Detection tells you something is there. It does not tell you what it means, how sure the machine is, or what the reading rests on. xenoglyph is the layer that answers those questions — and refuses to answer when it cannot.
If you are forwarding this upward, this paragraph is written to be pasted.
xenoglyph is an exploitation layer for imagery: it bolts onto existing infrastructure and returns a defensible reading — a measured profile against dimensions a human authored, with the evidence and model provenance attached, and an explicit record of when it could not explain itself. Two products ship privately today (visual intelligence for analysis teams; edge inference on paperback-sized hardware). Two USPTO provisionals, priority March 2026. Output is emitted against published NATO and OGC standards, so a conformant consumer needs no bespoke parser. Pre-revenue: no customer, no third-party evaluation, and none claimed. Delaware C-corp. Contact: hello@xenoglyph.ai
Most explainable AI is written after the fact: a black box decides, and a second system reconstructs a story about why. That story can be wrong about its own model, and it usually cannot be wrong out loud.
We invert it. The dimensions an operator wants read are authored by a human before the reading happens — in their own words, for their own domain. The engine measures against those axes and returns a profile, not a label. Legibility is not recovered afterwards; it is the input.
The instrument has no opinion of its own to explain. It reports how strongly an image reads against dimensions a person defined and can revise.
The output is a measured profile with the evidence attached. The judgement stays with the person accountable for it.
Where the reading has no backing evidence, the record says so, and the dossier computes the rate at which that happens. No deployment has produced that rate yet — so today this is a mechanism, not a measurement.
Provisional patent application, confirmation 8323. Priority filed March 2026, Alexandria VA.
Companion profiling method, confirmation 9747. Filed April 2026, Alexandria VA.
Visual semantic analysis of an unread manuscript, with an openly licensed
dataset sibling.
10.5281/zenodo.19560958
10.5281/zenodo.19560769
And these are platform behaviours, evidenced per deployment in the deployment dossier — a per-deployment evidence pack with a named section for each, rather than fields on a row:
The second group costs the most to build and is the reason the first can be trusted. Anyone can ship a faster answer. The scarce thing is a defensible one — and a system that says I cannot tell out loud.
Programs already own the collection, the geometry, the workstation and the dissemination backbone. Replacing any of that is a decade-long fight nobody wants. xenoglyph bolts onto what is already there and fills the gap that keeps recurring across every one of those stacks: a per-detection record an analyst — and an accreditor — can actually defend.
No sensors to buy, no platform to displace, no workstation to retrain onto. The instrument reads imagery that already exists in a program of record.
One canonical, schema-validated output contract and thin adapters to the host, emitted against published specifications rather than a private format.
Output is emitted against open, published standards so a conformant consumer needs no bespoke parser — and can replace us later without a migration.
The limit, stated here rather than on request: conformance is measured on OUR side — a versioned schema and a conformance suite against the published specifications. No third party has ingested this output in production. The emitters are tested; the integration is not yet evidence.
Deployable air-gapped and on-premises: the instrument does not require that data or weights leave the customer's boundary. Model upload and model validation inside that boundary are not built — the registry is in-process only today.
The engine is a configurable instrument: an operator authors the vocabulary for their domain, and the engine returns a profile rather than a label. Each torch is that engine pointed at a domain — with the honest status of each stated plainly.
| Name | Domain | Status |
|---|---|---|
| xenoglyph | The engine — reads meaning from images, not objects | Shipping · private |
| MANTIS | Visual intelligence for teams who must act on what they see | Shipping · private |
| APTERA | Edge inference on hardware the size of a paperback — MobileCLIP + ONNX runtime, no accelerator | Shipping · private |
| Lumen | Document and manuscript analysis | Preprint of record |
| pyroglyph | Wildfire intelligence — what a fire is doing, not just that it burns | Public preview |
| PROGLYPH | Adversarial self-review protocol for the instrument's own findings | In development |
| Nymph | Wearable interface — the profile at a glance | In development |
| field-sight | Field and naturalist vertical — read the sign, not just the scene | In development |
| aletheia | Airspace and formation analysis | In development |
The edge family follows a wing-naming roadmap from APTERA outward. The fire is not for sale. The torches are.
An early aerial-imagery capability conflated modern attributed earthworks with ancient ones. The claim was withdrawn, the page carrying it disabled, the capability demoted. Two research findings were withdrawn before publication the same way, on our own initiative, after our own review refuted them. The retirement registries live in the private product repositories and are available under NDA.
A system that has never publicly retired anything has either never been wrong or has never checked. We would rather you learn our limits here than find them yourself.