Truth. Evidenced.

Synthan.AI is building the discipline of Evidence Intelligence — a rigorous approach to verifying claims, tracing sources, and making every conclusion auditable.

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What is Evidence Intelligence?

Answers are easy.
Auditable answers are the work.

Generative AI

Produces an answer.

Evidence Intelligence

Produces an answer you can audit.

Evidence Intelligence is the practice of treating every claim as a question to be investigated — retrieving primary sources, weighing corroboration against contradiction, and showing the full chain of reasoning behind a conclusion. We are building this discipline in the open, and building UnmAI to put it to work.

Meet UnmAI.

Evaluation record · 4 September 2026

0.952

accuracy on evidence-based claims · 124 of 218 claims

MEASURED, pre-production. Reflects a routing improvement validated in our evaluation environment (+2 claims, zero regressions across two independent test sets). UnmAI remains pre-production and not yet publicly available. See the evaluation record below for the full breakdown and limitations.

The flagship Evidence Intelligence platform — built for the people whose work depends on getting it right: journalists, researchers, and analysts.

UnmAI · Evidence Investigation — preview

Claim under investigation

Source 1 peer-reviewed journalCorroborates
Source 2 official statistical releaseCorroborates
Source 3 no position on the claimNeutral
Source 4 undated secondary postConflicts

Confidence

Assessment
confidence

Evidence graph

In your browser Prototype

A Chrome extension for investigating claims on the page you are reading. In development; not yet publicly released.

In your stack Prototype

A REST interface for evidence-checked answers inside your own systems. In development; no public endpoint is available.

POST /v2/investigate { "claim": "…", "depth": "standard" }

Illustrative interface. Not a real investigation — no live data, sources or verdicts are shown.

7 published research papers Patent application filed Doctorate in AI Governance Enterprise research background

Use UnmAI

Verify a claim. Inspect the evidence.

The live UnmAI verification experience is now a dedicated product surface — designed for evidence, provenance and auditable conclusions.

Evidence, not opinion.

Submit one factual claim and review the verdict, supporting or contradicting evidence, confidence context and technical provenance behind the investigation.

Verify with UnmAI →
Submit a factual claim
Retrieve and evaluate evidence
Inspect verdict and provenance

Live verification sends the claim you submit to the UnmAI verification service. Do not submit secrets, confidential material or sensitive personal information. See our Privacy Policy for details.

Every day,
millions of claims
are created.

Most AI
simply answers.

UnmAI investigates.

Truth isn’t generated.
It is discovered.Explore the research

Research.
Not marketing.

7
Published research papers
2
Published books
1
Patent application

Intended Applications

Where evidence
has to hold up.

Deepfake & Fraud Detection

Investigate suspect media and claims against verifiable sources before they cause damage.

Financial Report Verification

Check disclosed figures against filings, registries and primary data.

Insurance Claim Investigation

Assemble evidence chains for claims — corroborated, contradicted, or unresolved.

Enterprise Knowledge Validation

Keep internal knowledge bases anchored to current, cited truth.

AI Governance

Make model outputs auditable with provenance and confidence on every answer.

Regulatory Compliance

Evidence trails designed for review by auditors and regulators.

These are the domains Evidence Intelligence is being designed for. They describe intended application, not deployed capability or existing customers.

Data Sources Evidence Collection Hybrid Reasoning Confidence Engine Evidence Intelligence Human Decision

Evaluation Record

What we have actually measured.

Current figures, the corrections that produced them, and the limitations that qualify them. A number without its scope is not evidence.

218
Claims in the
evaluation set
94/94
Deterministic computation
(arithmetic, dates, units)
0.952
Evidence-based verification
118 of 124 claims
1,142
Automated tests
passing
Record date4 September 2026
Evaluation set218 claims
Deterministic computation (arithmetic, dates, units)94/94 = 1.000
Evidence-based verification118/124 = 0.952
Combined212/218 = 0.972
Automated tests passing1,142
Deployment statusPre-production, not publicly available
Independent third-party validationNone to date

Limitations — read these with the numbers above

  • This is an internal evaluation set. Nothing here has been independently validated, and we would welcome that.
  • Accuracy on evidence-based claims is one metric on one class of claim. It does not describe behaviour across all claim types.
  • Deterministic claims — arithmetic, dates, unit conversion — never touch retrieval or evidence. They measure a routing layer, not the fact-checking system. 0.952 is the figure that describes evidence-based verification; the combined figure includes the deterministic set and should not be read as the accuracy of the evidence pipeline on its own.
  • The 0.952 figure reflects a routing improvement validated in our evaluation environment, not a change shipped to a live product. UnmAI is pre-production.
  • Live retrieval is not reproducible. Two identically configured runs have previously produced materially different results, which is why frozen-evidence replay exists — and why its correctness matters as much as the system it measures.
  • The evaluation set is single-provider by design, as an experimental control. It says nothing about behaviour across providers.

Research & Engineering Updates

Findings, including
the inconvenient ones.

Measurement audit30 August 2026

The instrument was bent, and one stubborn number was the only tell.

