Eagle doc

@Eagle doc extract all candidate details from this resume PDF including work history, education, and skills

The resume OCR returned structured data for the candidate: 5 years of software engineering experience at two companies (2019-2024), Bachelor's degree in Computer Science from State University (2015-2019), technical skills including Python, React, and PostgreSQL, plus AWS Solutions Architect certification obtained in 2022. The extraction also captured contact information and a brief professional summary. This data is now available in JSON format for further processing or database insertion.

This example demonstrates the resume parsing capability, which is useful for HR workflows and applicant tracking. The tool requires an API key with sufficient quota allocation. Note that OCR accuracy depends on resume formatting—highly stylized or image-heavy resumes may require manual review of extracted fields.

@Eagle doc process this invoice image and extract vendor name, total amount, line items, and payment terms

Invoice OCR completed: Vendor is Acme Office Supplies, invoice #INV-2024-0847 dated March 15, 2024. Total amount is $3,247.89 with payment terms Net 30. Extracted 12 line items including office furniture ($1,850), printer supplies ($890), and miscellaneous items. Tax amount of $247.89 was identified separately. The structured output includes item descriptions, quantities, unit prices, and category codes suitable for accounting system import.

This write-like example shows invoice data extraction, a common accounts payable automation use case. The tool accepts base64-encoded images or file uploads. Be aware that processing counts against your monthly page quota—check usage with the quota tools before batch operations to avoid over-usage charges.

@Eagle doc check my current month usage and tell me if I have enough quota remaining to process 500 more receipt images

Current month usage shows 2,340 pages processed out of your 5,000-page contractual quota, leaving 2,660 pages available. At your current usage rate, you have sufficient capacity for 500 additional receipt images (assuming single-page receipts). Total cost so far is $117.00 with no over-usage charges. Based on this month's trend, you're projected to use approximately 3,800 pages by month-end, staying well within your allocation.

This synthesis example pairs the quota management tool with AI reasoning to answer a practical planning question. It's essential for teams running batch OCR jobs to monitor quota proactively—over-usage incurs additional per-page charges. The MCP provides both current-month and historical usage data for forecasting and cost control.

When Eagle Doc wins for monthly vendor invoice runs

A 6-person finance team processes 200-400 vendor invoices per month, mostly PDFs and scanned images. Eagle Doc's invoice OCR tool extracts line items, totals, and vendor details into structured JSON, which feeds directly into their accounting system. The API key auth is simple enough for a single shared credential in Switchy, and the usage quota tools let the team track monthly page counts against their contract limit. This works until invoice volume hits 1,000+ pages per month—at that scale, the per-page cost adds up and you need to compare Eagle Doc's pricing against bulk-discount OCR providers. If your invoice flow is under 500 pages and you need structured extraction (not just text), Eagle Doc is the right call.

When Eagle Doc fits early-stage hiring pipelines

A 3-person startup hiring team receives 50-80 resumes per open role, mostly PDFs and Word docs. Eagle Doc's resume extraction tool pulls work history, education, and skills into a structured format, which the team drops into a shared Airtable or Notion database for first-pass screening. The batch processing tools let them queue up a folder of resumes overnight and review parsed data the next morning. This setup breaks down when the team scales past 10 hires per quarter—at that point, the manual queue management and lack of ATS integration become friction points, and you're better off with a dedicated recruiting platform that includes OCR. For early-stage teams doing ad-hoc resume parsing, Eagle Doc keeps the workflow simple.

When Eagle Doc's receipt OCR is overkill for small teams

A 4-person consulting team submits 20-30 expense receipts per month, mostly photos from phones. Eagle Doc's receipt OCR tools extract merchant names, totals, and dates, but the team already uses Expensify or Ramp, which have built-in receipt capture. The only scenario where Eagle Doc makes sense here is if you're building a custom expense workflow in Airtable or a homegrown system and need OCR as a standalone API. The deprecated v1 and v2 receipt quota tools suggest Eagle Doc is phasing out this use case in favor of invoice and document OCR. Unless you're committed to a custom expense stack, skip Eagle Doc for receipts and use your expense platform's native capture.