ABOUT FAIRE HIRE AI
We Built The Hiring Platform We Wanted When We Were The Ones Screening.
Two recruiters and an ML engineer who spent years watching strong candidates disappear into the pile because nobody had time to read past application forty.
WHY WE EXIT
Hiring is the last major business decision still made on gut feel.
Every company says it hires the best person for the job. Almost none can show you how they decided. The evidence lives in a recruiter's head, a hiring manager's Slack message, and a scorecard filled in three days after the interview.
That isn't dishonesty it's volume. When four hundred people apply for one role, something has to give, and what gives is consistency. The first fifty applications get read carefully. The last hundred get a glance.
The fix isn't asking recruiters to work faster. It's giving them a system that reads everything with the same care, and shows its working.
So FairHire scores every applicant against the same rubric, attaches the evidence behind each score, and hands the decision back to a person. The AI does the reading. People still do the hiring.
BY THE NUMBERS
What Seven Years Of Fairer Hiring Adds Up To
Halden Group
Faster time-to-shortlist across engineering roles.
Halden Group
Faster time-to-shortlist across engineering roles.
Aperture Labs
More qualified candidates reaching final interview
Aperture Labs
More qualified candidates reaching final interview
Baseline Retail
Days to full rollout, ATS synced both ways
Baseline Retail
Days to full rollout, ATS synced both ways
Baseline Retail
Days to full rollout, ATS synced both ways
Baseline Retail
Days to full rollout, ATS synced both ways
WHAT WE BELIEVE
Four Positions We Won't Trade Away For Speed.
01
A score without evidence is just an opinion with a number on it.
Every rank FairHire produces opens into the exact lines it was drawn from. If we can't show the working, we don't ship the feature.
01
A score without evidence is just an opinion with a number on it.
Every rank FairHire produces opens into the exact lines it was drawn from. If we can't show the working, we don't ship the feature.
01
A score without evidence is just an opinion with a number on it.
Every rank FairHire produces opens into the exact lines it was drawn from. If we can't show the working, we don't ship the feature.
02
The machine ranks. A person decides.
FairHire never advances or rejects a candidate on its own. Automation belongs to the reading and sorting; judgement stays with the recruiter whose name is on the decision.
02
The machine ranks. A person decides.
FairHire never advances or rejects a candidate on its own. Automation belongs to the reading and sorting; judgement stays with the recruiter whose name is on the decision.
02
The machine ranks. A person decides.
FairHire never advances or rejects a candidate on its own. Automation belongs to the reading and sorting; judgement stays with the recruiter whose name is on the decision.
03
Fairness is measured, not claimed.
Pass-through rates are monitored at every stage while the role is still open. A fairness problem you find in an annual report is a fairness problem you found too late.
03
Fairness is measured, not claimed.
Pass-through rates are monitored at every stage while the role is still open. A fairness problem you find in an annual report is a fairness problem you found too late.
03
Fairness is measured, not claimed.
Pass-through rates are monitored at every stage while the role is still open. A fairness problem you find in an annual report is a fairness problem you found too late.
04
Candidate data is never the product.
No training on customer data, no cross-customer models, no selling signals to anyone. Retention windows are set by you and deletions are honoured on request.
04
Candidate data is never the product.
No training on customer data, no cross-customer models, no selling signals to anyone. Retention windows are set by you and deletions are honoured on request.
04
Candidate data is never the product.
No training on customer data, no cross-customer models, no selling signals to anyone. Retention windows are set by you and deletions are honoured on request.
OUR STORY
From A Spreadsheet To 150 Recruiting Teams.
2021
A rubric in a spreadsheet
Nadia and Samuel were hiring twelve engineers at once and built a shared scoring sheet to stop three interviewers grading the same answer three different ways. It worked. It also took four hours a day to maintain.
2021
A rubric in a spreadsheet
Nadia and Samuel were hiring twelve engineers at once and built a shared scoring sheet to stop three interviewers grading the same answer three different ways. It worked. It also took four hours a day to maintain.
2021
A rubric in a spreadsheet
Nadia and Samuel were hiring twelve engineers at once and built a shared scoring sheet to stop three interviewers grading the same answer three different ways. It worked. It also took four hours a day to maintain.
