The Challenge
A lot of critical business information in manufacturing lives inside unstructured documents: invoices, supplier BOQs, specification sheets, compliance reports, scanned PDFs, or even handwritten notes. Teams spend hours copying values into spreadsheets or ERP systems just to make the data usable. This manual effort is slow, inconsistent, and prone to human error. It also means valuable insights are locked inside files that are hard to search, compare, or analyze.
The Solution with SIA
SIA’s Field Extraction Agent converts unstructured documents into structured, system-ready data. It works across varied formats such as scans and PDFs, without the need for fixed templates or predefined schemas.
Unlike rigid tools where every new format requires configuration, SIA keeps it simple: just tell it once which fields matter and those key value pairs will be extracted and output is a structured table for you to use in your downstream workflows. Teams save time, avoid schema-building effort, and can focus directly on using the data for decision-making. Some of the most common use cases are
- Invoices → Extract vendor details, line items, taxes, and totals into a clean table for faster finance processing
- Supplier BOQs and Specifications → Pull out material properties, dimensions, and compliance values for side-by-side comparison
- Compliance Reports → Capture key clauses and thresholds to validate against internal or regulatory standards
Impact You Can Measure
- Faster turnaround for data-heavy tasks
- Reduced manual errors and missed details
- Lower processing costs and rework
- Unlocking insights from documents that were previously siloed
Real-World Example
In the FMCG sector, finance teams process thousands of supplier invoices each month, while procurement teams review lengthy specification sheets before finalizing vendors. Extracting invoice fields, material grades, and compliance values into structured data allows these teams to save time, reduce errors, and accelerate decision-making.
FAQs
Manufacturers deal with a wide range of files: invoices, supplier BOQs, technical specification sheets, compliance reports, quality certificates, scanned PDFs, and even handwritten notes. All of these can be converted into structured tables that can be analyzed, compared, or exported to Excel and CSV.
OCR captures raw text from scanned images or PDFs but does not understand the meaning or organize it into usable fields. Data extraction goes further, it identifies key-value pairs such as invoice number, material grade, or compliance threshold, and structures them for direct use in sourcing, finance, or quality workflows.
No. This is one of the biggest advantages. Instead of spending weeks creating templates, you simply define once which fields matter. The system then extracts those fields across varied document formats. This eliminates schema-building effort and makes onboarding new suppliers faster.
Yes. Large files like technical spec sheets or tenders with annexures can be processed in full. Tables, footnotes, and supporting documents are read and structured so no critical parameter is overlooked.
Accuracy depends on document quality, but modern extraction methods achieve very high precision. However, to ensure no wrong data gets captured, in case of low confidence, those values are highlighted for human review.
If a field cannot be extracted with high confidence, it is flagged for review. Teams can correct and approve exceptions before the data is finalized, reducing risk of errors entering downstream systems.
Yes. For example, if a supplier spec sheet omits a required compliance value, the system will flag the gap. This ensures teams do not approve incomplete submissions.
No. Teams can start by exporting structured data into Excel or CSV for manual upload. Integration with ERP, PLM, or MES systems can be added later if desired.
Both. While key-value fields are easiest to structure, free-text descriptions (e.g., “material must withstand 250°C for 2 hours”) can also be extracted, tagged, and compared using the comparison tool provided by SIA.