What we investigated

A single claim in our frozen evaluation set had never reproduced its own earlier verdict. The result had sat in our reports as 34 out of 35 for three weeks. We had explanations on file — marginal evidence, a hard case near a decision boundary — and had recorded it as an acceptable characteristic of the set rather than a defect.

What we found

The explanations were wrong. The evaluation harness was intended to freeze everything about a claim so that reasoning could be measured in isolation. It was instead rebuilding three of the four values that determine how much each piece of evidence counts. Every score drifted by roughly a tenth of a point in a consistent direction, on every claim, in every run we had ever recorded. Only one claim in the set sat close enough to a decision boundary for that drift to change its outcome.

Four further measurement defects surfaced the same day: a scoring path that never resolved fourteen results, a cache returning verdicts for claims that had not actually been run, and evaluation records that omitted the feature configuration, leaving us unable to prove which version of the system had produced which result.

What changed

After correcting the instrumentation, accuracy on evidence-based claims measured 0.935, up from 0.895. No change was made to how the system reasons. The figure moved because the measurement was corrected, not because the system improved.

Why it matters

Thirty-four out of thirty-five had the shape of a healthy result. It was one claim away from being indistinguishable from a healthy result — and every conclusion drawn from that instrument over the preceding month rested on values that were wrong in a direction nothing in the reporting could reveal.

What it does not mean. The corrected figure has not been independently validated, and it is measured on an internal set. It also does not mean the instrumentation is now known to be correct — only that five specific defects in it have been found and fixed. Previously published figures produced by the faulty instrument are withdrawn rather than restated.

Decisions recorded30 August 2026

Three decisions we wrote down, including one that cost us accuracy.

A feature we built, measured, and switched off

We implemented a capability, tested it thoroughly, and measured its effect against the evaluation set. The measurement showed no material effect, so it is switched off. Building something is not evidence that it helps.

A disagreement we logged rather than deferred to

On one claim, an external fact-check database entry conflicted with the conclusion our system reached on unanimous evidence from primary scientific sources. The system did not defer to the database, and on review the disagreement was recorded rather than resolved in favour of the more authoritative-looking source. Provenance means recording which sources disagreed, not selecting the one that is easiest to defend.

A change that would have improved the number, which we rejected

We measured what would happen if we adjusted our decision thresholds. It would have gained two claims. It would also have removed the constraint that prevents the system asserting conclusions from thin evidence. We did not make the change, and we recorded why — because tuning thresholds against your own test set produces a better score and a worse system.

What it does not mean. These are internal engineering decisions on an internal evaluation set, published for transparency rather than as validated methodology. The third-party fact-check entry referred to above is described in general terms and is not identified.

Controlled experiment18 August 2026

Our own system was systematically under-confident.

What we tested

Whether a ceiling on confidence scores persisted when evidence was held frozen, removing retrieval and model variation as explanations.

What we observed

Across two live evaluation runs and one frozen-evidence replay of the same claim set, no claim reached 0.70 confidence. The cause localised to a single factor binding on 30 of 35 claims — it counted retrieved material that took no position on the claim, and such material averaged half of everything retrieved. In one case a claim where every source that bore on it agreed was reported at 27% confidence.

Why it matters

Confidence is user-facing. For someone deciding whether a claim is safe to publish, a well-evidenced claim shown at 27% is not caution — it is a signal carrying no information, because the system says much the same thing about everything.

What it does not mean. A correction has been specified and its predicted effects registered in advance, but the confirming run is not complete. No post-correction confidence figures are published here.

Controlled experiment18 August 2026

Neutral evidence is not contradiction.

What we tested

Three candidate definitions of “conflicting evidence,” measured on the same frozen set, to establish when a claim genuinely warrants escalation.

What we observed

Treating any spread of stances as conflict flagged all 35 of 35 claims. Requiring genuine contradiction — evidence both supporting and opposing — flagged 3. Requiring a material minority flagged none. Eighteen claims contained no contradicting evidence at all, yet every one would have been reported as conflicted.

Why it matters

A system that reports everything as contested is as unhelpful as one that reports nothing as contested. This is now recorded as a standing invariant in our design.

What it does not mean. This rules out two escalation designs. It does not establish the correct one; that remains open work.

Updates describe internal engineering experiments and measurement audits on controlled evaluation sets. They are not peer-reviewed findings and have not been independently validated.

Latest Research

Full publication list available on request · Contact us

Our Philosophy

Information
became abundant.

Truth
became scarce.

Then AI made both faster —
and harder to tell apart.

So we’re building a discipline
for telling them apart:
Evidence Intelligence.

Built by researchers. Built in the open. Designed for everyone.

AI Researcher
Enterprise Technology Leader
Founder of Synthan.AI

Dr. Jerome Jayanathan Needhipathi

DBA, AI Governance & Enterprise Transformation — Swiss School of Management

Synthan.AI is pioneering the Evidence Intelligence category — a new class of AI defined by verification, provenance and auditable reasoning.

7 published research papers, 2 books and a filed patent application, with prior leadership roles in global energy and technology research organisations.

Synthan.AI — From Thought to Truth

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to build
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