2022
First ten customers, all by referral
The spreadsheet became a product after two recruiting friends asked to borrow it. We spent nothing on marketing that year — every customer arrived through someone who had used it inside another company.
2022
First ten customers, all by referral
The spreadsheet became a product after two recruiting friends asked to borrow it. We spent nothing on marketing that year — every customer arrived through someone who had used it inside another company.
2022
First ten customers, all by referral
The spreadsheet became a product after two recruiting friends asked to borrow it. We spent nothing on marketing that year — every customer arrived through someone who had used it inside another company.
2023
Evidence trails ship
Hiring managers kept asking the same question: why is this candidate ranked above that one? We rebuilt scoring so every number opens into the exact lines it came from. It's still the feature customers name first.
2023
Evidence trails ship
Hiring managers kept asking the same question: why is this candidate ranked above that one? We rebuilt scoring so every number opens into the exact lines it came from. It's still the feature customers name first.
2023
Evidence trails ship
Hiring managers kept asking the same question: why is this candidate ranked above that one? We rebuilt scoring so every number opens into the exact lines it came from. It's still the feature customers name first.
2024
Adverse-impact monitoring, by default
We started tracking pass-through rates by stage for every customer, whether they asked for it or not. Six teams found problems in their own funnels within the first month. That was the point.
2024
Adverse-impact monitoring, by default
We started tracking pass-through rates by stage for every customer, whether they asked for it or not. Six teams found problems in their own funnels within the first month. That was the point.
2024
Adverse-impact monitoring, by default
We started tracking pass-through rates by stage for every customer, whether they asked for it or not. Six teams found problems in their own funnels within the first month. That was the point.
2025
AI Teammates
Parsing, drafting and chasing moved to a set of assistants that work inside a live pipeline. Recruiters approve; the assistants do the typing. Larger teams got back around forty hours a week.
2025
AI Teammates
Parsing, drafting and chasing moved to a set of assistants that work inside a live pipeline. Recruiters approve; the assistants do the typing. Larger teams got back around forty hours a week.
2025
AI Teammates
Parsing, drafting and chasing moved to a set of assistants that work inside a live pipeline. Recruiters approve; the assistants do the typing. Larger teams got back around forty hours a week.
2026
$12M Series A
Led by Fieldstone Capital, to fund responsible-AI research, EU and US data residency, and the integrations customers keep asking for. Same thesis as year one: explainable beats clever.
2026
$12M Series A
Led by Fieldstone Capital, to fund responsible-AI research, EU and US data residency, and the integrations customers keep asking for. Same thesis as year one: explainable beats clever.
2026
$12M Series A
Led by Fieldstone Capital, to fund responsible-AI research, EU and US data residency, and the integrations customers keep asking for. Same thesis as year one: explainable beats clever.
LEADERSHIP
The People Behind The Platform
Amelia Chen
Co-founder & CEO
Ran talent for a 400-hire-a-year healthcare group
Amelia Chen
Co-founder & CEO
Ran talent for a 400-hire-a-year healthcare group
Amelia Chen
Co-founder & CEO
Ran talent for a 400-hire-a-year healthcare group
Daniel Okoro
Co-founder & CEO
Built ranking systems before he built hiring ones.
Daniel Okoro
Co-founder & CEO
Built ranking systems before he built hiring ones.
Daniel Okoro
Co-founder & CEO
Built ranking systems before he built hiring ones.
Priya Raman
Co-founder & CEO
Publishes on measurement bias in section.
Priya Raman
Co-founder & CEO
Publishes on measurement bias in section.
Priya Raman
Co-founder & CEO
Publishes on measurement bias in section.
Tomas Ferreira
VP Engineering
Keeps the parser honest at 5,000 applications a role.
Tomas Ferreira
VP Engineering
Keeps the parser honest at 5,000 applications a role.
Tomas Ferreira
VP Engineering
Keeps the parser honest at 5,000 applications a role.
Ingrid Sorensen
VP Customer
Has Personality run 200+ rollouts.
Ingrid Sorensen
VP Customer
Has Personality run 200+ rollouts.
Ingrid Sorensen
VP Customer
Has Personality run 200+ rollouts.
Maya Adeyemi
Head of Design
Believes an interface should never hide its reasoning.
Maya Adeyemi
Head of Design
Believes an interface should never hide its reasoning.
