Automating Tax Due Diligence

An AI-powered solution to extract structured data from diverse commercial tax documents, enabling faster, more accurate financial due diligence for US, Netherlands, and Canada.

The Business Challenge

Manual review of complex tax filings from multiple jurisdictions is slow, costly, and error-prone. With high document volume and diversity, a scalable and accurate method was critical to streamline analysis and accelerate deal cycles.

70%
Reduction in Document Processing Time

Achieved through AI-powered automation, significantly improving overall efficiency.

Our Automated Solution Pipeline

We implemented a modular pipeline using Azure Document Intelligence to orchestrate the entire data extraction process from ingestion to final reporting.

Ingest
Classify Documents
Extract Attributes
Normalize Data
Generate Report

Processing Time: Manual vs. AI

The AI solution drastically cuts down on processing overhead, freeing up expert time for value-added analysis.

Cross-Jurisdiction Capability

Our solution was trained to handle distinct document formats across three key geographies, including both PDF and HTML sources.

Key Benefits & Outcomes

The project delivered significant value across technical and business domains.

Business Impact

  • Increased Efficiency: Slashed document processing time by over 70%.
  • 🎯
    Improved Accuracy: Drastically reduced manual data entry errors.
  • ⏱️
    Faster Deal Cycles: Accelerated financial due diligence efforts.
  • 💰
    Cost Savings: Lowered operational costs tied to manual data review.

Technical Advantages

  • 🔄
    Cross-Format: Natively handles both PDF and HTML document processing.
  • 🧠
    Custom ML Models: Fine-grained control over data extraction from varied templates.
  • 📈
    Scalability: Ingests hundreds of documents in batch mode with low latency.
  • 🧩
    Extensibility: Easily adaptable for new countries or document types.

Challenges Overcome

Navigating complexity was key to the project's success.

Document Variability: Managed diverse layouts, formats, and noisy scans.
HTML Parsing: Built custom scrapers for semi-structured Dutch documents.
Model Training: Iteratively refined high-accuracy ML models.
Data Normalization: Harmonized data from different tax terminologies.
Language Nuances: Adapted models to domain-specific language.