ECF (Enterprise Context Fabric) sits between your enterprise systems and your AI applications. It connects to approved sources, syncs the data those workflows need, applies governance and identity controls, and returns grounded context to your agents and LLMs.
Current product posture: sellable pilot MVP. Founder-led demo and paid pilot, not self-serve SaaS and not a one-time ZIP download.
ECF runs as a separate service and connects to approved enterprise systems through connectors, APIs, exports, or controlled integration paths. It should not install itself into a customer's production database or mutate their schema.
ECF is built to narrow aggressively before retrieval: tenant, actor/role, micro-RAG domain, connector source, memory category, and retrieval strategy. The LLM never has to inspect the full enterprise corpus directly.
Customer agents and apps call ECF first. ECF returns governed context, citations, recall signals, and trace metadata through compile flows and /rag/answer before the model generates an answer or action.
4-6 week paid pilot with 1-3 connectors, 1-2 AI workflows, one bounded tenant, and explicit success criteria agreed before start.
Run ECF in the customer's environment or as a dedicated Agoragentic-managed pilot. The product is maintained enterprise software with upgrades, migration guidance, and support, not a buy-once static drop.
Approve systems, help scope connectors, configure identity and governance, wire their agent/app to ECF, and review request traces and retrieval outcomes with operators.
ECF is not yet a full SAML/SCIM/multi-provider enterprise IAM product.
There is no self-serve checkout or self-service enterprise onboarding flow yet. This is a founder-led pilot motion.
ECF is designed to sync, scope, and index the subset of data needed for AI workflows. It should not brute-force search raw enterprise storage in real time.
No. The current motion is paid pilot first, then an ongoing enterprise relationship with updates, migration guidance, and operating support.
As a separate service in the customer's environment or a dedicated pilot environment. It uses its own runtime and database, then connects to approved upstream systems.
Through REST APIs, Python or TypeScript SDKs, connectors, and tenant-scoped /rag/answer or compile endpoints. Their LLM uses the context ECF returns.
The strongest current story is governed enterprise context for real internal AI workflows: connectors, memory, policies, identity baseline, traceability, and operator control.