Eight places where telling
real from fake is critical

The problem

AI models cannot tell a real user from a synthetic one, and verifying who is real at the point of access is becoming a national- and commercial-security requirement.

What changes

A model only learns the boundary if it is shown both sides of it. Training on identities that are known-synthetic ground truth — every file labelled, every seed reproducible — gives a positive class that is exact rather than scraped and guessed at.

What to buy

Synthetic detection · Baseline, and the Liveness engine if the decision has to run at the capture.

The problem

Remediating a model is usually a one-off: data is collected for the failure of the week, and the next run cannot be compared to the last.

What changes

Because every image is reproducible from its seed and dial vector, a remediation set can be regenerated exactly, extended along one attribute, and re-run against the same benchmark. Model fixing becomes a pipeline instead of a scramble, and every hardening run pulls compute.

What to buy

Remediation in whichever category your model sits in, then Continuous once the configuration holds.

The problem

A detector is only ever as good as the synthetics it trained on, and public sets are already inside the training data of the models being tested.

What changes

Our identities read as genuine to flagship frontier models 94–100% of the time. That is precisely what makes them useful as the hard positive class: a detector trained against them is trained against the current ceiling, not last year's artefacts.

What to buy

Synthetic detection · Baseline, then Remediation once you have measured where it misses. Or license the Detect engine directly.

The problem

In regulated and sovereign markets, proof of genuine identity is a precondition to procurement — and training data provenance is the first thing asked about.

What changes

The data carries no personal data to begin with: no consent chain to audit, no subject to erase. Every delivery ships a signed manifest and checksums, and any image can be reproduced from its seed, so an auditor can verify what was trained on rather than take a description of it.

What to buy

Any category with the seeds reserved to you, and the delivery manifest retained for audit.

The problem

Counter-synthetic capability is becoming critical: detecting deepfakes, resolving true identities, and red-teaming your own systems without exposing real people.

What changes

Controlled synthetic material can be red-teamed against freely — there is no subject whose face is being misused, and the attack conditions are dialled deliberately rather than hoped for. Exclusivity keeps the identity pool out of anyone else's training set.

What to buy

Face recognition · Remediation with the seeds reserved to you, and the Identify engine for resolution at gallery scale.

The problem

Robots have to recognise real people across demographics, under real capture conditions, and tell them from synthetics — and their vision models are trained on data that underserves both.

What changes

Pose, occlusion, lighting and age are dials rather than luck of the draw, so coverage of the hard captures can be specified instead of sampled. The demographic split is sampled to a documented target rather than inherited from whatever was scraped.

What to buy

VLM training · Baseline for the demographic breadth, with Face recognition · Baseline alongside it for the pose and occlusion depth.

The problem

eIDAS set the reference and other states will follow; each programme needs enrolment and deduplication that works on its own population, not on a Western benchmark's.

What changes

The corpus is Türkiye-anchored and extends across MENAT and the Global South — the populations most datasets underserve — so thresholds are calibrated where the programme actually operates. Deduplication can be rehearsed at gallery scale before a single citizen enrols.

What to buy

Face recognition · Remediation for the enrolment rehearsal, then Continuous, with the Verify and Identify engines for the live decisions.

The problem

The rails are the target: a payment network with weak liveness and synthetic detection carries the fraud, and cannot extend biometric payment safely.

What changes

Attack presentations — print, screen replay, mask — arrive as labelled data with liveness scored at generation time, so a PAD model can be tuned against the specific attack mix a network sees rather than a generic set.

What to buy

Synthetic detection · Baseline for the attack conditions, with the Liveness and Verify engines on the capture path.