A company is the people
in it. We exist to help
you get that part right.

We find people you'd trust with the hard problem,
and build them into a team that lasts.

Aniruddha Kukday
Client testimonialsAccompany Health

"InCommon didn't push us toward a default setup. We had specific requirements about the team's workspace and how they'd plug into our tooling and process, and they built to all of it. The team in India works the way we work."

Aniruddha Kukday
Chief Technology Officer
Azim Damani
Client testimonialsFindigs

"We needed people fast, and most options would have taken months. InCommon moved quicker than anyone we talked to. They understood the roles, went out, and came back with candidates we could actually hire. It took the hiring problem off my plate at a point where we couldn't afford to wait."

Azim Damani
Sr. Manager, Global Operations & Support
Tobey Bryant
Client testimonialsVeramatic

"We sell accuracy. If the numbers are wrong, we don't have a product. So the team building it isn't a place we can cut corners, and InCommon didn't make us."

Tobey Bryant
Co-founder
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 Role specification JD · SCORECARD · COMP Cultural DNA VALUES · TEAM · STAGE INCOMMON HIRING INTELLIGENCE Talent pool ALWAYS REFRESHED AI platform READS THE POOL Domain experts FINAL JUDGMENT OUTCOMES The right hire Updated org hiring context
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.

Applications are the fraction of the market that happened to be looking. We build and refresh a pool continuously, so every search starts against the whole field — and the field keeps growing. More real options, better odds of a strong hire.

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.

Everything mundane in hiring — sourcing, screening, scheduling — is automated without dropping quality. And the model reads patterns across thousands of hires that no human has the bandwidth to see. The more we hire, the more context it holds, the sharper it gets.

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

The person judging your staff engineer has been one.

A recruiter knows the job description. Our experts know the work — the specific combination of skills, background, and instinct that actually predicts someone will be great in your business. They make the final call, and it's the part no model should own.

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
Our thesis

Our bet: three roles, and we go deep on all three.

Someone builds the thing. Someone teaches it what good looks like. Someone judges whether it works. We think most technical hiring converges here, so that's where we go deep — the people, the networks, the judgment to tell them apart. AI teams are where it's clearest today, so that's what's below.

RETRAIN Data SOURCES · PIPELINES Training GPU RUNS · CHECKPOINTS Model ARTIFACT · REGISTRY Serving INFERENCE · API Monitor DRIFT · P95 · UPTIME

Builds AI

Model architecture, training pipelines, inference at scale, and the platform that keeps it fast and cheap. The most crowded of the three, and the easiest to get wrong — plenty of people have shipped a notebook, far fewer have kept a model alive in production.

ML EngineerML PlatformMLOpsInference
ALIGNMENT LOOP PREFERENCES Base model PRETRAINED Data CURATE · LABEL Fine-tune SFT Reward RLHF · A/B PAIRS

Trains AI

Data curation, annotation strategy, fine-tuning, RLHF, preference data. Barely a job title five years ago, so there's no clean résumé signal for it. You find these people by knowing what good judgment about data looks like.

Data & AnnotationFine-tuningRLHFEval data
EVAL REPORT LIVE v14 vs v13 Capability 0% Robustness 0% Safety 0% Regressions 0 FOUND BENCHMARKS · ADVERSARIAL · RED-TEAM

Evaluates AI

Benchmarks, adversarial testing, red-teaming, regression tracking, safety. The rarest of the three by some distance, and the only reason you can trust what eventually ships.

Eval EngineerRed-teamingSafetyQA
From the blog

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Let’s talk

Let’s get hiring right.

Tell us the hard problem you’re hiring for. We’ll find the people you’d trust with it — and build them into a team that lasts.

Talk to us See our work