COMPUTABLE POLICY
Prescribed knowledge your AI agents can act on with precision, determinism and proof.
Generalised intelligence is commoditising. What’s scarce is what your institution knows and trusts. Rainbird turns your regulations, policies, procedures and controls into one computable model your agents can reason over deterministically, with a full audit trail. Not RAG.
✓ Deterministic reasoning ✓Source-linked evidence ✓Governed knowledge
Computable Policy explained
Turn regulatory knowledge into something you can use
Sam Line explains how Rainbird connects regulations, policies, procedures, controls and expert judgement, helping regulated organisations identify gaps, assess the impact of change and reach clear, defensible answers faster.
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
Same facts, same conclusion.
Every time.
Zero
hallucinations
Symbolic inference over your knowledge, never invented.
THE SHIFT
Intelligence is now abundant.
Trusted context isn’t.
Your prescribed knowledge lives in Word and PDF, written for people. Prompting, fine-tuning and RAG all put a statistical model between your agents and your rules, none can guarantee the same answer twice, or prove how it got there. Computable Policy closes that gap.
THE PROBLEM
A policy estate written for people, not machines
Global banks typically run hundreds of procedures, thousands of addenda, documents into the hundreds of pages, and regulation never stops changing. Proving coverage to regulators is still largely manual.
Gaps
An obligation with no policy, a
policy with no procedure,
a step with no control.
Variance
The same requirement, stated differently across units and jurisdictions.
Drift
Regulation changes; downstream procedures and controls don’t.
WHAT IS COMPUTABLE POLICY?
A governed architecture for prescribed knowledge
Rainbird turns source documents and expert judgement into connected knowledge graphs under one ontology, interrogated as a whole, not searched as files.
Codification is the process. Computable Policy is the capability it creates
The capability can support reference, assurance, remediation and decision automation from one model.
HOW IT WORKS
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 4
Reason and
evidence
Rainbird analyses coverage, gaps, conflict and change impact, then returns source-linked evidence.
PROVEN
Two engagements, one architecture
Same architecture, different densities.
Third-party risk – encoding the estate.
Regulation, policy, procedure and controls encoded into one traceable graph. Demonstrated obligation extraction, golden-thread mapping with break-point diagnosis, proof trees, gap and conflict detection, and change-impact analysis.
Credit decisioning – deciding from the graph.
A single decision built to decision density.
The engine returns the outcome deterministically, with a full audit trail – an agent deciding, and showing its working.
.
ONE ONTOLOGY, A CONTINUUM OF DENSITY
Build the model once. Deepen it where decisions are worth it.
One ontology per domain. What changes is density – how much of the domain is captured.
Document density
Everything the documents state. Delivers the golden thread, assurance answers, change-impact analysis, and gap and conflict detection. Enough to interrogate the estate – not yet to make a decision, because documents leave out the expert judgement every case relies on.
Decision density
Expert judgement encoded into the same model. Now the inference engine reasons over live case data to a decision that’s contextual, deterministic and auditable – every conclusion returned with its proof
.
Encode the whole estate at document density; enrich only the highest-value decisions. The asset compounds, one graph answers questions it was never explicitly built for.
RETRIEVAL VS REASONING
Retrieval finds text. Reasoning reaches conclusions.
Search, RAG, prompting and fine-tuning improve access. High-stakes conclusions need a governed model 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
BUILT TO SCALE
Federated graphs, coordinated by a metagraph
Each document is its own self-contained graph. A metagraph holds the catalogue and golden-thread links – not content – so it stays fast at thousands of documents. Add a new document; nothing existing changes.
WHY RAINBIRD
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.
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.
Book a 30-minute discovery meeting with Sam Line, Senior Account Executive at Rainbird.
Tell us where the policy, assurance or regulatory-change burden is highest.
