Purchaser.ai is revolutionizing manufacturing procurement with a platform that intelligently ingests, analyzes, and compares purchasing documents—from quotes and bills of materials (BOMs) to complex vendor spreadsheets. By combining deep domain expertise with thoughtful automation, they’ve turned a manual, error-prone workflow into a streamlined engine for faster, smarter purchasing decisions.

Based in Lexington, Kentucky, Purchaser.ai has raised $8 million from investors including Homebrew and Long Journey Ventures to bring structure and speed to one of the supply chain’s most chaotic processes.

Seeking a document intelligence solution that “doesn’t suck”

Manufacturing procurement operates in a world of messy, inconsistent documents that resist standardization. Purchaser.ai processes hundreds of quotes and BOMs weekly—many buried in thousand-row spreadsheets or cluttered PDFs with competing material specs and regional compliance variations.

“We see everything from DFARS-compliant aluminum quotes to scans from dot-matrix printers,” said Justin Burnette, co-founder and CTO. “And they all look different.”

Cost wasn't a constraint, and the team was willing to invest in a solution that worked. Their CEO even dedicated an entire month to training models in Azure and Google's Document AI, but despite these efforts, the output never achieved the production-level fidelity they required.

Achieving speed, savings, and unexpected stickiness with Datalab

While exploring alternatives, Purchaser.ai discovered Datalab on GitHub and integrated it almost immediately. Within days of discovering the tool, they team was able to: 

• Launch a breakthrough feature that drove up to 50% higher retention. Their “upload everything” feature lets users upload even blurry images and receive clean, structured output. “It feels like magic,” said Justin. “You do all the work, but we get the credit. It’s awesome.”
• Achieve instant accuracy without infrastructure management or model training, saving months in R&D time and freeing the team to focus on core platform development.
• Standardize all documents with high-quality outputs for downstream processing through LLMs or for vendor management workflows.
• Generate immeasurable cost savings - Azure’s pricing would have been $30 per 1,000 pages—not to mention the time cost of maintaining custom models. Datalab’s efficiency saved the team both time and money.

The results speak for themselves: “We tried training our own models, spent weeks on it, and still couldn’t match what Datalab did out of the box.”

Building for Trust, Compliance, and Scale

The partnership between Datalab and Purchaser.ai continues to evolve as they enhance their quote comparison capabilities and integrate coordinate-level metadata for improved auditability. This advancement will allow users to trace summaries back to original source locations, deepening user trust and enhancing procurement accuracy across the platform. 

Looking ahead, Purchaser.ai is preparing for CMMC certification, with growing document volume and increasingly varied formats, Datalab remains key to scaling speed and accuracy in procurement.