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The Blue River Intelligence Layer

A computable foundation for enterprise intelligence.

Blue River Labs connects fragmented enterprise context, models how entities and events relate over time, produces predictive signals, supports graph-aware reasoning, and governs the workflows that act on that intelligence.

  1. Enterprise InputsL1

    Systems, signals, and decisions as they already exist

  2. Canonical Knowledge GraphL2

    Resolved entities, relationships, and history

  3. F3ML3

    Graph-native predictive signals

  4. GRIL4

    Typed, permissioned briefing

  5. F2RML5

    Graph-aware reasoning

  6. Agent OS / F2 CockpitL6

    Governed execution and oversight

From enterprise signals to governed action.

Six layers, each one taking a specific input and producing a specific output. Open any layer for the plain-language version.

  1. 1

    Enterprise Inputs

    Code, documents, tickets, communications, business systems, operational data, incidents, telemetry, organizational records, and human decisions.

  2. 2

    Canonical Enterprise Knowledge Graph

    A living, text-attributed, temporal graph that resolves the same real-world entities across disconnected systems and maps their relationships, ownership, history, and dependencies.

    In plain terms

    One shared map of the business. The same service, supplier, or product appears once — not five times under five names — and the map remembers how it changed.

    Takes in
    Raw enterprise signal
    Hands on
    Resolved entities and relationships over time
  3. 3

    F3M — Predictive Graph Model

    A graph-native predictive engine designed to learn how relationships, changes, and events propagate through the enterprise.

  4. 4

    GRI — Graph Reasoning Interface

    A compact, typed bridge that packages relevant graph structure, temporal context, predictions, provenance, and permissions for the reasoning layer.

  5. 5

    F2RM — Graph-Aware Reasoning Model

    A reasoning model designed to turn structured enterprise intelligence into explanations, plans, documents, decisions, and tool actions.

  6. 6

    Agent OS and F2 Cockpit

    The orchestration and control layer that manages multi-step workflows, approvals, tool access, provenance, audit trails, and human oversight.

The platform does more than retrieve. It builds an operational understanding.

Entity resolution

The same service, asset, product, person, supplier, decision, or event may appear under different names across multiple systems. Blue River resolves those fragments into one canonical entity.

Structural and temporal context

The graph captures not only what is connected, but how ownership, dependencies, states, and events change over time.

Predictive graph intelligence

F3M is designed to model propagation, risk, likely ownership, likely outcomes, and other signals that cannot be obtained through semantic similarity alone.

Grounded reasoning

GRI provides the reasoning model with a structured briefing rather than an undifferentiated wall of retrieved text.

Governed execution

F2 Cockpit gives people visibility into what the system knows, why it recommends an action, what it plans to do, and where approval is required.

Agent-Agnostic by Design

An ecosystem, not another closed copilot.

The intelligence layer is designed to support workflows built by the enterprise, individual teams, employees, Blue River Labs, and approved third-party providers. Every workflow should inherit the same grounded context, permissions, provenance, and governance.

Platform Direction

Described as designed intent and architectural direction, not generally available marketplaces.

Designed to Enable

Enterprise Workflow Exchange

A team or employee can define a useful workflow for day-to-day work, test it within enterprise controls, and make it available through an internal catalog. Enterprise-wide workflows can be centrally governed while individual workflows remain scoped to the user's permissions.

Designed to Enable

Partner Agent Ecosystem

Vetted third-party applications and agents can connect through governed interfaces and operate inside customer-controlled privacy and permission boundaries. The intelligence layer remains neutral: the best agent for a task can use the same trusted enterprise foundation.

Autonomy earns trust through visibility and control.

Provenance

Every material recommendation should show the nodes, relationships, events, documents, and signals that informed it.

Human control

Sensitive actions can be approved, rejected, modified, escalated, or constrained before execution.

Policy-aware execution

Workflows must respect existing role-based access, data boundaries, and organizational controls.

F2 Cockpit — conceptual view
  1. Step 1

    Recommendation

    Proposed action with predicted impact.

  2. Step 2

    Evidence path

    Nodes, events, and documents that informed it.

  3. Step 3

    Approval state

    Awaiting human review under policy.

  4. Step 4

    Audit record

    What was known, decided, and by whom.

Bring intelligence to the data—not sensitive data to an uncontrolled model.

  • Customer-controlled deployment patterns, including private-cloud or VPC-oriented architecture where appropriate.
  • Role-based access and permission-aware retrieval.
  • Traceability, approvals, and audit trails.
  • Modular industry adaptation without rebuilding the intelligence foundation from zero.

See what becomes possible when the enterprise can reason over itself.

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