We aren't building simple chat wrappers. We are engineering autonomous agents that natively execute Indian compliance, deep EPF UAN verification, behavioral interviews, and pixel-level forensics.
Select an agent to execute case
System Trace
System standby.
Awaiting execute command.
Structured Output
Evidence extracted and mapped in real-time.
Document forensics
Reads an uploaded document the way a forensic examiner would — the pixels, the fonts, the metadata, the compression history — rather than the way a person skims it.
Limit — Cipher proves a document was altered. It cannot prove who altered it, or that the original claim was false — a candidate may have edited a typo. The finding goes to a human with the evidence attached.
Source correlation
Queries the registries that actually hold the record — EPFO, eCourts, NSDL, UIDAI, sanctions lists — and reconciles what comes back against what the candidate declared.
Limit — An absent EPFO record is not evidence of no employment. Establishments below the statutory threshold, genuine contractors and much of the informal economy never appear — Aegis reports that as an insufficiency, never as a clear.
Structured interviewing
Runs the reference conversation as a structured interview rather than a chat — same questions, same order, every time, so two references can actually be compared.
Limit — A reference produces judgement, not fact. Echo records what was said and flags where two accounts differ — it does not decide who is right, and a referee's opinion is never reported as a verified finding.
Candidate experience
Runs the candidate's side of the case — consent per purpose, the exact document that is missing, and a nudge that says what to fix rather than 'please re-upload'.
Limit — Nexus cannot proceed without the candidate. If consent is withdrawn or the document genuinely does not exist, the case stops and is reported as an insufficiency — never quietly closed as a clear.
The candidate supplies everything the role's package needs in one structured pass, validated as they type. Every check that has its inputs starts immediately.
Produces — A consented, complete case
Checks fire in parallel, not in sequence. Identity resolves first because everything downstream depends on the identity being right — a criminal search against the wrong person returns a confident clear.
Produces — Every source queried at once
Cipher reads the documents, Aegis reconciles what the sources returned, Echo runs the structured conversations, Nexus keeps the candidate moving. Each attaches its evidence to the case.
Produces — Findings with sources attached
Cases with nothing to decide clear automatically, with the evidence retained. Anything adverse, ambiguous or unresolved goes to a person — with the finding, the source, and what the agent could not establish.
Produces — A decision someone stands behind
Not a roadmap item. These are the boundaries the platform is built around, because the cost of a false positive is borne by the candidate and the cost of a false negative is borne by you — and those are not the same error.
A discrepancy, a tampered document, a record match. The agent surfaces it with its evidence; it never converts it into a verdict about a person.
A name variant, a common name in a searched jurisdiction, a partial date match. Name-plus-city is not identity, and treating it as identity is how someone loses an offer over another person's record.
A registrar that never replied, a record not digitised, a candidate who did not authenticate. Reported as an insufficiency with the reason — never as a clear.
When a candidate disputes a verified finding, both versions stay on the record with the difference stated. A system that stores one verdict has nowhere to put a disagreement.
No agent here ships because it demoed well. Each one is measured against cases whose answers are already known, and the measurement that matters is not the one most vendors publish.
Closed cases with known outcomes, never seen in development. Synthetic test documents are useful for coverage and useless for calibration — real forgeries are worse than the ones you would invent.
A single accuracy figure hides the trade. We track missed findings and false flags as distinct numbers, because tuning to improve one silently worsens the other.
A second model, from a different provider, argues against every finding. A model checking its own work shares its own blind spots and will confirm its own mistake.
The agent runs alongside the human process without touching the outcome, until its disagreements are rare and explainable. Only then does it reach a real case.
An AI verification platform reads a great deal of sensitive personal data. What happens to it afterwards is a fair question and a short answer.
Candidate data is used to run the check that was consented to, and for nothing else. It does not train a model, ours or anyone's.
Storage and processing stay in Indian infrastructure, which is a requirement for most BFSI and healthcare buyers rather than a preference.
A confirmation that a degree was verified may reasonably be kept for the employment; the certificate scan behind it usually may not. They expire on separate clocks.
A check cannot be started without a live consent record for that specific purpose. It is enforced in the code that dispatches, not in an operator's judgement.
Listed with their current state rather than as a roadmap of certainties. An agent in training is one whose disagreements with a human reviewer are still too frequent to explain.
Financial-record correlation
Reconciles bureau data and directorship filings against declared conflicts of interest. Currently over-flags common-name directorship matches, which is exactly the failure mode that cannot ship.
Continuous re-screening
Watches sanctions and adverse-media sources for existing employees rather than re-running a full check. The hard part is not detection, it is suppressing the same match every week.
Jurisdiction routing
Decides which court registries to search from an address history. Today a person makes that call, and gets it right more often than the model does.
An agent that says “verified” without the record behind it is asking you to trust it. Each step above names the source it queried and what came back, and all of it stays attached to the case.
Agents retrieve, extract, match and rank. The decision to flag a person is made by a reviewer, because a false positive costs someone a job they were entitled to.
When a source cannot answer, the report says so. The most dangerous output a verification platform can produce is a confident clear on a check that never actually ran.
Bring a live requisition and we will walk a case end to end — the checks, the evidence behind every verdict, and exactly where a discrepancy would surface.