Maxor Ground
Catch the hallucination before your users do.
Maxor Ground is a deterministic hallucination firewall. It sits between any language model and whatever comes next — your users, your pipeline, your research record — and checks every answer before it ships. Three independent checks of our own invention, all built on information theory : no training data, no ML classifier to drift, no black box. Just defensible math you can audit, on a verdict you can re-check yourself.
3
Independent checks, on every answer
0
Training data — nothing to poison or retrain
100%
Auditable — re-check every verdict yourself
Cloud → Air-gap
Runs where your data lives
A firewall, not a vibe check.
Language models are fluent by default and correct only sometimes — and the fluent-but-wrong answer is the dangerous one, because it reads exactly like the right one. Ground sits in front of that output and measures whether it's actually grounded in the source you gave it. The check is deterministic : the same answer against the same context always gets the same verdict. Nothing is learned, so nothing drifts and nothing can be poisoned. Wrong answers get blocked or sent back for revision before anyone downstream ever sees them.
Wrap. Check. Decide.
Drop Ground between your model and your users. One integration ; a verdict on every call.
01 — Wrap
Place Ground in front of any model — OpenAI, Anthropic, a local Heisen engine, anything. Pass it the answer plus the source the answer was supposed to come from. No retraining, no model access required.
02 — Check
Three independent checks run in parallel against the source : is the answer grounded, is it appropriately novel, and is every factual claim actually supported. Deterministic math — no classifier, no training data.
03 — Decide
Get a verdict : pass, block, or return-for-revision — with the exact check that failed and why. You wire the policy : silent gate, human review, or auto-retry the model.
Three angles on the same question.
Each check is independent and measures a different failure mode. An answer only passes when all three agree — and any one of them can explain its own verdict in numbers you can reproduce.
Grounding
Does the answer actually follow from the source you provided — or did the model invent it? We measure how much of the output is anchored in the context versus confabulated on its own.
Compression distance
Is the answer a verbatim parrot of the source (no value, and a leak risk) or untethered from it (a hallucination)? Only the grounded-but-useful middle band passes — measured, not guessed.
Claim verification
Every factual claim in the answer is checked against the source before it ships. A claim the source doesn't support is flagged with the exact sentence that failed.
Deterministic, honest, verifiable.
Built to be checked, not trusted on faith — and to drop into the stack you already run.
Deterministic, not a classifier
Same answer, same source, same verdict — every time. There's no model to train, so there's nothing to drift, nothing to poison, and nothing to explain away when it's wrong.
Three independent checks
Grounding, compression distance, and claim verification run in parallel — three different angles of our own invention, so a failure one check misses, another catches.
Drop-in firewall
Model-agnostic and provider-agnostic. Wrap any LLM, any RAG pipeline, any agent loop — Ground only needs the answer and the source, not access to the model.
Bring-your-own-verifier
Don't trust the verdict — reproduce it. The math is open : re-check any decision yourself, on your own infrastructure, and get the same number.
Every block is explained
A blocked answer always says which check failed and on which sentence — actionable for a human reviewer, parseable for an auto-retry loop.
No training data, no telemetry
Nothing learned, nothing logged off-box by default. The whole check runs where your data lives — including fully air-gapped.
The failure modes that ship as facts.
Each of these reads like a correct answer. Ground measures the failure directly, so a brand-new variant trips the same check — there's no signature list to keep updated.
Confabulation
Facts, figures, and quotes the model invented that appear nowhere in the source — the classic hallucination, caught before it ships.
Ungrounded claims
Assertions the provided context simply doesn't support — plausible, fluent, and unbacked.
Broken citations
Claims attributed to a source that doesn't actually say it — the footnote that doesn't check out.
Parroting & leakage
Verbatim regurgitation of the source that adds nothing and can leak material you never meant to surface.
Confident wrongness
A fluent, authoritative answer with no factual footing — the most dangerous output, because it reads like the right one.
Context drift
Answers that start grounded and wander off the source mid-response, smuggling unsupported claims into a credible opening.
Hosted, or entirely inside your perimeter.
Start in the cloud ; bring it in-house when your security review — or your data-residency rule — demands it.
Cloud
Fully managed on Canadian-resident infrastructure. The fastest way to put a firewall in front of your model — nothing to provision.
Self-hosted
Run the engine inside your own environment for Enterprise deployments — your keys, your infrastructure, your control plane. Answers never leave your network.
Air-gapped
Fully disconnected for the most sensitive work. No telemetry, no phone-home — the check runs where the data lives, because the math doesn't need the internet.
Research records and production answers alike.
Anyone who ships a language-model output that has to be right — whether that output goes to a customer, a regulator, or into the scientific record.
One deterministic philosophy, across the suite.
Ground shares the Maxor posture : deterministic math over black-box models, verifiable over trust-us, sovereign-deployable by default. It's the firewall that sits beside Heisen and Maxor Audit in the evidence substrate behind every Maxor surface.
Explore the Maxor suiteStatus
🟡 Foundation
Deployment
Cloud · Self-hosted · Air-gap
Owner
Maxor Global LLC
Put a firewall in front of your model.
Tell us where your model output goes and what it can't get wrong — research, regulated, or customer-facing. A senior lead responds within one business day.