pagination handlers — rewritten from scratch by every team, every quarter. Hard-won API knowledge lives in Slack threads, not reusable infrastructure.

Enterprise AI Use Cases

What Your Engineering Agents Can Do

Governed workflows across your entire engineering stack — with audit trails, policy controls, and safe access to production systems built in.

MCP Sprawl → One Governed Endpoint

Plug all your existing MCP servers into the Multiplexer and expose them as a single, coherent endpoint. Tools are deduplicated by SemIO semantic type, policies enforced uniformly, and agents route to the right tool automatically — no manual server selection.

  • Connects all upstream MCP servers (your own, Composio, Zapier, etc.) into one endpoint
  • Deduplicates overlapping tools by semantic type — agents always get the best match
  • Uniform policy layer across all sources — no per-server governance gaps
Hub

Incident Response Automation

When an alert fires, an agent correlates signals from monitoring, fetches relevant runbooks, updates the incident ticket, and notifies stakeholders — before a human has opened their laptop.

  • Correlates alerts across monitoring, logging, and APM tools
  • Surfaces relevant runbooks and past incident context automatically
  • Updates tickets and notifies on-call with structured context
HubIntelligenceMemory

Deployment Pipeline Coordination

Request 'deploy feature X to staging' and an agent runs tests, checks dependencies, updates tickets, triggers the deployment, and notifies stakeholders — eliminating manual coordination across tools.

  • Connects CI/CD, ticketing, and monitoring in one governed workflow
  • Validates pre-conditions before any deployment step runs — with cryptographic CTCs as proof
  • Wrap internal services, deployment scripts, and test frameworks as versioned tools any agent can safely invoke
HubIntelligence

Private Network Integration

Connect on-prem databases, legacy ERP, and air-gapped systems to your agent stack without opening inbound firewall ports. Your data center stays dark — your tools don't.

  • Outbound-only VPN tunnel, zero inbound firewall rules
  • Full policy enforcement and audit logs still apply
  • mTLS authentication, scoped credentials, instant revocation
Hub

Documentation & Runbook Maintenance

Agents that watch your systems and keep documentation current — flagging drift, proposing updates, and maintaining wikis aligned with actual system state.

  • Detects documentation drift against actual system configuration
  • Proposes wiki updates grounded in real architectural decisions
  • Architectural decisions and past incidents stored as persistent memory
HubIntelligenceMemory

AI Cost Visibility Across Your Engineering Stack

IT teams are often accountable for runaway LLM costs without the tooling to explain them. Intelligence's observability layer surfaces per-agent, per-workflow, and per-model token spend — so you can answer the CFO's questions and optimize before costs compound.

  • Token usage attributed by team, agent, and workflow
  • Cadence loop detection prevents runaway retry costs
  • Route low-stakes calls to cheaper models automatically via Governor
Intelligence

Security That Ops and GRC Can Approve

DataGrout isn't just access control — it's a governing layer baked into every tool call your agents make.

🔐

mTLS identity + kill switch

Agents prove identity at the TLS layer with DataGrout CA-signed certificates. Per-connector IP allowlists and instant revocation mean stolen keys are just souvenirs — and you can kill any tool, connector, or agent without tearing down the whole stack.

🛡️

SemanticGuard: policy at every tool call

SemanticGuard enforces side-effect controls (none/read/write/delete), validates OAuth scopes, applies tool allowlists, and blocks destructive operations — before the API call happens. Governance rules cascade from org → server → integration → agent.

🚫

Redactor: PII stays out of agent context

Redactor auto-detects emails, SSNs, phone numbers, and credit cards in tool responses and applies field-level redaction before data reaches agents or logs. Enforced at the Hub layer — infrastructure-wide, not per-prompt.

🚪

Private Connectors for on-prem

Outbound-only VPN tunnel to your data center. Zero inbound firewall rules. Your crown jewels stay fenced in while agents can still reach them — with full SemanticGuard, Redactor, and audit enforcement still applying.

📋

CTCs: cryptographic audit trail

Cognitive Trust Certificates (CTCs) are issued before any workflow executes and updated with a runtime receipt after. Cryptographic proof of what ran, what it accessed, under which policy. Answer any audit question with confidence.

Built for Every Engineering Role

Platform / Infra Engineers

  • Central tool registry — all APIs published as typed, versioned tools
  • Private Connectors bridge on-prem without inbound firewall rules
  • OAuth, mTLS, routing, and logging baked in — not re-coded per app

Engineering Managers

  • Shared integration catalog every squad can reuse — no more rebuilding OAuth and pagination per team
  • Policy-aware tool calls prevent production accidents — kill switches work instantly
  • CTCs provide tamper-evident receipts for every automated workflow

Security / IT / GRC

  • SemanticGuard + mTLS + IP allowlists + outbound-only tunnels — 7-layer enforcement stack
  • Redactor strips PII at the Hub layer before it reaches any agent or log
  • CTCs answer 'what did our agents do last week?' with cryptographic proof

Data / ML Engineers

  • Swap models freely — tool mesh, memory, and planning logic stay the same
  • Discovery Engine gives LLMs a structured way to pick tools and build plans
  • Focus on model quality and evals, not wiring brittle REST calls around them

Give Your Engineering Teams a Shared AI Fabric.

One integration layer. Governed access. Every team building on the same rails.

Get Started Free

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