If your procurement team is sitting on mountains of PDFs, invoices, and contracts that never quite make it into your systems properly, you’re not alone. In fact, you’re in the majority.
Here’s the uncomfortable truth: most procurement organisations have spent years accumulating valuable data that’s essentially locked away in unstructured formats. Every invoice that gets filed without proper extraction, every contract sitting in someone’s inbox, every supplier document that requires manual data entry – these represent missed opportunities for savings, duplicate payments waiting to happen, and strategic insights that never surface.
This isn’t a failure of effort or ambition. It’s a reflection of how procurement data is created, stored, and passed through systems over time. The good news is that it’s also very fixable. We like to classify it as a structural challenge that modern technology can solve.
Why Strong Data Foundations Matter
Before looking at the solutions in the market, it’s worth stepping back and understanding why getting procurement data foundations right has become increasingly non-negotiable. As procurement teams are asked to play a more strategic role, decisions are only as good as the data behind them. When spend information is fragmented across PDFs, invoices, and manually maintained spreadsheets, visibility is limited and results suffer.
Strong data foundations change this. When procurement data is structured, consistent, and machine-readable, teams can move away from reactive reporting and towards informed, forward-looking decision-making.
In practice, strong data foundations help procurement teams
- Support better strategic decision-making by improving visibility into supplier concentration, category spend patterns, and pricing benchmarks
- Prevent duplications and errors by identifying duplicate suppliers, pricing discrepancies, and missed early payment discounts before they impact the P&L
- Reduce manual effort spent cleansing, reclassifying, and reconciling data across systems
- Improve auditability and compliance by maintaining clear, consistent records for spend visibility and supplier documentation
At its core, investing in strong data foundations is about creating clarity, and using that information to smoothen operations and drive cost savings.
The Solution Landscape: OCR + LLM Document Intelligence
The technology that makes this possible combines Optical Character Recognition (OCR) with Large Language Models (LLMs). OCR handles the initial text extraction from documents but is only about 70% accurate on it’s own, whilst LLMs understand context, identify relevant fields, and structure the information intelligently – even when invoices and documents don’t follow consistent formats and brings the accuracy rate significantly higher.
Here’s how some of the leading solutions stack up:
| Solution | Best For | Key Strengths | Considerations |
|---|---|---|---|
| Anvil Analytical’s AI Extract |
High-volume document processing with advanced classification needs | Optimised for returning clean data across global suppliers and can connect into your ERP system; strong focus on classification, translations, and duplicate removal; competitive pricing | Primary focus is data quality and analytics rather than payment processing workflows |
| Moss | Organisations wanting integrated payment automation | Solid extraction capabilities with built-in payment processing; can automatically execute payment runs directly from the platform |
More payment-operations focused; may be overkill if you primarily need data for analysis rather than payment automation |
| Rossum | High-volume invoice processing with complex validation needs | Strong at handling invoice variations across global suppliers; strong validation rules engine; good API for system integration | Can require more setup time for complex validation scenarios; pricing scales with volume and can be high price |
| Docsumo | Teams wanting straightforward document extraction without heavy configuration | User-friendly interface; quick setup for common document types; flexible enough for various procurement documents beyond invoices, competitive pricing | May need more manual review for highly variable document formats; less specialised for procurement-specific analytics |
Which Approach Makes Sense for Your Team?
The right choice depends on what you’re trying to achieve, and the reality is that you don’t need to fix everything at once. Start with your highest- value documents – typically invoices and contracts – and focus on the fields that matter most for your immediate needs.
The teams achieving quick wins typically:
- Begin with a single category or supplier set to prove the concept
- Define clear success metrics (time saved, errors cau
ght, savings identified) - Use these initial wins to build momentum for broader adoption
- Integrate gradually rather than attempting a wholesale system replacement
Don’t be too hard on yourself if your procurement data feels messy. In most cases, it’s simply the result of limited capacity to manually digitise large volumes of documents, combined with the fact that the right tools haven’t always been available. But with modern OCR and LLM capabilities, the gap between your unstructured document reality and your structured data needs is finally closable. The question isn’t whether to address it, but how quickly you can start turning those dormant PDFs into actionable insights.
