COMPUTABLE POLICY
Codify your policy estate once. Prove it on demand, forever.
Rainbird turns regulations, policies, procedures and controls into connected, governed knowledge, so you can prove coverage on demand, see exactly what a regulatory change touches, surface the gaps before your auditor does, and give AI agents decisions they can defend.
✓ Deterministic reasoning. ✓Source-linked evidence. ✓Governed institutional knowledge.
AI Decision Automation for regulated work
Rainbird is built for decisions that must be right and defensible.
Regulator-ready
audit trails
Proof trees show exactly how each decision was logically reasoned.
100% deterministic reasoning
Repeatable outcomes
for the same facts
Zero
hallucinations
Every outcome adheres precisely
to your institutional knowledge.
THE STRUCTURAL PROBLEM
The policy estate was written for people, not machines
Regulations, policies, procedures and controls usually sit across separate documents, systems and teams. The links between them are often rebuilt manually for audit, remediation or regulatory change.
Gaps
An obligation has no policy coverage, a policy has no operating procedure, or a process step lacks an evidenced control.
Variance
The connection exists, but different documents, business units or jurisdictions express conflicting requirements.
Drift
Regulation or policy changes while downstream procedures, controls
or decision models remain unchanged.
WHAT IS COMPUTABLE POLICY?
A governed architecture for prescribed knowledge
Rainbird converts source documents and expert interpretation into connected knowledge graphs under a common ontology. The result can be interrogated as a whole rather than searched as a collection of files.
Documents remain the source evidence. The reusable asset is the encoded knowledge architecture, including the links between obligation, interpretation, operating procedure and control.
Policy codification is the process. Computable Policy is the institutional capability it creates.
The capability can support reference, assurance, remediation and deterministic decision automation without creating a separate model for each use.
CODIFY. CONNECT. EVIDENCE.
From source material to a visible golden thread
Build a governed model once, then use it to analyse coverage, trace change and support decisions.
STEP 1
Ingest and
version
Regulations, policies, procedures and control frameworks retain their source provenance and version history.
STEP 2
Structure
knowledge
LLM-assisted extraction accelerates the draft. Domain experts review and approve the encoded meaning.
STEP 3
Connect the
estate
A shared ontology and coordinating metagraph link obligations to policies, procedures and controls.
STEP 3
Reason and
evidence
Rainbird analyses coverage, gaps, conflict and change impact, then returns source-linked evidence.
WHAT CHANGES?
Move from repeated reconstruction to governed reuse
A connected policy architecture changes how assurance, regulatory change and enterprise AI are supported.
✓ Coverage on demand
See where obligations are covered and where the golden thread breaks.
✓ Faster change impact
Identify the policies, procedures and controls affected by a regulatory update.
✓ Consistent interpretation
Make approved institutional meaning reusable across teams, systems and agents.
✓ Evidence at
source
Return conclusions with the facts, rules and source material that support them.
ONE ONTOLOGY, FROM REFERENCE TO DECISION
The architecture stays consistent. Its knowledge density changes.
A document-led graph can make the estate navigable, traceable and testable. Add expert judgement and tacit practice where an operational decision requires it.
Reference
Find, connect and trace authoritative material.
Assurance
Test coverage, conflict, gaps and change impact.
Decision automation
Apply approved knowledge with deterministic reasoning and evidence.
BUILT TO SCALE ACROSS THE ESTATE
A federated architecture, coordinated by a metagraph
Each regulation, policy or procedure is encoded as a self-contained document-level graph. A coordinating metagraph records the catalogue, shared ontology and golden-thread links between them.
RETRIEVAL AND REASONING
Retrieval is useful. It is not the same as reasoning.
Search, RAG and GraphRAG improve access to relevant text. High-stakes conclusions require a governed model of the domain and a reasoning layer.
RETRIEVAL
Finds relevant material
- Locates passages and documents
- Supports search and summarisation
- Improves access to dispersed information
- Leaves final interpretation to a person or probabilistic model
COMPUTABLE POLICY
Tests the connected meaning
- Represents obligations, interpretation and controls explicitly
- Tests coverage, conflict, variance and change impact
- Applies approved knowledge consistently
- Returns a source-linked rationale for the conclusion
RELEVANT RAINBIRD CREDENTIALS
Designed for regulated, knowledge-dense work
A document-led graph can make the estate navigable, traceable and testable. Add expert judgement and tacit practice where an operational decision requires it.
More than a decade
of experience
Encoding complex regulation, policy and expert judgement into transparent knowledge models.
Deterministic symbolic inference
Repeatable conclusions with a logical reasoning path and supporting evidence.
Governed model lifecycle
Visual build, testing, versioning and maintenance for knowledge graphs.
Fits the existing
stack
Complements repositories, workflows, LLMs, agent frameworks and downstream systems.
FREQUENTLY ASKED QUESTIONS
Questions enterprise teams ask first
Is this a policy-management system?
No. Rainbird can complement systems that author, approve and distribute documents. It adds a computable knowledge and reasoning layer across the estate.
Does this replace subject-matter experts?
No. Experts review the encoded knowledge, resolve ambiguity and add the judgement that documents do not capture. Rainbird makes that approved knowledge reusable.
Can it work with our existing repository and technology stack?
Yes. The architecture is designed to integrate with source repositories, enterprise systems, workflow tools, AI agents and downstream applications.
How is this different from RAG?
RAG retrieves material for an LLM to interpret. Computable Policy represents the domain explicitly and uses symbolic inference where a deterministic, auditable conclusion is required.
Do we have to encode the whole estate before seeing value?
No. A programme can start with a defined domain, establish the ontology and prove coverage, change impact or decisioning before scaling.
What does a first engagement look like?
A discovery session identifies the policy domain, current evidence process, source material, stakeholders and measurable result. Rainbird then proposes a bounded proof or phased programme.
START WITH ONE POLICY DOMAIN
Make the golden thread visible
A 30-minute discovery session will map the current process, identify the highest-value starting point and determine whether Computable Policy is a fit. Read the Computable Policy paper here.
Book a discovery meeting with Sam Line, Senior Account Executive at Rainbird.
Tell us where the policy, assurance or regulatory-change burden is highest.
