Two Principles
- Evidence, not the organization: Operational Intelligence reports what the evaluated evidence demonstrates, not what the organization definitively is or is not.
- Observations, not conclusions: A valid finding looks like “No recovery documentation was found within the evaluated knowledge.” It does not conclude “Your organization has no disaster recovery plan.”
FAQ Ally prepares knowledge for AI agents. Operational Intelligence evaluates that prepared knowledge and the structured records extracted from it. Additional documents may exist outside FAQ Ally; findings stay accurate as statements about what was evaluated.
What Operational Intelligence Is For
It helps you improve the completeness, consistency, trustworthiness, and usefulness of the knowledge available to an AI agent, and related operational records that agent (or company-scoped evaluation) can see.
It is not an organizational assessment, CMDB replacement, governance platform, or consulting product. It does not claim to understand your entire organization.
Q&A vs Operational Intelligence
| Capability | Document Q&A | Operational Intelligence |
|---|---|---|
| Primary question | What does our documentation say? | What does the evaluated evidence show needs attention? |
| Trigger | Someone asks in chat or Smart Tools | Scheduled evaluation, data change, or on-demand run |
| Evidence base | Passages and structured records | Evaluated structured records, relationships, and agent knowledge inventory |
| Output | Cited answer for that question | Persistent findings with lifecycle, scope disclosure, and delivery |
| Trust signals | Retrieval and answer validation | Evidence Strength (computed) and Scope Assurance; verification before publish |
One Experience, Two Filters
Operational Intelligence is a single product surface. On Insights, filter by:
- Knowledge Intelligence: findings about evaluated knowledge (quality, completeness, consistency, AI readiness).
- Operational Findings: findings calculated from evaluated structured records (financial, contracts, assets, security, service desk, projects, and related).
- All Findings: both together.
These are filters, not separate destinations. Knowledge Health remains a specialized projection for an AI agent’s readiness. Knowledge Hub (files, snippets) remains where you manage sources.
Company and AI Agent Visibility
FAQ Ally still separates truth ownership from who is looking:
- Company truths exist once (for example a runbook gap observed for a system in evaluated knowledge). Multiple agents do not create duplicate company findings.
- Agent truths come from that AI agent’s training and usage (for example frequent questions without verified answers for that agent).
- Relevance: Company findings can appear for an agent when FAQ Ally can explain why they affect that agent’s inventory, without blending unrelated agents’ training into one score.
Losing an entity from one agent’s inventory does not clear a company finding by itself. Narrower reevaluations may mark scope as degraded; they do not invent “the company is fine now.”
How Findings Are Presented
Findings use layered wording, not disclaimer spam on every title:
- Title: short observation (example: “No recovery documentation found for Payroll System”).
- Scope label: Evaluated knowledge or Evaluated records.
- Summary: what was searched and what was not found or measured.
- Tooltip: reminds that additional information may exist outside FAQ Ally.
Trust chips prioritize Evidence Strength (is this supported?) and Scope Assurance (how complete was the evaluation?). Evidence Strength is computed from the typed claim and evidence coverage, never manually assigned as an independent authority.
Absence Findings Stay Valid When Scoped
“No recovery documentation was found in the evaluated knowledge” remains true as a historical observation even if another document exists elsewhere. That outside document means the evaluation scope was incomplete relative to the whole organization, not that FAQ Ally’s observation was false.
Knowledge Intelligence Catalog (examples)
- No documented owner found for a policy or system within evaluated knowledge
- No runbook or recovery documentation found for a system within evaluated knowledge
- Proven process without complete procedural guidance in evaluated knowledge
- Documentation nearing or past review; frequent questions without verified answers for an AI agent
- Department knowledge coverage observations when department metadata is present
Conflicting or duplicate knowledge detectors may expand Knowledge Consistency later; they are not claimed as shipped here unless available in your tenant.
Operational Findings Catalog (examples)
- Financial and contract observations from evaluated records (spend change, concentration, linkage gaps, expiry windows)
- Security and access observations from evaluated records (MFA field state, access-review dates, overdue vulnerabilities)
- Service desk and project threshold observations (aging tickets, SLA breaches, overdue milestones)
- Operational Findings Summary: a rollup of active operational findings from evaluated records (not an assessment of organizational health)
How Evaluation Works
- Evaluate: Deterministic rules over structured records and relationships. No LLM invents severity.
- Verify: Evidence verification before publish; incomplete coverage leads to review or abstention, not a polished guess.
- Publish with typed claims: Each finding carries a typed claim (what was observed) separate from recommended actions (what you might do next).
- Lifecycle: Detected, verified, active, resolved, expired, dismissed, and related states. Recommendations do not overclaim beyond the observation.
- Deliver by policy: Delivery Administration decides recipients. Email never recomputes intelligence or invents claims.
Trust Boundaries
- Deterministic evaluation definitions in code, not generative prompts inventing risk.
- Verification before proactive feed and alert paths by default.
- Review-recommended items stay dashboard-first until policy allows delivery.
- Abstain when required evidence is missing (for example a contract may simply not be ingested yet).
- Correlation findings are labeled as correlation, not causation.
- Human decisions remain human for finance, legal, security, and compliance actions.
How This Fits the Broader Stack
- Documents are trained and searched.
- Typed business records are extracted and reviewed (Business Data Intelligence).
- Relationships connect entities across domains.
- Operational Intelligence evaluates conditions against that evaluated evidence.
- Verification and delivery policies decide what is published and who is notified.
Limits
Operational Intelligence is available on Small and higher plans. Coverage depends on what you train and approve. Insight types that need tickets, MFA evidence, recovery plans, or contract links will not fire usefully until those records exist in the evaluated scope. High-stakes decisions still need human verification of sources and context.
Related: Business data intelligence | Structured data + AI search | Why document chatbots fail on numbers | Beyond RAG | How to verify AI answers