A measurement substrate for language: coherence, domain, and source-anchored fidelity — computed deterministically, traceable to public linguistic resources, and auditable end to end.
Modern AI puts meaning into billions of opaque weights. It's capable — but its judgments are non-deterministic, hard to explain, and difficult to audit.
In compliance, security, and other high-stakes settings, "the model decided" is not an answer. Teams need to know why a piece of content was routed, flagged, or trusted — and to get the same answer twice. Today that guarantee is missing.
We measure language on a structured, deterministic substrate grounded in public linguistic resources. The same input always yields the same result, and every result carries a derivation you can follow back to its sources.
It's a complement to neural systems — built for the places where reproducibility, explainability, and footprint matter more than open-ended generation. It runs on commodity CPUs, needs no model resident, and plugs in alongside the tokenizers and pipelines you already use.
The name is the idea. Common stones — the shared, public linguistic foundations every judgment is built on, identical for everyone who checks the work.
In-line allow / deny on outbound content by domain — to LLMs and beyond the network — with a deterministic, auditable decision on every request.
Classify and route content by domain before it reaches a model, so only the right material incurs a call — a cheap, transparent pre-classifier.
Source-anchored, per-fact verification of numbers, entity-bindings, and citations against the input or a retrieved source — an auditable verdict, not a scalar guess.
Reproducible coherence and domain scores with a derivation that traces to public linguistic resources — measurement you can put in a report.
These are settings where "the model decided" fails an audit — and a deterministic, traceable verdict is the deliverable.
Finance, healthcare, and telecom archives held under multi-year retention rules (for example SEC 17a-4 and HIPAA). Reproducible classification and verification of stored communications, with a record that holds up on review.
An evidence trail for automated language decisions — a show-your-work record that risk, legal, and regulators can inspect, instead of a confidence score no one can interrogate.
Deterministic allow / deny on outbound content by domain, at the edge, with a decision you can replay byte-for-byte during an incident review.
A per-fact check on model output before it ships — numbers, entity-bindings, and citations verified against the source, so a hallucinated figure is caught and named rather than passed downstream.
Byte-identical results under fixed inputs — reproducible by construction.
Decomposable, per-fact verdicts that name what was checked and against what.
Every result traces back to public linguistic resources, not hidden weights.
Runs on commodity CPUs with no resident model — viable at the edge.
Sits alongside existing tokenizers and pipelines instead of replacing them.
Interlocking inventions filed across the stack — coherence measurement, paragraph domain identification, an indexed storage architecture with build-reproducibility guarantees, and a compact structured-token representation. The claims are designed to reinforce one another rather than stand alone.
The linguistic inputs are public for reproducibility and audit — so the advantage is the method and the engineering, not gated data a competitor could license too.
An auditable, reproducible decision is a hard requirement in compliance and security — one that opaque-model competitors cannot meet by tuning a prompt.
We're engaging design partners and investors. If reproducible, auditable language measurement is relevant to what you're building or backing, we'd like to talk.