LEAD — Organizational Context Layer for Enterprise AI & Search

// the gap in enterprise AI

Why enterprise AI/search
needs this layer

Document retrieval and content graphs solve the information layer. They don't solve the people layer. In real organizations, the signals that determine whether an AI action is safe, accurate, and trusted live in behavior — not content.

Decision rights are not in documents. Who can approve or escalate an action is never reliably written down in retrievable form.
Authorship ≠ ownership or execution. The person who wrote a document is rarely the current operator, owner, or approver of the thing it describes.
Org charts ≠ real influence networks. Informal authority, trust, and execution power flow through relationships that reporting lines don't capture.
Workflow definitions ≠ workflow reality. Actual handoffs, escalations, and routing differ significantly from any documented process.

Result: without runtime organizational context, AI misroutes work, violates decision boundaries, and loses user trust — adoption stalls.

Fix: LEAD makes trust, authority, expertise, and execution pathways retrievable as structured signals that can be injected into search ranking, UI, and agent action policies.

Three-layer architecture diagram showing LEAD as the Behavioral Layer beneath the Information and Process layers

LEAD is the behavioral layer — the missing foundation beneath information and process layers.

// structured context objects

What LEAD delivers

Five categories of structured organizational context — each derived from behavioral signals, each embeddable directly into your product stack.

01 · experts

Experts

People with demonstrated, behaviorally-verified authority on a given topic — ranked by signal strength, not title or self-declaration.

02 · owners

Owners

Who is accountably operating and responsible for a domain, process, or resource right now — with confidence and recency signals.

03 · approvers

Approvers

The actual approval chain for a given action scope — named individuals, ordered by authority level, drawn from real decision patterns.

04 · trust

Trust Signals

Behavioral trust scores between people and teams: collaboration frequency, delegation patterns, and network centrality in the real influence graph.

05 · routing

Routing Paths

Shortest trusted paths to resolution — who to loop in, in what order, to get a task approved, escalated, or executed correctly.

// integration patterns by platform type

How product teams use it

LEAD embeds into your existing architecture. It doesn't replace your search stack or agent framework — it adds the people-side context layer your stack currently lacks.

01 / Enterprise Search

People answer panel + behavioral re-ranking

Surface a "people who can answer this" panel alongside document results. Re-rank using expert authority scores. Route ambiguous queries to the right person — not just the right document.

expertise signals trust scores re-ranking people panel

02 / Agent Platforms

Approval gating + safe delegation logic

Before an agent takes a consequential action, retrieve the real approvers for that action scope. Gate execution. Route to the correct human-in-the-loop — based on actual authority, not hardcoded role maps.

approvers routing paths approval gating HITL routing

03 / GenAI Applications

Inject structured org context into prompts and tools

Enrich your prompts, tool calls, and retrieval augmentation with organizational context. Give your LLM the "who" that document retrieval cannot provide — traceable, structured, and scoped to user context.

owners explanations context injection prompt enrichment

// architecture

Embed → Retrieve → Inject

Three integration steps. No pipeline rebuilds. LEAD slots alongside your existing retrieval and agent infrastructure as a context layer.

STEP 01 · EMBED

Your system provides context

User query or agent task description
Relevant domain or topic scope
Action type — search, approve, execute
Requesting user identity

STEP 02 · RETRIEVE

LEAD returns structured org context

Ranked experts with confidence scores
Named approvers ordered by authority
Trust signals between relevant nodes
Routing paths to resolution
Human-readable explanations for each signal

STEP 03 · INJECT

Inject where it matters

Re-rank search results by expertise signal
Gate agent actions on real approvers
Surface people panel in your search UI
Route tasks through trusted execution paths
Prepend owner/expert context to LLM prompts

User Query / Agent Task

→

LEAD Context Layer

→

Structured Org Context Objects

→

Search UI · Agent Gate · LLM Prompt

LEAD sits alongside your vector store and document retrieval — adding the behavioral/people layer that content graphs structurally cannot provide.

// the data foundation

Behavioral Knowledge
Acquisition Dataset

LEAD turns everyday collaboration into a structured behavioral dataset — continuously capturing how knowledge and influence actually move through organizations, without disrupting work.

Passive

Calendar and collaboration patterns — who works with whom, how frequently, in what contexts. Continuous, low-friction, requires no manual input from employees.

