Reduces document compliance verification time by 85%, transforming unstructured reports into structured, fully auditable compliance findings using advanced Knowledge Graphs.
The Business Challenge
Ensuring regulatory, financial, and environmental compliance across diverse, unstructured corporate disclosures is a highly resource-intensive bottleneck.
Compliance, audit, and ESG teams must manually cross-reference hundreds of pages of complex annual reports or project documentations against rigid, ever-evolving frameworks—from accounting standards (IFRS) and financial disclosures to sustainability certifications (such as BREEAM). A single comprehensive review typically requires 2.5 to 3.5 hours of manual, repetitive tracing.
Baseline vector-search (RAG) approaches often fail here, as they struggle with multi-page synthesis, relational entity mapping, and complex multi-hop regulatory logic across large document sets.
Industry
- Financials
- ESG Governance
- Regulatory Compliance (GRC)
Maturity
Production
Capabilities
- Enterprise Knowledge Graphs (GraphRAG)
- Agentic Multi-Hop Search
- Automated Compliance Auditing
- Semantic Citation Grounding
What We Delivered
We engineered an enterprise-grade compliance engine powered by a hybrid GraphRAG architecture that automates checklist validation across unstructured corporate documentation.
Users can upload custom audit checklists along with their supporting reports, after which the system performs:
- Dynamic Knowledge Graph Construction
- Agentic Multi-Hop Search
- Deterministic Grounded Reasoning
- Semantic Citation Mapping
Dynamic Knowledge Graph Construction
Automatically extracts and maps entities, financial values, and structural relations from complex documents into a semantic Knowledge Graph, capturing relational context that standard vector databases miss.
Agentic Multi-Hop Search
Utilizes autonomous AI agents to plan, reason, and execute multi-stage semantic queries across the Knowledge Graph and document index, tracing complex relationships across different sections of the report.
Deterministic Grounded Reasoning
Evaluates checklist criteria (IFRS, financial disclosures, and green building/sustainability standards like BREEAM) generating explainable answers with strict source context bounding to eliminate hallucinations.
Semantic Citation Mapping
Pinpoints, highlights, and maps the exact passages within the source documents directly to the checklist items for rapid, auditable validation.
Engineered with strict enterprise-grade security guardrails, the solution integrates into existing workflows, functioning as a highly reliable decision-support system with human-in-the-loop oversight.
Key Outcomes
Cost-Benefit Estimation
€58K
savings / year
3.7 mo
payback period
800 h
hours reclaimed
Snet = Ndocs × (Tmanual − Tassisted) × Chr − Cplatform
Ndocs = reviews/year | Tmanual = manual review time (h) | Tassisted = AI-assisted time (h) | Chr = fully loaded hourly rate | Cplatform = annual platform cost
Example — Mid-sized EU financial services group, 4-person compliance team, 320 reviews/year
| Manual review cost | 320 × 3.0h × €95 | = €91,200 |
| AI-assisted cost | 320 × 0.5h × €95 | = €15,200 |
| Platform cost | − €18,000 | |
| Net annual savings | €58,000 |
Illustrative estimate. Actual savings depend on document complexity, team structure, and deployment model. Platform cost assumes Azure-hosted deployment with consumption-based LLM pricing.
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