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Gaining Control Over a Complex Policy Landscape

Gain control over a sprawling policy landscape. We make relationships, ownership, and definitions visible and traceable so change impact is clear and updates are safer and faster. This lets policy and governance functions implement and audit changes with confidence.

Delivered as a mix of advisory services, applied data science, and our software Calcifer.

Reality represented

The policy landscape as expressed through thousands of governance documents: standards, policies, instructions, and contracts that inter‑reference and evolve over time. We surface how definitions align or drift, who owns and is accountable for content, and how responsibilities are assigned across roles. This makes change‑impact and implementation paths explicit — what to update, where it propagates, and which stakeholders must act.

Situation​

Policy landscape control

Policy and governance are maintained through a large, evolving set of specifying and regulatory documents across the organisation and its partners. Change requests, reviews, and ownership span many teams and systems, and references accumulate over time.

Client​

Large national public agency with regulatory and operational responsibility for safety‑critical work.

Challenge​

The agency manages a large, interlinked set of specifying and regulatory documents. Person‑dependent, manual practices created inconsistency, slow change cycles, and difficulty understanding downstream impacts when updating rules. Ensuring compliance while changing policy was risky without a transparent view of dependencies, ownership, and definitions across documents.

Objectives​

  • Increase clarity and quality by harmonizing and consolidating specifying documents with AI assistance
  • Evaluate how AI can handle large volumes and complex document relationships
  • Establish traceability from insights back to original sources to build confidence in decisions

What we did​

We ran a time‑boxed engagement over two months to prove value and shape an implementation path:

  • Mobilization: Defined scope, data sources, success criteria, and stakeholders
  • Start‑up: Secured access and agreements, configured the AI service, scheduled working sessions
  • Use: Onboarded users, provided training and support, collected feedback, and enhanced capabilities between rounds. In total, thousands of documents from several different sources were loaded for analysis
  • Evaluation: Consolidated findings and feedback, and documented outcomes and next steps

The work combined subject‑matter expertise with AI‑supported analysis to map the documentation landscape and surface where terminology, references, roles, and responsibilities align or diverge. All findings were kept traceable to original documents to support review and governance.

Results​

Despite a tight timeline and shifting project context, the engagement demonstrated clear potential to improve quality and speed while reducing risk:

  • Identified redundant content and duplications across documents
  • Detected circular references and inconsistent terminology usage
  • Clarified roles, responsibilities, and ownership to support governance
  • Highlighted documents suitable for consolidation or retirement
  • Enabled version diffs, change tracking, and reference checking to expose gaps and missing links

Expected benefits for policy and governance functions include:

  • Reduced effort and cycle time for drafting, updating, and reviewing documents
  • Higher transparency and availability of information (including 24/7 source‑linked Q&A)
  • Improved legal certainty through consolidated, consistent answers
  • Faster, safer change implementation with impact analysis across the corpus
  • Machine‑assisted first drafts for consolidated clauses, contract text, and guidance
  • Generation of concise, role‑appropriate summaries for decision‑makers and practitioners

How it works​

  • Document landscape mapping: Build a navigable view of how documents relate and reference each other
  • Semantic and structural analysis: Detect redundant or conflicting passages, circular references, and definition drift
  • Governance graph: Make ownership, roles, and responsibilities visible across the corpus
  • High‑trust assistants: Allow experts to query the corpus with source‑linked answers instead of opaque summaries

Governance impact​

  • Change with confidence: See where a proposed change propagates and who needs to be involved
  • Traceability by design: Every insight links back to specific passages in source documents
  • Consistent definitions: Surface where terms diverge and align language across the estate
  • Lifecycle control: Make ownership, review cycles, and retirement candidates explicit
  • Audit readiness: Exportable, explainable evidence of how conclusions were reached