RFQ OCR Extraction

[ RFQ OCR Extraction ]

Automate RFQ OCR Extraction to Deliver Accurate Quotes Faster

Use LlamaParse to extract line items and terms from messy RFQs with verifiable accuracy.

Extract RFQ Line Items into Structured JSON

LlamaParse turns messy RFQs into clean, line-item JSON you can trust, capturing part numbers, quantities, pricing, and delivery terms across layouts. Agentic document parsing uses layout-aware vision and validation loops to reduce rework, with citations and confidence scores for fast human review.

Best-in-Class Accuracy

RFQ OCR Extraction Across Industries

Manufacturing Procurement & Supply Chain

Use LlamaParse to turn messy supplier RFQs—PDFs with multi-column specs, tolerance tables, and line-item pricing—into clean JSON that maps directly into your ERP and sourcing templates. Layout-aware table extraction and validation loops reduce rework from scrambled part numbers and units, so buyers can compare quotes faster and award with confidence.

Construction & Commercial Contracting

Parse subcontractor and materials RFQs that include scope tables, alternates, and schedule requirements into structured Markdown/JSON for bid leveling and estimating systems. Natural-language parsing instructions let teams reliably capture exclusions, lead times, and compliance notes from inconsistent formats without writing brittle rules.

Oil, Gas & Energy Services

Extract equipment, service-rate, and mobilization details from RFQs that embed spec sheets, diagrams, and scanned field documents, preserving reading order and critical table context. Tier-based agentic processing routes only the complex pages to heavier models, keeping per-RFQ costs predictable while improving straight-through processing for high-volume tenders.

B2B Startups Building Procurement Automation

Ship RFQ ingestion in days by using LlamaParse as the document processing layer, returning AI-ready structured outputs with citations and confidence scores for review workflows. JSON mode plus granular metadata makes it straightforward to build searchable quote repositories and automated quote-comparison experiences without spending months on extraction edge cases.

The Solution

RFQ OCR Extraction That Captures Tables, Specs, and Key Fields—Accurately

01

Layout-Aware RFQ Tables

LlamaParse detects page structure and reliably extracts multi-column sections and nested RFQ tables without scrambling line items. This keeps part numbers, quantities, units, and terms aligned so downstream pricing and quoting logic doesn’t break.

02

Multimodal Spec Capture

LlamaParse reads diagrams, charts, and embedded images that often carry critical RFQ requirements, not just plain text blocks. That means you can pull tolerances, performance curves, and annotated drawings into AI-ready text instead of losing context in a “text-only” pass.

03

Prompted Field Extraction

You can give LlamaParse natural-language instructions to extract exactly what an RFQ needs—ship-to, incoterms, due dates, approved manufacturers, and compliance notes—without writing brittle regex pipelines. This makes it easy to standardize outputs across wildly different customer templates.

04

JSON Output With Citations

LlamaParse can return structured JSON plus granular metadata like page references and element-level traceability for each extracted field. For RFQ intake, this enables quick QA, confidence-based routing to human review, and audit-ready links back to the source document.

Technical OCR documentation

Agentic OCR, documented for builders.

Explore our developer guides to easily connect your document pipelines to LlamaParse.

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Eliminate Human Error

Our AI catches the typos that tired eyes miss.

Format Flexibility

Export to Excel, JSON, XML, or directly via API.

Enterprise-Grade Security

SOC2 Type II compliant with end-to-end encryption.

No-Code Templates

Train the tool on your specific forms in minutes, not days.

Lightning Speed

Average processing time of <3 seconds per page.

LlamaParse’s support of a wide variety of filetypes and its accuracy of parsing made it the best tool we tested in our evaluations. The LlamaIndex team was very responsive and we were off to the races within a day.

Turn data chaos into data clarity.

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Common FAQs

How Does it Work?

01

Will it keep RFQ tables intact, or will my line items get scrambled?

It’s layout-aware, so multi-column sections and nested tables are extracted without mixing rows or shifting columns. Part numbers, quantities, units, and terms stay aligned, which helps your pricing and quoting workflows run reliably.

02

Can it capture requirements that are only shown in diagrams, charts, or embedded images?

Yes—multimodal spec capture reads more than plain text, including charts, annotated drawings, and image-based callouts. You get key details like tolerances and performance curves converted into AI-ready text so you don’t miss critical requirements.

03

How do we extract specific RFQ fields like ship-to, incoterms, due dates, and compliance notes?

You can use natural-language prompts to pull exactly the fields you care about, even when customers use different templates. This avoids brittle regex rules and helps you standardize outputs across suppliers and regions.

04

What does the output look like, and can we tie each value back to the source for QA?

Outputs can be delivered as structured JSON, with citations like page references and element-level traceability. That makes it easy to review questionable fields, route low-confidence items to a human, and keep an audit trail.

05

How accurate is it across different customer RFQ templates and formats?

Because it uses page structure plus prompted extraction, it adapts well to varied layouts, wording, and document styles. You can also refine prompts over time to improve consistency without rebuilding your pipeline.

06

How fast can we integrate this into our RFQ intake workflow?

You can start with JSON outputs that map directly into your quoting system or ERP intake step, then expand to additional fields as needed. The built-in citations simplify validation, so teams typically move from pilot to production faster with less manual rework.