The missing link to scalable agentic AI

• 80-90%
  Of enterprise data is unstructured
• 6
  Stages to bridge the gap
• 100%
  Governance & compliance

Why unstructured data is your biggest blind spot

The enterprise nervous system runs on structured communications. Here’s why it matters and why it’s holding back your AI transformation.

• 80-90%
  of new data is unstructured.

  The Read Bottleneck. Most enterprises have invested heavily in structured data – warehouses, lakes, master data management. Yet the majority of new data growth comes from unstructured sources.
• Weeks
  before issues surface

  Early Warning Signals Lost. Goals, risks, and decisions emerge in conversations – often weeks before they appear in dashboards. Without structure, this data remains invisible to analytics and AI.
• 0%
  Governance on unstructured data

  Governance Gap. Emails, chats, calls, and documents lack lineage and governance. This creates compliance risks and prevents AI agents from making reliable, auditable decisions.

Organisations cannot scale generative AI without addressing the challenges of unstructured data. Structured systems alone aren’t sufficient to support AI agents that depend on real-time, governed context.

McKinsey on Scaling GenAI

The solution. Six-stage architecture to close the gap

Transform unstructured communications into structured, traceable, reusable assets that power reliable AI decision-making.

• 
  Ingest

  Securely pull emails, chats, calls, and documents. Apply transcriptions, OCR, and segmentation to create meaningful units of content.
• 
  Enrich

  Extract entities, events, and relationships. Map them into a business ontology—Goals Initiatives Issues Actions—with full traceability.
• 
  Catalog

  Generate metadata automatically using GenAI to tag sensitivity, participants, timelines, and topics. Maintain full lineage.
• 
  Secure Storage

  Store structured records with role- and snippet-level permissions, ensuring privacy and compliance by design.
• 
  Retrieve / RAG

  Enable policy-aware retrieval for analytics, dashboards, and AI agents. Guardrails persist across every stage.
• 
  Consume

  Deliver governed outputs as data products: Alignment Drift indicators, Emerging Risk alerts, Customer Issue heatmaps.

The Critical Difference

This architecture ensures that once data enters the pipeline, it remains secure, structured, and reusable for many future use cases. Every stage maintains governance and lineage—creating a foundation for reliable agentic AI.

Why this matters for agentic AI

• 
  Deterministic Context

  Securely pull emails, chats, calls, and documents. Apply transcriptions, OCR, and segmentation to create meaningful units of content.
• 
  Policy-Aware Autonomy

  Extract entities, events, and relationships. Map them into a business ontology—Goals Initiatives Issues Actions—with full traceability.
• 
  Reusable Foundation

  Generate metadata automatically using GenAI to tag sensitivity, participants, timelines, and topics. Maintain full lineage.

Move from Pilot Mode to Production Scale

Avoid the extremes McKinsey warns about: stuck in pilots or expanding into uncontrolled sprawl

• 
  Eliminate AI hallucinations from ungoverned data
• 
  Maintain compliance and auditability across all AI operations
• 
  Scale from pilot to production without architectural rewrites
• 
  Detect risks and opportunities weeks before they hit KPIs
• 
  Enable real-time decision intelligence across the enterprise