Cofactr (YC W’22) is a source-to-pay and 3rd-party-logistics platform for critical hardware manufacturers. Their platform connects engineering, procurement, and logistics teams through a unified solution, ensuring manufacturers get exactly what they need from BOM to build. Notable customers include Amazon's robotics division, which uses Cofactr to procure thousands of components for warehouse robots.

 The Brooklyn-based startup has raised $28.8M from investors including Bain Capital, Y Combinator, Floating Point Ventures, Broom and DNX, growing to serve 80+ customers across 5,000+ suppliers in aerospace, defense, medical devices, robotics, and automotive industries.

The Challenge: Processing Diverse Documents at Scale

This variability made consistent parsing difficult for standard LLM-based approaches and raised the bar for accuracy. Operating in regulated, mission-critical industries, Cofactr requires near-perfect extraction fidelity to support traceability, compliance, and downstream automation.

Over four years, Cofactr evaluated multiple approaches. Manual processing was accurate but slow and labor-intensive. AWS Textract with custom post-processing helped early on, but struggled with document variety and complex tables. A patchwork of tools like Tabula and custom Python scripts introduced brittle workflows that were difficult to scale or maintain.

The Solution: Structured, Scalable Automation with Datalab

Cofactr discovered Datalab's Marker technology and implemented a sophisticated two-pipeline architecture:

1. Time-sensitive documents flow through a high-speed pipeline, where structured outputs are passed directly into Cofactr’s LLM engine for rapid turnaround.
2. An asynchronous document AI pipeline ingests files from FTP, SharePoint, and email, processing them with Datalab’s advanced OCR and document understanding capabilities.

Rather than treating extraction as an isolated step, Datalab’s structured outputs became a first-class input to Cofactr’s workflow management system.

• Database Normalization: All document outputs are normalized into relational database schemas (quotes → quote schema, invoices → invoice schema, etc.)
• Quality Control: Programmatic heuristics compare expected vs. actual line counts, validate subtotals, and cross-reference documents
• Supply Chain Traceability: Maintains complete traceability for quality issues, tracking which supplier/batch corresponds to specific product serial numbers
• Workflow Automation: Integrated with purchase order generation, demand planning, and production scheduling systems

Impact: From Bottleneck to Competitive Edge

By automating document processing end to end, Cofactr eliminated manual bottlenecks while improving both accuracy and reliability across a wide range of document qualities. Engineering overhead was reduced as non-technical team members gained the ability to adjust prompts and classification logic without deep code changes. Most importantly, the system scaled with growing document volumes without requiring proportional increases in staff.

Cofactr represents the evolution of modern supply chain management, where automated document processing is critical for serving mission-critical industries. Through their partnership with Datalab, document processing shifted from an operational constraint into a durable competitive advantage—enabling faster turnaround times, higher accuracy, and a platform that scales seamlessly as Cofactr expands its customer base and processing demands in the critical hardware supply chain.