Connect enterprise data, tools, memory, MCP servers, and zero‑trust access into one governed platform for AI agents.
While LLMs are powerful, they're blind to your private enterprise world. Building production‑grade AI agents means wrestling with fragmented data, security gaps, and zero governance.
Enterprise knowledge locked across dozens of disconnected systems with no unified access layer.
Exposed APIs, unmanaged MCP servers, and zero-trust policies ignored by default.
Zero audit trail for agent decisions, tool calls, or context retrieval events.
AI agents forget everything between sessions — every request starts from zero.
A single, secure gateway connecting your entire enterprise intelligence layer to AI agents — with full governance at every hop.
Everything your enterprise AI stack needs — connectivity, memory, governance, security, and observability — in one governed platform.
Connect your entire enterprise tech stack through a governed MCP layer — GitHub, Slack, Jira, Confluence, databases, cloud platforms, internal APIs, and Kubernetes.
Persistent, semantic memory that evolves across agent interactions. Episodic and semantic memory combined for cross-session continuity that actually works.
Policy-based access control, PII redaction, data residency enforcement, RBAC, tenant isolation, and compliance automation built in at the platform level.
OpenZiti-powered secure tunnels with no exposed endpoints or VPNs. Identity-aware routing and cryptographic agent authentication at every hop.
Track prompts, tool calls, context retrieval, memory usage, token consumption, and agent behavior with enterprise-grade fidelity and full audit trails.
Stop exposing internal APIs to the public internet. CogniContext uses OpenZiti to create secure, identity-based tunnels for every agent communication channel — no VPNs, no exposed endpoints, no compromise.
Every agent and tool has a unique cryptographic identity. No static credentials, no shared secrets, no implicit trust.
Fine-grained role-based access control with strict tenant isolation. Each team's data and tools remain fully segregated.
Seamless connectivity across multi-cloud and on-prem environments without complex network configuration or inbound firewall rules.
High-stakes tool calls require explicit human approval before execution. Policy-aware gating at every sensitive operation.
Powering the next generation of autonomous AI operations across engineering, security, and compliance teams.
Give coding agents secure access to private repos, CI/CD logs, and architecture docs with full auditability and zero credential exposure.
Automate root‑cause analysis by letting agents safely query Datadog, AWS CloudWatch, Slack incidents, and historical incident context.
Unified RAG that respects existing permissions across Notion, Confluence, and internal wikis — governance built in by default.
A central hub to manage and govern all Model Context Protocol servers across your organization with policy enforcement and audit trails.
Enable AI to execute infrastructure changes via governed MCP tools with human‑in‑the‑loop approvals and complete audit trails.
Automatically redact PII and enforce data residency policies before any context reaches the AI model or agent workflow layer.
Track prompts, tool calls, context retrieval, memory usage, token consumption, and agent behavior — with the same rigor as production infrastructure. Full audit coverage across your entire AI fleet.
We're hiring founding engineers who love hard infrastructure problems — distributed systems, security, AI ops — and want to define the category from day one.
Join the waitlist for CogniContext — we'll be in touch shortly.