Across every area
Every vendor will tell you they have AI. Ask what it produces.
A category where everybody has AI is a category where the word has stopped meaning anything. What separates one from another is what comes back, and whether you can check it.
Here it does five different jobs across twelve areas. Every one of them returns something you can open — a value with the sentence it came from, a finding that plays its moment, a ticket somebody already solved.
- A value carries its source
- Unverifiable findings are dropped
- Some questions are refused
Five verbs · twelve areas
- ReadsRoles · CVs · documentsA value with the sentence it came from
- ListensInterviews · sales callsA finding that plays its moment
- FindsTickets · articles · peopleThe answer that already existed
- WritesLetters · follow-ups · draftsA draft grounded in this record
- RefusesEvery query, every areaThe question it will not answer
Not one assistant with a chat box. Five different jobs, each one answerable to something you can open.
The shape of the claim
Not an assistant. Five jobs, in the places the week actually gets spent.
Most AI in this market arrives as a panel on the right-hand side of a screen: it drafts something, or it gives you a number. Both are easy to build and neither survives the second week, because a draft nobody trusted and a score nobody could open are the same problem.
The test we hold ourselves to is narrower. Whatever it produces has to be answerable to something already in your workspace — a line in a document, a second in a recording, a ticket somebody closed in March. If a finding cannot be traced back, it is dropped rather than shown.
- It shows its source, not its confidence —
A percentage tells you how sure a model is. A quoted sentence tells you whether it is right, which is a different and more useful thing.
- Corrections belong to you —
Fix an extraction and the fix holds in your workspace — your phrasing, your clients' job titles, your team's vocabulary.
- It is allowed to say nothing —
Silence is a designed outcome. A finding that cannot be verified against the source does not appear at all.
The five
Reads. Listens. Finds. Writes. Refuses.
Each one has its own page, because each is a different promise with a different failure mode, and lumping them together is how an AI page ends up saying nothing.
It reads what people wrote
Roles, CVs and documents turned into values that carry the sentence they came from — and a verdict that opens per requirement instead of arriving as a single number.
It listens to what was said
Interviews and sales calls returned as quoted, timestamped evidence, with coaching measured against your own team rather than somebody else's.
It finds what already exists
The answer in a resolved ticket, the ticket that matches this one, the candidate you already met two years ago.
It writes what the record supports
Letters and follow-ups grounded in this candidate and this role — with a deterministic version when the draft cannot be produced.
It refuses
Fields it is never sent, questions it declines to answer, and fairness reported on a screen a recruiter does not see.
A companion, named honestly
Cognisense is a different product, and this page is not selling it.
CognityHire also builds Cognisense, an AI interview platform that runs interview rounds itself. It is a real and substantial product, and it is not integrated into CogniYukti — there is no round conducted by it here, and nothing on this platform delegates to it.
We say that plainly because an AI page is exactly where the two get blurred. What is shipped here is a participant that joins a meeting, records it, and returns the hour as quoted evidence with interviewer coaching. A recorder and an analyst of human interviews — not an AI that conducts one. The difference is the first thing a demo exposes.
Objections first
The questions a technical buyer opens with.
›Is this just a wrapper around a general-purpose model?
The value is not the model, it is what surrounds it: the source sentence stored beside every extracted value, the corrections that persist per workspace, the refusal list, and the rule that an unverifiable finding is dropped. Those are the parts that decide whether a team keeps using it.
›What happens when the model is wrong?
You see what it read. Every value carries its source line and every finding seeks the second it came from, so being wrong is visible in about a second rather than discovered a fortnight later.
›Does our data train anything outside our workspace?
Corrections apply in your workspace. Fields you mark personal, financial or a credential are never sent to the reader in the first place — not redacted afterwards, not sent.
›Can we turn the AI parts off?
The areas are entitled separately, and the reading layer is part of the hiring area rather than a separate purchase. What you cannot switch off is the refusal behaviour, which is the point of it.
›Does it conduct interviews?
No. It joins, records, and reports what happened with the moments behind each finding. The product that conducts interview rounds is Cognisense, a companion product with no integration into this platform.
Book a demo
Ask it something it should refuse.
Thirty minutes with your own documents. The useful test is not whether it impresses you — it is whether you can check it when it is wrong.
- Open an extracted field and show me the line it came from
- Correct one, then extract another CV and show me the correction held
- Play the moment behind a finding in an interview
- Search for something phrased completely differently and find it anyway
- Ask it a question about a protected class, and let us watch it decline