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Extracta Labs

Extracta.ai Review: AI Resume Parser & Custom Field Extraction Tested (2026)

Tested Hands-OnResume ParserPDF ParsingJSON OutputCustom SchemaStructured Data ExtractionLast verified May 2026

Our take

Extracta.ai is the most precise and predictable custom field extraction tool tested across all input types. It handles clean, multi-column, and messy resume formats without any manual layout configuration, returning clean minimal JSON with exactly the fields you define — no metadata noise, no taxonomy IDs, no unwanted extras. Best choice for developers and teams who know exactly which fields they need and want a lean, schema-driven output they can plug directly into their pipeline. Free tier available with 50 pages and no credit card required.

In-Depth Review

Our detailed analysis of Extracta Labs — features, performance, and real-world testing.

Rugved

AI Demos Team

Verified Review

Feature-by-Feature Breakdown

We tested each feature individually. Click any card to see inputs, outputs, and our observations.

Clean Resume Parsing

Very Good — lean clean JSON with only defined fields, no metadata noise

8/10

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Test Summary

Feature tested: Clean Resume Parsing

Result: Passed (8/10) — Very Good — lean clean JSON with only defined fields, no metadata noise

Extracta.ai accepts a standard single-column PDF resume and automatically extracts all fields defined in the extraction schema including name, email, phone, address, work experience with descriptions, education with grade details, certifications, and a full skills array. Output is lean clean JSON with no extra metadata or taxonomy data — only the values you asked for.

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Bottom Line

Multi-column parsing works excellently. All fields from both columns extracted correctly including the spoken languages (English, Hindi, Marathi) from the right sidebar — correctly identified from the dedicated LANGUAGES section. All 12 skills from the sidebar returned as individual array items. Main weaknesses are LinkedIn URL not extracted (not in schema), key projects not returned (not in schema), and the job title headline not captured. Fields not defined in the schema are never returned regardless of how prominent they are in the resume.

Multi-Column Resume Parsing

Excellent — both columns parsed correctly with no layout hints needed

9/10

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Test Summary

Feature tested: Multi-Column Resume Parsing

Result: Passed (9/10) — Excellent — both columns parsed correctly with no layout hints needed

Extracta.ai handles multi-column PDF layouts automatically without any column mapping or layout hints. Both the left column and right sidebar column are parsed correctly and all defined fields are returned in a single clean JSON output.

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Bottom Line

performs well on messy resumes. All 3 education levels extracted correctly (B.E., 12th, 10th), both certifications captured, and all 14 skills including soft skills returned as individual array items — the most complete skills extraction of all tools tested on the messy input. Main weaknesses are percentage marks for education returned as raw inconsistent strings ("67%", "72 percent marks", "passed 81%") without normalisation, and fields not in the schema such as objective, hobbies, and languages are not returned at all.

Messy Resume Parsing

Strong — all 14 skills including soft skills captured, most complete skills extraction tested

8/10

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Test Summary

Feature tested: Messy Resume Parsing

Result: Passed (8/10) — Strong — all 14 skills including soft skills captured, most complete skills extraction tested

Extracta.ai handles non-standard resume formatting including inconsistent date formats, flat comma-separated skill lists, missing section headers, and mixed percentage notation. All 3 education entries extracted correctly, both certifications captured, and full skills list including soft skills returned as a clean array.

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Bottom Line

Extracta.ai performs well on messy resumes. All 3 education levels extracted correctly (B.E., 12th, 10th), both certifications captured, and all 14 skills including soft skills returned as individual array items — the most complete skills extraction of all tools tested on the messy input. Main weaknesses are percentage marks for education returned as raw inconsistent strings ("67%", "72 percent marks", "passed 81%") without normalisation, and fields not in the schema such as objective, hobbies, and languages are not returned at all.

Frequently Asked Questions

Pricing & Access

TESTED

Free

50 free pages, no credit card required — sufficient for initial evaluation and testing across multiple resume inputs

Starter

$9/mo

500 pages per month, all extraction features, custom schema support, JSON export

Growth

$29/mo

2,000 pages per month, priority processing, batch uploads, API access

Business

$79/mo

10,000 pages per month, dedicated support, advanced schema options, full API integration

Pricing checked May 2026. We re-check quarterly. Visit extracta.ai for current plans and annual pricing options.

Is This Right For You?

A side-by-side guide based on our hands-on testing.

✓ Use This If

●You know exactly which fields you need and want only those fields returned — nothing extra

●You want the cleanest, leanest JSON output with no taxonomy metadata, no noise, and no extra fields

●You are building a resume ingestion pipeline and want a predictable, schema-driven output every time

●You need strong multi-column layout handling without any manual configuration

●You want 50 free pages with no credit card to evaluate before committing

✕ Skip This If

●You need fields like LinkedIn URL, professional summary, or projects extracted automatically without defining them upfront — Extracta.ai will not return them unless they are in your schema

●You need CGPA or grade scores as a clean standalone numeric field — Extracta.ai embeds them in a description string

●You are doing exploratory parsing where you want everything the resume contains — Affinda or LlamaParse would serve better

●You need certifications returned as structured objects with name, issuer, and year as separate fields — Extracta.ai returns them as raw strings

●You need the objective, hobbies, or references fields from messy resumes — not returned unless defined in the schema

Use Case Track Record

Parse resumes into structured data using an API

Strong — clean structured JSON output, good field coverage across all resume formats, custom schema support via natural language

Featured in Rankings

Independent rankings where Extracta Labs was tested and rated.

RankingsUsable

Best AI Tools for Parsing Resumes via API (2026)

This ranking evaluates AI resume parsing APIs based on their ability to...

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Yes. Extracta.ai parsed a two-column resume layout with a sidebar correctly on first upload with no manual configuration. Both the main column and sidebar content including education, certifications, skills, and spoken languages were extracted and returned in a single clean JSON output.

No. You define the extraction schema once and it applies automatically to every subsequent upload. There is no per-file manual work after the initial schema setup. The schema can be reused across any number of resume files.

No. Extracta.ai only returns fields that are explicitly defined in the schema. If LinkedIn URL, professional summary, or projects are not added as schema fields, they will not appear in the output regardless of whether they are present in the resume. This is a deliberate design choice — it gives you full control over what is returned.

Yes, when skills are defined as a schema field. In testing, all 14 skills including soft skills (good communication, team player, fast learner, problem solving) were returned as individual array items from the messy resume input — one of the most complete skills extractions across all tools tested.

Partially. Extracta.ai captures the CGPA value but embeds it inside a description string (for example "Graduated with CGPA: 8.2 / 10") rather than returning it as a clean standalone numeric field. The value is present and accessible but requires string parsing downstream to extract the number.