AI training data & domain experts, verified and kept | InCommon
AI Training Data & Experts

Data quality is a hiring problem.

Model quality traces back to the people who made the data. Everyone else recruits a crowd and hopes. We verify real experts — chartered accountants, doctors, engineers — test them on the work, and keep the same people on yours. The quality compounds.

Verified inFinance · Medicine · Engineering& adjacent domains
Home Solutions AI Training Data & Experts
The problem

Your data was made by whoever showed up this week.

The data platforms sell scale: millions of raters, any domain, on demand. Look closer and “expert” means someone who passed an online quiz, is juggling task queues on three platforms, and may or may not be pasting your prompts into a chatbot. You can’t verify a crowd of millions. That isn’t a knock on anyone; it’s arithmetic.

And the crowd doesn’t stay. The rater who finally understood your guidelines last month is gone, and this month’s batch was labelled by someone reading them for the first time. Calibration lives in people — when the person leaves, it leaves with them. Your quality doesn’t just vary. It resets, batch after batch.

Every vendor in this market says “experts.” The questions that matter are the two nobody wants: are they real, and will the same ones be on your work next quarter?

PER-EXPERT QUALITY · BY BATCH
Expert A0.94
Expert B0.90
Expert C0.71
Expert C trending down — pulled from client work this batch.
INTER-RATER AGREEMENT
0.86
measured, not assumed
BATCH QUALITY RECORD
Disagreements adjudicated by seniors — attached to delivery.
You see how it was made
How the quality holds up

Real experts, actually checked — and kept on your work.

01 · Verified

Credentials, checked by the same field.

Vetting people isn’t a feature we added for this market. It’s what we’ve built for years — finding people US companies trust with their hardest work, tested by domain experts and a system that reads every claim sceptically. The same machinery now builds expert benches for AI work.

Checked by their own field
A CA who’s closed audits, reviewed by one. A doctor who’s practised, vetted by a doctor.
Not a résumé filter, never a quiz
Real judgment is confirmed by a peer, not inferred from keywords.
India’s expert depth
The world already sends its audits, radiology reads, and engineering docs here.
Seniority, not headcount
Economics here buy you experienced practitioners, not a bigger crowd.
CREDENTIAL · UNDER REVIEW
CA
Chartered Accountant
11 yrs · statutory audit, IFRS
Membership verifiedregistry
Work history confirmedreferences
Peer interview in progressnow
AUTHENTICITY0.93 · STRONG
NOT HOW WE DO IT
An online quiz
Résumé keywords
CA
Reviewer
same field · 18 yrs
“Asked them to walk a real audit. It holds.”
Verdict: verified
02 · Tested

Tested on the work, before your work.

Calibration first
Every expert clears domain calibration tasks before they ever touch a client batch.
A CV doesn’t skip the test
Impressive credentials still have to produce the work at the bar.
Graded against a gold set
Answers are scored against expert-built references, not self-assessed.
The gate is real
Below the threshold means more calibration — not your data as practice.
CALIBRATION TASK · 8 OF 10
Flag the treatment that contradicts the stated contraindication.
SCORE VS. GOLD SET0.91 · PASS BAR 0.85
TASKS
GOLD SET
Expert-built answer key. Every task graded against it.
Not self-assessed
GATE
Cleared for client work
Only then does your data start
03 · Measured

Verified once, measured always.

Scored on every batch
Each expert’s work is measured continuously, not just at intake.
Caught before you’d notice
Anyone whose quality slips comes off client work early.
Agreement, not assumption
Inter-rater agreement is tracked; disagreements are adjudicated by seniors.
The record travels with the data
You see exactly how each batch was made and by whom.
PER-EXPERT QUALITY · BY BATCH
Expert A0.94
Expert B0.90
Expert C0.71
Expert C trending down — pulled from client work this batch.
BATCH QUALITY RECORD
Agreement measured, disagreements adjudicated — attached to delivery.
You see how it was made
04 · Kept

The same people, month after month.

Verification tells you the first batch will be good. Consistency is what makes the tenth batch better — the same experts, deeper in your guidelines, your edge cases, your taste. That’s the part a marketplace structurally can’t offer, because its whole model is whoever’s available. So we don’t run one.

A dedicated cohort
Named experts committed to your project — not spread across five platforms’ queues.
Learn your bar once
They internalise your guidelines, then refine them — instead of relearning weekly.
Calibration deepens
Understanding compounds across batches instead of resetting between them.
Continuity, guaranteed
Dedicated allocation with backfill rules — the cohort persists even as people change.
YOUR COHORT · MONTH 6STABLE
RAR. Anand · finance6 mo
SNS. Nair · finance6 mo
DKD. Kapoor · finance4 mo
GUIDELINE DEPTHdeepening
VS A MARKETPLACE
????
Whoever’s available. Calibration walks out weekly.
QUALITY OVER TIME
Batch 10 > batch 1
Two ways to work

A team you direct, or a dataset we deliver.

Expert cohorts
A dedicated, calibrated team — yours to direct.

A stable cohort of verified experts in your domain, trained on your guidelines and tasking frameworks.

Direct contact, your tools or ours, your playbooks — run it the way labs run their best pods.

The cohort persists. Calibration deepens instead of resetting.

Best when you have your own tooling and want to run the pod directly.
Managed delivery
A spec goes in. A dataset comes out.

You define the task, the schema, and the quality bar. We build the pipeline: experts, review, throughput.

Layered QA on every batch — senior experts review, disagreements are adjudicated, agreement is measured, not assumed.

Delivered on schedule with the quality record attached, so you can see exactly how it was made.

Best when you want a finished dataset, not a team to manage.
The work

From annotation to adversaries.

The tasks change; the requirement doesn’t — people with real domain judgment, working consistently.

Supervised data

Expert-written and expert-labelled examples in finance, medicine, engineering, and adjacent domains.

Preference & feedback

Rankings and rewrites by people qualified to say which answer is actually better.

Evaluations

Domain benchmarks and model report cards, graded by practitioners rather than pattern-matchers.

Red-teaming

Experts probing where a model’s domain reasoning breaks, before your users find it.

The difference

A crowd resets. A team compounds.

DATA QUALITY OVER BATCHES The crowd With InCommon
Batch 1The crowd resets every time its people turn overBatch 10
The crowd
Millions of raters, none of them verifiable at that scale.
Whoever’s available, juggling queues on other platforms.
Calibration walks out the door weekly.
Quality varies by batch — and you find out after delivery.
With InCommon
Verified experts, checked by their own field.
A named cohort, dedicated to your work.
The same people, deeper in your guidelines every month.
Quality measured on every batch, adjudicated by senior experts.
Start small

Judge us on a pilot batch.

Send us a spec and a small batch — a few hundred tasks, your rubric, your bar. We’ll put a verified cohort on it, and you grade the output. The data will make the argument better than this page can.

Start with a pilot
01
Send a spec & a small batch

A few hundred tasks, your rubric, your quality bar.

02
We put a verified cohort on it

Named experts in your domain, with the quality record attached.

03
You grade the output

The data makes the argument better than this page can.

If your task needs a scale or a domain we can’t serve properly, we’ll say so before you spend anything.