Customer stories

What this looks like on a real req load.

Three sketches of how teams use TalentGraph day to day — an in-house talent team, a staffing agency working a client database, and an HR function standardising across departments.

This page is a template, not a claim.TalentGraph has not published approved customer stories yet, so the three cards below are clearly-marked examples showing the shape a real story will take. Replace each one with a named, approved account — or delete the section — before this page is linked from a live campaign. Nothing here should be quoted as a reference.

From a Monday of CV-reading to a Monday of calls

A five-person talent team handling roughly forty open engineering requisitions parses every inbound application automatically. Talent Match ranks the pool per req, and the recruiters start their week with a scored shortlist and the reasons behind each position.

~5h
Recruiter hours saved weekly
2 days
Faster to first shortlist

Nine years of CVs, finally searchable

An archive of unstructured CVs is bulk-parsed into structured candidate records. New client briefs are pushed through the Job Parser and matched against people the agency already had on file, which turns redeployment from a memory exercise into a query.

~12k
Archived CVs structured
<1 min
Brief to shortlist

One shape of hiring data across every department

Hiring managers brief a role in a sentence; the JD Generator produces a consistently structured description and the Job Parser normalizes the title. Every candidate is then scored against the same criteria, so quarterly hiring reporting stops being archaeology.

100%
Reqs with structured JDs
1
Scoring standard, not six

Would you talk about your own?

If TalentGraph is working for your team, we would rather publish your numbers than our adjectives. We will write it, you approve every word, and you can pull it at any time.