Pavo System Knowledge
Knowledge systems that learn from every investigation, experiment, and outcome.
Pavo builds a verifiable System Book from code, data, traces, experiments, and expert knowledge, giving every person and agent the context to improve the system intelligently.
- 01Reconstruct the system
- 02Verify what is true
- 03Compound every learning
System Knowledge
How notification eligibility works
Who is allowed to receive a lifecycle message, and the policies that gate every send. Reconstructed from code, warehouse queries, policy docs, and one expert rule.
Key policy claims
The worldview
Knowledge systems should continuously verify what they know, learn from outcomes, and invent new knowledge through work.
Knowledge is not static documentation. It is continuously strengthened through expert verification, investigations, experiments, interventions, and production results.
Propagate every update →
A complete system for building and improving production knowledge.
Reconstruct the system. Verify critical claims. Apply trusted knowledge across improvement work. Learn from every decision and production outcome.
The System Book, in full
Every claim keeps its sources, its conflicts, and its verification.
Scattered evidence resolved into a book people and agents can both read, and audit.
Detailed product case
From scattered evidence to verified system understanding.
Question
How does the notification system decide who should receive a message?
Scattered evidence
- CodePolicy logic
- WarehouseEligibility data
- ServiceFatigue rules
- ExperimentsTreatment evidence
- DashboardsOutcome metrics
- ExpertsDocumented exceptions
Before Pavo
6 sources · unreconciledThe same decision is described six different ways, with conflicts unresolved and no single source people or agents can trust.
Pavo reconstructs and verifies
System Book updated- 01Reconstruct the full decision flow
- 02Surface a conflicting claim
- 03Ask an expert to verify it
- 04Update the System Book
- 05Make verified context available to people and agents
- 06Write the later experiment outcome back into knowledge
Verified answer
6 sources · 1 conflict resolvedA user receives a message when eligibility clears the fatigue rules and an active treatment applies, unless a documented exception overrides it, the conflict Pavo surfaced and had an expert verify.
Knowledge in action across Pavo
The context layer behind every improvement.
System Knowledge is the context layer behind Opportunity Discovery, Frontier Benchmarks, Interventions, and human work.
- 01
Opportunity Discovery
Uses system structure, metrics, policies, prior investigations, and domain knowledge to find meaningful headroom.
Learn more - 02
Frontier Benchmarks
Uses system goals, historical failures, expert criteria, and production outcomes to define what better means.
Learn more - 03
Interventions
Uses architecture, constraints, prior attempts, and outcome memory to determine which changes are technically and strategically credible.
Learn more - 04
Human work
Gives teams verified context, prior decisions, unresolved questions, and outcome memory for investigations and execution.
Knowledge is not the output of the improvement loop. It is what makes every part of the loop intelligent.
Research behind systems that learn from experience.
System intelligence, context retrieval, multi-agent systems, production decision systems, continual learning, and knowledge verification.
FAQs
System Knowledge is a structured, verifiable model of how a production system works: its architecture, policies, objectives, decisions, constraints, outcomes, and unresolved questions.
Pavo assembles that model into a System Book that people and agents can use as shared context for investigation and improvement.
A wiki stores documents, and enterprise search helps retrieve them. A System Book reconstructs claims and relationships across code, data, traces, experiments, documents, and expert input, then records where each claim came from and how strongly it is supported.
It is designed to support decisions and execution, not only discovery, and to learn when those decisions produce new evidence.
Critical claims carry provenance, confidence, freshness, conflicts, and verification state. Pavo can check claims against source systems, compare contradictory evidence, and route uncertain or high-impact questions to an expert.
The result is not a claim that everything is known. It is an explicit record of what is verified, what is inferred, and what still needs an answer.
Investigations, experiments, interventions, and production outcomes write their findings back into the System Book. Pavo also tracks source freshness and can flag claims whose evidence has changed or whose supporting systems no longer match.
Every improvement cycle therefore starts with the accumulated result of the cycles before it.
Yes. The same structured context can support people, Pavo agents, and other agents working on the system, with provenance and verification metadata available alongside the underlying claim.
That gives an agent the system-specific context it needs without treating a retrieved document or an old decision as unquestioned truth.
The useful starting set is the evidence your team already relies on: code repositories, relevant data and traces, experiment history, system documents, dashboards, and access to domain experts who can resolve important questions.
Pavo begins by mapping the system and its sources, then prioritizes the claims and relationships that matter most to the first improvement objective.