Keeping Regulatory Content Current
Your guidance was accurate when written — but the law has moved on. AI-powered currency review built on Evidence Packs, Knowledge Graphs, and AI Guardrails.
“What's past is prologue.
Guidance goes stale
Regulatory guidance is a living obligation. But the process of keeping it current? It's reactive, manual, and nobody owns it end to end.
"I have 400 guidance documents and no idea which ones are still current"
No systematic review process exists. Reviews are triggered by complaints, not proactive monitoring. Currency status is unknown across the library.
"When legislation changes, we don't know which guidance is affected"
Cross-referencing legislative changes against guidance is done manually, inconsistently, and only when someone thinks to check.
"We promised the Minister we'd review all guidance by year-end"
A ministerial deadline is approaching and manual review won't get there. The team is stretched and the library is larger than anyone realised.
"Staff turnover means nobody knows what needs updating anymore"
Institutional knowledge has been lost. There is no documented currency status. New staff inherit an opaque guidance estate with no map.
It's not about more reviewers
The problem isn't that your team isn't working hard enough. The problem is that manual currency review doesn't scale. Your guidance library grows, legislation changes, staff turn over — and the gap between what's current and what's published widens.
Some documents are current. Some are outdated. And nobody knows the full picture.
Most agencies review only a fraction of their guidance each year. The rest is assumed current until proven otherwise.
Many guidance documents haven't been reviewed since publication. Some reference legislation that has been amended multiple times.
Subject matter expert time is expensive and scarce. Manual review competes with every other priority for the same people.
Reviews are triggered by complaints or incidents, not systematic monitoring. By the time you know, the damage is done.
AI that watches the regulatory environment, so your team can focus on redrafting
We don't replace your subject matter experts. We eliminate the mechanical work of tracking what's changed and figuring out what's affected. Your experts focus on updating content. The system handles detection.
Automated Comparison
Continuously compare existing documents against updated legislation, standards, case law, and best practice. Detect where the regulatory environment has moved.
Prioritised Revision Queue
Generate a risk-ranked queue with immediate escalation for high-severity outdated content. Your team acts on the highest-risk gaps first, not whichever document happens to be next in the alphabet.
From guidance library to currency dashboard
A systematic process designed for continuous monitoring, risk-based prioritisation, and governance reporting.
Ingest your guidance library
All guidance documents, policies, and operational content ingested. Metadata extracted, references mapped, document relationships identified.
We normalise content across formats, extract publication dates, review dates, and referenced legislation. Each document becomes a traceable unit in the system.
Map regulatory references
AI identifies legislation, standards, and regulations referenced in each document. Builds a dependency graph showing which documents rely on which legal instruments.
Unlike keyword matching, we understand context. A reference to "the Act" in one document is resolved to the specific legislation. Informal references and abbreviations are handled through domain Knowledge Graphs.
Monitor for changes
Continuously compare against published regulatory changes — legislation amendments, standards revisions, regulatory updates, and new instruments.
When a statute is amended, a standard revised, or a new regulation published, the system identifies every document in your library that references or depends on the changed instrument.
Detect inconsistencies
Flag where your guidance no longer reflects current requirements. Categorise by severity and type of change — from minor terminology updates to substantive legal shifts.
Each flagged inconsistency comes with an Evidence Pack showing the original reference, the change that occurred, and the specific sections of guidance affected.
Generate revision queue
Produce a prioritised revision queue ranked by risk. High-severity items — where guidance contradicts current law — are escalated immediately.
Priority considers severity of change, public-facing visibility of the document, frequency of use, and regulatory risk. Your team acts on the highest-risk gaps first.
Track and report
Dashboard showing currency status across your entire library. Progress reporting for ministerial and governance requirements.
Real-time visibility into how many documents are current, under review, or flagged. Exportable reports for governance boards and ministerial briefings.
What changes
Complete library coverage, not selective review. Every document assessed for currency.
Continuous monitoring replaces annual reviews. Changes detected within a day of publication.
Production deployment on schedule. Not a research project — a working system.
Governance commitments delivered on time. No excuses, no extensions.
The questions you should ask
"Legislation changes are too complex for AI"
AI detects objective changes — amendments, revisions, repeals, and new instruments. Phase 2 adds subjective assessment of how those changes affect your guidance. Human experts still make the final call on how to redraft. The system tells you where to look; your team decides what to do.
"Our guidance references aren't standardised"
We build domain Knowledge Graphs that understand your reference patterns, even when they're inconsistent or informal. "The Act", "HSWA", "the Health and Safety at Work Act 2015" — all resolved to the same instrument. The system adapts to how your documents actually reference legislation, not how they should.
"We tried this before and it didn't scale"
Previous approaches relied on keyword matching — brittle, high false-positive rates, and unable to handle context. Ours uses semantic understanding of regulatory relationships. We understand that a change to section 36 of an Act affects guidance that references "employer duties" even without a direct section reference.
"What about subjective changes in practice?"
Phase 1 covers objective legislative changes — the things you can verify against published sources. Phase 2 incorporates shifts in industry practice, technology changes, and evolving best practice that render guidance obsolete even when the law hasn't changed. We build incrementally, starting with what's provable.
Built on Evidence-First AI
Content currency review is powered by the three pillars of our Evidence-First AI platform.