Maya Adeyemi
Head of Design
Believes an interface should never hide its reasoning.
Marcus Whitfield
General Counsel
Translates hiring law into product requirements.
Marcus Whitfield
General Counsel
Translates hiring law into product requirements.
Marcus Whitfield
General Counsel
Translates hiring law into product requirements.
Hana Kobayashi
Head of People
Runs our hiring on our platform, deliberately.
Hana Kobayashi
Head of People
Runs our hiring on our platform, deliberately.
Hana Kobayashi
Head of People
Runs our hiring on our platform, deliberately.
Rishabh Jain
Director of People Ops
APERTURE LABS
“FairHire removed a huge amount of manual work for us. Screening, scheduling, scorecards and candidate comms are connected now, which gave the team back the part of the week they used to lose to admin.”
Hours saved per week
Faster shortlist creation
Shia Daly
Chief People Officer
Amelia Chen
VP Talent
Rishabh Jain
Director of People Ops
Marcus Whitfield
Head of Talent Acquisition
Ingrid Sørensen
Global Head of Recruiting
Daniel Okoro
Recruiting Manager
Rishabh Jain
Director of People Ops
APERTURE LABS
“FairHire removed a huge amount of manual work for us. Screening, scheduling, scorecards and candidate comms are connected now, which gave the team back the part of the week they used to lose to admin.”
Hours saved per week
Faster shortlist creation
Shia Daly
Chief People Officer
Amelia Chen
VP Talent
Rishabh Jain
Director of People Ops
Marcus Whitfield
Head of Talent Acquisition
Ingrid Sørensen
Global Head of Recruiting
Daniel Okoro
Recruiting Manager
Rishabh Jain
Director of People Ops
APERTURE LABS
“FairHire removed a huge amount of manual work for us. Screening, scheduling, scorecards and candidate comms are connected now, which gave the team back the part of the week they used to lose to admin.”
Hours saved per week
Faster shortlist creation
Shia Daly
Chief People Officer
Amelia Chen
VP Talent
Rishabh Jain
Director of People Ops
Marcus Whitfield
Head of Talent Acquisition
Ingrid Sørensen
Global Head of Recruiting
Daniel Okoro
Recruiting Manager
Rishabh Jain
Director of People Ops
APERTURE LABS
“FairHire removed a huge amount of manual work for us. Screening, scheduling, scorecards and candidate comms are connected now, which gave the team back the part of the week they used to lose to admin.”
Hours saved per week
Faster shortlist creation
Shia Daly
Chief People Officer
Amelia Chen
VP Talent
Rishabh Jain
Director of People Ops
Marcus Whitfield
Head of Talent Acquisition
Ingrid Sørensen
Global Head of Recruiting
Daniel Okoro
Recruiting Manager
CAREERS
We Hire The Way We Ask Our Customers To.
Senior Backend Engineer
Engineer
Remote, EU
Full-Time
Senior Backend Engineer
Engineer
Remote, EU
Full-Time
Senior Backend Engineer
Engineer
Remote, EU
Full-Time
Applied ML Researcher, Fairness
Research
Lisbon or remote
Full-Time
Applied ML Researcher, Fairness
Research
Lisbon or remote
Full-Time
Applied ML Researcher, Fairness
Research
Lisbon or remote
Full-Time
Customer Success manager, Enterprise
Success
New York
Full-Time
Customer Success manager, Enterprise
Success
New York
Full-Time
Customer Success manager, Enterprise
Success
New York
Full-Time
Product Designer
Design
Remote, EU
Full-Time
Product Designer
Design
Remote, EU
Full-Time
Product Designer
Design
Remote, EU
Full-Time
Implementation Consultant, ATS
Services
Remote
Contract
Implementation Consultant, ATS
Services
Remote
Contract
Implementation Consultant, ATS
Services
Remote
Contract
GET IN TOUCH
Finally, A Hiring Platform That Adapts To Your Process Not The Other Way Around.
GET IN TOUCH
Finally, A Hiring Platform That Adapts To Your Process Not The Other Way Around.
GET IN TOUCH
Finally, A Hiring Platform That Adapts To Your Process Not The Other Way Around.
GET IN TOUCH
Finally, A Hiring Platform That Adapts To Your Process Not The Other Way Around.