The problem: verifying judgment, not just identity
When an AI model is trained on expert feedback, everything depends on whether the experts are who they say they are and know what they claim to know. In government work, that bar is high. A reviewer who has never run a source selection or interpreted a regulation in practice can approve an answer that sounds right and isn’t.
Most AI-training marketplaces try to solve this with automated screening: an AI-led interview, a timed skills test, and a reputation score that builds up only after someone has completed work on the platform. Those tools can confirm that a person exists and can answer questions on demand. They struggle to confirm the thing that matters most: whether that person has exercised sound judgment in the real world, over years, in the right context.
Where AI interviews fall short
An AI interview is a snapshot. It sees one conversation, on one day, in a format the candidate can prepare for. That creates gaps:
It can be rehearsed
Candidates can study likely questions or get help from AI tools during the interview itself. Polish is not the same as experience.
It measures recall, not judgment
Knowing the textbook answer is different from knowing when the textbook answer doesn’t apply, which is where government expertise lives.
Nobody stands behind the result
A score from an algorithm has no accountability attached. No one who saw the person’s work is putting their name on it.
Ratings arrive too late
Reputation scores only exist after someone has already done paid work, so the first engagements run on trust that hasn’t been earned yet.
How we verify experts
Verification starts from the evidence that already exists in a professional’s career: where they worked, what they did and what they’re credentialed to do. It has three parts.
- 01
Credentials and work history
You share your employment history and credentials. A member of our team reviews them and may follow up with you.
- 02
References from colleagues
If you add references, former colleagues who saw your work can confirm it. We contact them only with your permission.
- 03
Qualified problem by problem
You’re approved for projects that match your background, and every expert is screened for conflicts before the first task.
Always shown separately
Clearance status and conflict-of-interest screening are never folded into a score. Organizations see them on their own, because in government work they can matter more than anything else.
Why colleagues matter most
In government, reputations are built over years and shared through people. Hiring managers call former supervisors. Contracting officers ask peers who they trust. Our references bring that same instinct online, in a form that can be checked.
An AI interview tells you someone performed well for thirty minutes. A colleague tells you they performed well for three years.
It’s earned over time
A reference reflects years of observed work, not one performance.
It carries accountability
A named professional is putting their own reputation behind what they say.
It speaks to context
Colleagues saw how you handled real situations in the specific setting that matters.
It’s hard to fake
You can coach for an interview. You can’t invent real coworkers who confirm your history.
Side by side
| Question | AI interview | Your Roster |
|---|---|---|
| What’s measured? | Answers given in one session | Career history and credentials, reviewed by our team |
| Time span of evidence | Minutes | Years |
| Who stands behind it? | An algorithm | Our team, and colleagues who saw the work |
| Can it be rehearsed? | Yes | Not without a real career |
| Can an organization trace it? | Rarely | Yes, through each expert’s record |
Testing still has a place. Your Roster uses domain rubrics to check the quality of delivered work. The difference is the foundation: we start from a reviewed human record and add testing on top, rather than asking a test to carry all the weight.
Built on consent and privacy
Verification is only worth having if the people in it trust how it works. These commitments are part of the design:
- Opt-in at every layer. Adding references, giving one for a colleague and being eligible for paid work are each separate choices, and each can be revoked.
- Only what’s needed. Credentials are reviewed and noted; raw files are not stored.
- No unsolicited messages. We never send unsolicited messages to the people you know.
- Your network is never for sale. We do not license or sell relationship data, in any form.
What it means for you
For experts
Your record travels with you
The credentials and work history behind your judgment speak for you, without performing for a chatbot.
For organizations
Know who wrote every answer
Every answer arrives with its record: the expert who wrote it, how their credentials were reviewed, and the rubric it was graded against.