Growing Intelligence

Repair the model.
Prove nothing else broke.

The only tool that repairs AI models, proves zero damage, and signs every result.

Contact us
ROME2,233 broken

ROME: 2,233 of 6,709 questions broken (answered correctly before, not after), 21 repaired, 14 already correct and left alone, 4,441 still wrong.

Probe560 broken

Probe56: 0 of 6,709 questions broken (answered correctly before, not after), 839 repaired, 2,247 already correct and left alone, 3,623 still wrong.

Every method on the same 6,709 questions. Violet repaired, green already correct, grey untouched, red broken — a fact the model answered correctly before and no longer does.

3
models certified
69%
accuracy, from 32%
0
collateral damage
Ed25519
signed proof
The platform

repair and certify

Scan any open-weight model. Find exactly where it fails. Repair it with zero collateral damage. Monitor it in real time. Sign every result.

Probe56

AI model health platform

Diagnostic scan across 56 functional elements and 5 dimensions. Targeted repair with zero measured collateral. Ed25519-signed certificate proving model health before and after. Air-gapped. No outbound calls. No customer data required.

Guardian

Deterministic safety classifier

Blocks prompt injections, jailbreaks, and unsafe inputs in under 5ms. Zero GPU. Standalone safety layer. Always on. Deterministic ruleset. Zero false positives.

Gyro

Real-time drift monitor

Monitors every inference at the neuron level. Detects model drift before it reaches production. Confidence score on every answer. Calibrated per deployment. Neuron-level, not output-level.

GI Engine

Cryptographic audit trail

Ed25519-signed, tamper-evident record of every AI action. Live in production. The reference implementation of Verifiable Process Infrastructure.

Proven on production models
32B
general model
32% → 70%
24B
banking model
32% → 70%
24B
healthcare model
certified
~2,000
repairs
0 damage

3 models certified · ~2,000 repairs · 0 damage. All results independently verified and Ed25519-signed. Full case studies available on request.

Verified against ROME, MEMIT and AlphaEdit — zero collateral on two unrelated models. See the proof →

MEMIT177 broken

MEMIT: 177 of 6,709 questions broken (answered correctly before, not after), 1,044 repaired, 2,070 already correct and left alone, 3,418 still wrong.

AlphaEdit523 broken

AlphaEdit: 523 of 6,709 questions broken (answered correctly before, not after), 1,211 repaired, 1,724 already correct and left alone, 3,251 still wrong.

The two methods that score well on general benchmarks, on the same 6,709 questions and the same scale as the panels above. MEMIT broke 177; AlphaEdit broke 523.

Built for

environments that cannot afford to be wrong

Healthcare

Data sovereignty is not negotiable and neither is a wrong answer. Model weights and patient prompts never leave your environment.

Finance

Every result is Ed25519-signed and reproducible, which is the form an auditor can act on when asked to show what changed and when.

Government

Runs air-gapped against open-weight models you host yourself — no vendor endpoint, no outbound call, no dependency on our uptime.

Insurance

Claims models move between jurisdictions and must carry their evidence with them. Every repair ships as a signed record of exactly what changed.

HR and employment

Hiring models are a high-risk class under the EU AI Act, where a fix that quietly breaks an unrelated answer is a compliance event.

Aviation and technology

A chatbot's answer has been held to be the company's answer. Failures are found before release and every repair is provable after it.

FAQ

questions

What is AI model health?
AI model health is the practice of measuring and improving the reliability of a deployed language model without degrading its existing correct behavior. Unlike observability, which only monitors drift, model health includes repair — finding and fixing model failures, and proving that the fix caused no measurable collateral damage.
Does model editing break other facts?
Probe56 performs targeted edits verified against the full control set. Every edit ships with a measured collateral report: sample size, control set, and a 95% confidence bound. If any previously-correct answer degrades, the edit is rejected automatically.
How do you prove an edit caused no damage?
Every Probe56 run is evaluated against a held-out control set of previously-correct items under a no-teacher-forcing protocol, reporting sample size and a 95% confidence bound on the true collateral rate.
Does this work on open-weight models we host ourselves?
Yes. Probe56 is built for open-weight models running on your own hardware or in your own VPC. Nothing leaves your environment, which is what makes it viable for healthcare, finance and government deployments with data-sovereignty requirements.