Enterprise demo

See ECF in action.

Choose an enterprise scenario below. See what systems connect, how ECF scopes retrieval, when it can use branch-aware structural retrieval for long documents, how it treats retrieved content as untrusted evidence, and what the operator trace looks like before the model ever answers or acts.

This demo is intentionally buyer-facing. It shows the repeatable ECF pattern across platform, support, engineering, security, and operations workflows without dumping an internal system diagram on the page.

Explore scenarios Start pilot intake Try customer copilot
5 scenariosOperator traceGoverned retrieval
Choose a scenarioWatch the flow
Interactive enterprise demo composition showing scenario selection, the ECF runtime, and grounded outputs.
Use this walkthrough to show exactly where ECF sits between approved systems and enterprise AI consumers.
Choose an enterprise scenario below.

One runtime, five enterprise patterns.

Each scenario shows the same ECF motion: approved systems feed the runtime, the runtime scopes before retrieval, and downstream AI receives grounded context with trace.

AI platform team builds governed context for every internal consumer.

Instead of each team shipping its own retrieval pipeline, the platform team deploys ECF once. Internal copilots and agents call the same enterprise runtime for tenant-safe, actor-aware context.

Tenant scopingPolicy enforcementOperator visibility

Approved systems

  • Confluence
  • SharePoint
  • Internal APIs
ECF runtime
  • Tenant scoping
  • Policy enforcement
  • Retrieval strategy

Outputs

  • Internal copilot
  • Agent workflows
  • Grounded answers
Operator trace preview
tenantacme-corp
actorplatform-copilot-v2
sources_usedconfluence::engineering, sharepoint::handbook
policytenant_boundary, domain_scope:engineering
citations3 sources, 7 passages
answer_modegrounded_rag

Buyer takeaway

ECF becomes the governed context layer every internal AI consumer can share, rather than letting each team invent its own retrieval surface.

Architecture

Approved systems → ECF → grounded AI outputs.

Use this view in sales, discovery, and pilot conversations to show exactly where ECF sits without dumping an internal system diagram on the buyer.

ECF four-layer architecture: sources, connectors, ECF runtime, and output consumers.

The architecture story stays simple.

  • Approved systems stay the system of record.
  • Connectors and sync establish the governed working set.
  • ECF applies scoping, retrieval policy, answer controls, and action gating before downstream model use.
  • Retrieval can be keyword, semantic, hybrid, or branch-aware structural for long documents with named sections.
  • Internal apps, copilots, and agents receive grounded context instead of direct raw-system access.
  • Retrieved content stays evidence-only, and risky outbound or delegated actions can be blocked or reviewed.
Governance

Every request is traceable and reviewable.

The governance layer is the differentiator. ECF returns context plus the evidence required to operate enterprise AI responsibly, even when source content is noisy or adversarial.

ECF operator console showing audit trace, grounded answer, and policy indicators.
  • Full request trace: which connectors, scoping rules, sources, and policies were applied.
  • Grounded citations: every answer links back to the approved sources that contributed context.
  • Policy enforcement: tenant, actor, domain, and retrieval policies applied before model use.
  • Retrieval safety: suspicious hidden-content or prompt-injection patterns can be flagged before they steer the runtime.
  • Memory review: low-trust memory stays reviewable or quarantined instead of silently becoming durable context.
  • Operator review: platform teams can inspect any request end-to-end before production use.
Ready?

See ECF govern your enterprise context.

Start with a structured intake or book a live pilot demo with the Agoragentic team.

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