Talent infrastructure for AI-native companies | InCommon

The hardest part of
building with AI
is still the humans.

InCommon builds the teams behind
fast-growing AI-native companies.

Aniruddha Kukday
Client testimonials
Accompany Health

"We wanted an AI-native engineering team in India that could work alongside our product team in the US, not behind it. That's what we got. They plug into our tooling and our review standards, and on a call you can't tell which side of the world someone's on."

Aniruddha Kukday
Chief Technology Officer
Azim Damani
Client testimonials
Findigs

"We were building AI into underwriting, and we needed the people who would work alongside it. InCommon scaled that team faster than anyone else we spoke to, and the people they found understood what the work actually required."

Azim Damani
Sr. Manager, Global Operations
Soumya Sengupta
Client testimonials
Veramatic

"We were building AI-native automotive software from scratch. Nobody comes ready-made for that. InCommon found engineers with a product mindset and real AI fluency, which is what let us move fast on the build. Most of that team is still here."

Soumya Sengupta
Director of Engineering
Talent of the future

Knowledge work is converging on three capabilities.

Knowing these properly is what lets us read the work instead of the account of it — the difference between someone who can do the job and someone who interviews like they can.

TRAIN YOUR OWN FRONTIER MODEL SHIPPED WHAT TO BUILD · WHERE IT BREAKS

Build with AI

Your team can ship with models. Whether that means training your own or building on someone else's, the scarce judgment is the same: what to build, and where it breaks.

AI EngineerML PlatformApplied ResearchData Engineering
RUNNING MODEL v14 PASSED FLAGGED

Evaluate AI

Your team knows whether it works. Benchmarks, regression tracking, red-teaming — plus the domain experts who can tell when a model is confidently wrong. A lab needs this before it ships. A company in regulated work needs it before it goes live.

Eval EngineerBenchmarksRed-teamingDomain Experts
MODEL WORKFLOW HUMAN REVIEW

Deploy AI

Your team can put it inside a real business. The integration is rarely the hard part. Knowing where the model will be confidently wrong, and designing around it, is.

Forward-deployedSolutions ArchitectAI ProductIntegration
Hiring intelligence system

Hiring intelligence that compounds.
Domain experts and an AI-native system — so every hire sharpens the next.

CONTEXT FEEDS BACK INPUTS Hiring manager context PREFS · PAST HIRES · BAR What the team must deliver SCOPE · SCORECARD · COMP Cultural DNA VALUES · TEAM · STAGE INCOMMON HIRING INTELLIGENCE Talent pool ALWAYS REFRESHED AI platform READS THE POOL Domain experts FINAL JUDGMENT OUTCOMES A team that holds What we learned about your bar INPUTS Hiring manager context PREFS · PAST HIRES · BAR What the team must deliver SCOPE · SCORECARD · COMP Cultural DNA VALUES · TEAM · STAGE INCOMMON HIRING INTELLIGENCE Talent pool ALWAYS REFRESHED AI platform READS THE POOL Domain experts FINAL JUDGMENT OUTCOMES A team that holds What we learned about your bar CONTEXT FEEDS BACK
Hiring intelligence system

A closer look at how it works.

01 · Talent pool

The best person for the role isn't reading your job post.

Every standard tool selects for who's available. Applications reach whoever happened to be looking, in the two weeks the post was up. A CV is the candidate's own marketing — what they claim, not how they work. Cold outreach lands next to a dozen others that week and gets the same reply: none.

We do the slow part in advance, so you never wait for it. We judge people on work we've actually seen, get introduced by the ones we've already placed, and keep talking for years with no role attached. By the time you have a role, the conversation is years old and the shortlist takes days.

Talent Pool
POOL · SOURCESLIVE
Trusted referrals
From top professionals
38%
AI agent outreach
High-intent, personalized at scale
24%
Compounding network
Deepens every week
18%
Hackathons & events
Seen building in person
12%
Inbound interest
LinkedIn & community
8%
Talent Pool
Five live channels · refreshed daily
IC
02 · AI platform

Somewhere in a thousand résumés, there's a pattern.

Every hiring process runs out of attention before it runs out of candidates. A thousand applications means six seconds each, which is triage, not judgment. Keyword filters reject people who did the work but wrote about it differently — and you never see them. Nobody checks which signals actually predicted a good hire, so the bar never improves.

So we let software do the reading. It holds the same bar at candidate one and candidate twelve hundred, sorts on the work rather than the words, and gets sharper with every hire we make. Your team only meets the ones worth meeting.

Hiring Pipeline
PIPELINESTEP 2 / 3
Shortlisting
1,200 → 60 candidates
DONE
Screening
60 → 18 in deep review
ACTIVE
Scheduling
Interviews booked for you
QUEUED
Hiring Pipeline
Shortlist · screen · schedule
AI
03 · Domain experts

You cannot assess work you have never done.

That's not the recruiter's fault. Every standard check measures something other than the work. A recruiter has read the job description, not done the job, so they can check keywords but not depth. Interviews reward people who interview well, which is a different skill from the one you're hiring for. And references are chosen by the candidate — they were never going to say anything else.

So before anyone reaches you, they have been through someone who has done the work. They go at the real problems, where polish stops helping, and put their name on what comes back. Software narrows the field, an expert vouches for what is left, and the hire is still your call — made from a shortlist where nobody is there by accident.

Expert Review
EXPERT · REVIEWVETTING
JUDGMENT PARAMETERS
Depth of craftcore
Real ownershipcore
Trajectory & slopehigh
Signal over polishhigh
Human interview
45 min with a domain expert
BOOKED
Expert Review
Clear parameters · human final call
DX
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