TalentGraph parses resumes and job descriptions into clean structured data, ranks your talent pool against any role, and drafts a full job description from a single sentence. A cached, self-learning local engine does most of the work — the AI model is only called when it is genuinely needed.
No credit card for the free tier · Your documents are never used to train a model.
Every tool shares the same extraction engine, occupation taxonomy and usage stats, so results line up across resumes and roles.
Contact details, skills, experience, education and projects out of a PDF, DOCX or TXT — with tenure and seniority derived automatically.
Open Resume Parser →Any posting becomes title, company, employment type, compensation, required and preferred skills, normalized against the O*NET taxonomy.
Open Job Parser →Drop in a requirement and get your top candidates ranked, each with a match score and the exact skills they have and lack.
Open Talent Match →One sentence in, a complete job description out — responsibilities, skills, nice-to-haves, education and screening questions.
Open JD Generator →Score a resume against the job it is aimed at and see the keywords an applicant tracking system will miss.
Open Resume ↔ JD →Every ATS issue anchored to the line that caused it, beside an AI rewrite that is checked against the original before it is offered.
Open Resume Builder →A structured first-round screen that asks role-specific questions and scores the answers consistently.
Open AI Interviewer →Each document takes the fastest path that will answer it, and only escalates when local confidence is low.
Seen this exact text before? The structured result comes back instantly, with zero AI calls.
Regex plus a learned skills gazetteer and section-heading synonyms extract most fields with no API call at all.
Only when local confidence drops below threshold does it call Gemini, OpenAI or Anthropic to fill the gaps.
Every AI answer teaches the local engine new skills and headings, so the next batch of documents needs the model less.
The same extraction that powers matching, scoring and search — and the same JSON you can post straight into your ATS.
PRIYA RAGHAVAN priya.r@example.com | +91 98200 11223 | Pune EXPERIENCE Senior Backend Engineer, Northwind Labs Mar 2021 - Present - Rebuilt billing service on Python/FastAPI - Cut p99 latency 840ms -> 120ms with Redis - Mentored 3 juniors Backend Engineer, Cobalt Systems Jul 2018 - Feb 2021 SKILLS Python, FastAPI, PostgreSQL, Redis, Docker, AWS
{
"name": "Priya Raghavan",
"email": "priya.r@example.com",
"phone": "+91 98200 11223",
"location": "Pune",
"total_experience_years": 7.2,
"seniority": "senior",
"current_title": "Senior Backend Engineer",
"skills": [
"Python", "FastAPI", "PostgreSQL",
"Redis", "Docker", "AWS"
],
"experience": [
{ "company": "Northwind Labs",
"title": "Senior Backend Engineer",
"start": "2021-03", "end": null,
"is_current": true }
]
}The same pipeline, pointed at four different problems.
Shortlist a req in minutes instead of an evening of CV-reading.
See how →Turn a decade of unsearchable CVs into a queryable talent pool.
See how →Consistent, structured hiring data across every requisition.
See how →Show a client exactly what an ATS sees when it reads their CV.
See how →Quotes from teams running TalentGraph on their own req load.
We were reading 300 CVs a week by hand. Now the shortlist is waiting for us on Monday morning and we spend the time on calls instead.
The part that sold us was the missing-skills column. It turns a rejection into something you can actually explain to a hiring manager.
Our old parser broke on every two-column resume. This one just handles them, and the JSON drops straight into our ATS.
Resumes are personal data. The architecture reflects that: documents are processed on your own infrastructure first, and never used to train a model.
Badges marked planned are on the roadmap and not yet certified. Read the security overview →
Start on the free tier with no card, or walk through your own req load with us on a call.