Active

Pulses, micro-surveys, and explicit expertise and trust signals — targeted, minimally invasive, high-signal inputs layered onto passive data.

Programs

Lightweight structured interactions that reveal how knowledge and influence move — surfacing organizational dynamics that passive signals alone don't capture.

Our models are trained on anonymized behavioral signals across thousands of organizations — enabling generalization far beyond any single deployment's data volume.

Visualization of organizational trust and influence networks modeled by LEAD — clusters of people connected by real collaboration patterns

Real organizational networks — clusters of trust, influence, and execution authority modeled from behavioral signals.

// differentiation

Beyond content graphs
and document RAG

You already have vector search and knowledge graph products. LEAD adds what those systems structurally cannot model: the behavioral, people-network layer.

⚡

Behavioral, not structural

Authority and expertise are inferred from real collaboration patterns, decision history, and trust formation — not from org charts, titles, or curated profiles that quickly go stale.

🔗

Runtime retrieval, not pre-configured maps

No manually maintained role-to-approver maps or expert directories. Organizational context is retrieved dynamically at the point of need, with freshness signals built in.

🎯

Trust and influence signals documents don't have

Trust scores, delegation patterns, and network centrality let your search and agent products reason about authority — not just semantic similarity to text content.

🔌

Designed for safe agent actions

Approval gating and execution routing grounded in real organizational authority prevents agents from acting outside decision boundaries — building the user trust that drives AI adoption.

// use cases

Built-in use cases

Three core capabilities that become available the moment LEAD is embedded in your stack.

Trust-aware enterprise search

Augment document results with behavioral re-ranking and a people-answer panel. Surface the right expert for the right query — identified by who actually has domain authority, not who wrote the most content.

→ Signals: expertise, trust, ownership
→ Inject into: ranking layer, search UI
→ Outcome: precision content retrieval alone can't reach

Approval-safe agents

Gate agent actions with dynamic approval context. Retrieve the actual approvers for a given scope — ordered by authority — and route to the right human in the loop. No hardcoded role maps to maintain.

→ Signals: approvers, routing paths
→ Inject into: agent gate, escalation workflow
→ Outcome: agents that stay within real decision boundaries

Expert discovery + execution routing

Identify who to contact for a given task or domain and compute the shortest trusted path to resolution. Route work through the network that actually executes — not the formal reporting line.

→ Signals: experts, routing, explanations
→ Inject into: agent tool calls, prompt context
→ Outcome: faster resolution with traceable attribution

// security & deployment

Enterprise-ready deployment

Built for enterprise data sensitivity requirements. Deployable in your environment, auditable, and permissioned at the source.

✓

Compliance

Current status: [SOC 2 — verify]. Additional: [ISO 27001, GDPR readiness — verify].

✓

Deployment options

Supports [single-tenant / VPC / on-premise — verify]. Behavioral data does not transit shared infrastructure without explicit configuration.

✓

Data retention & deletion

Configurable retention windows. [Retention policy — verify]. Right-to-deletion support for individual records.

✓

Permissions model

Returned context is scoped to the requesting user's authorization. [SCIM / RBAC integration — verify depth]. No over-permissioned context exposure.

✓

Audit logs

Full audit trail of every context retrieval — who requested, what scope, what was returned. [Log export options — verify].

✓

Network isolation

Traffic stays within your perimeter. [Private endpoint / PrivateLink — verify]. Behavioral signals do not leave your environment unencrypted.

// enterprise traction

Used by teams building
enterprise AI at scale.

LEAD is in production with enterprise AI and search product teams. Specifics available under NDA — reach out to start the conversation.

[ Customer logo row — available under NDA ]

[Xm+]

Org relationship signals modeled

[X]

Enterprise deployments in production

[Xk+]

Organizations in training dataset

[Case study headline — e.g., "Enterprise search team reduced expert-finding time by X% after embedding LEAD's behavioral context layer into their people-answer panel."]

Details available on request. We work with customers under mutual NDA during partner evaluation. Contact us to see architecture diagrams and outcome data from existing deployments.

// get started

The people-side layer
your enterprise AI needs.

We work with product and engineering teams at companies building enterprise AI and search products. If you're evaluating org context as a layer in your stack, let's talk.

Partner evaluations are conducted under mutual NDA. Bring your stack architecture — we'll show you exactly where LEAD embeds and what it changes.

Organizational Context Layer for Enterprise AI & Search

Why enterprise AI/searchneeds this layer