TO
Transfer OracleSign in
Structural AI Model Auditing

Transfer Oracle

Know before you ship

Standard accuracy metrics miss structural divergence. Transfer Oracle catches distribution shift, memorisation, and deployment mismatch before your model reaches production. One API call. No labels required.

Try a free demoView benchmarks

Transfer Oracle is powered by Growt — a general mathematical framework for structural alignment.

Your accuracy metric is lying to you

High validation accuracy + low oracle score = a model that will fail in production. This is the danger zone.

DomainScenarioVal accOracleRisk
RoboticsPolicy A → Real Robot91%12%HIGH
Medical AIScanner A → Scanner B88%34%HIGH
NLPLoRA fine-tune → prod76%71%LOW
Standard metrics test on held-out data from the same distribution. They cannot tell you whether your model's internal structure will survive a different scanner, a different simulator, a different geography, or a different sensor. Transfer Oracle can.
See the full benchmarks →

Where Transfer Oracle is used

The core question is always the same: are two data sources seeing the same thing — and if not, where do they disagree?

Model layers showing structural degradation after quantization

Quantization auditing

Int4 overall accuracy drops 1.6%. But automobile loses 7.3% while truck stays at 98.2%. Growt runs 10 metrics to find the blind spots accuracy benchmarks miss.

FPGA monitoring chip mounted on PCB alongside edge AI processor

Edge FPGA black box

Hardware-enforced AI monitoring. FPGA co-processor audits every inference in under 10μs — tamper-proof, hash-chained, insurable. If your model stops recognizing pedestrians, you know before the next frame.

Neural network data transfer visualisation

AI model transfer

Does your trained model transfer to production data? Validate before you ship — not after it fails.

Autonomous vehicles with LiDAR sensor overlays

Sensor pair comparison

Two radars, two lidars, two cameras observing the same scene — do they structurally agree, or has one drifted?

Medical research lab with data monitors

Multi-site clinical studies

Patient data from Site A vs Site B — certify structural equivalence before pooling cohorts or training shared models.

Satellite view of Earth with sensor grid overlay

Cross-modality alignment

Radar vs optical, Sentinel-1 vs Sentinel-2 — does the structural content of two modalities agree over the same geography?

Quantum circuit validation dashboard

Quantum output validation

Your quantum computer gave you an answer. Is it structurally sound? Detect noise types, calibration drift, and cross-backend divergence — without classical simulation.

Time-series monitoring screens with drift curves

Temporal drift detection

Has your data distribution shifted between last quarter and today? Catch regime change before your model notices.

Robot arm with simulation ghost overlay

Sim-to-real transfer

Does your simulated training data structurally match real-world sensor readings? Validate the reality gap before deploying.

Split-screen simulation environments comparison

Sim-to-sim comparison

Two simulators, same environment — do they produce structurally equivalent training distributions?

Autonomous vehicle in varied environments

Domain randomisation

Does your randomised sim actually span the real-world variation your robot will encounter? Structural coverage analysis.

Neural network adapter module visualisation

LoRA adapter testing

Does a fine-tuned adapter transfer to new data? Validate adapter compatibility before deploying to a new domain.

What you get from a single API call

POST /v1/audit/transfer — submit two datasets, get a complete structural analysis.

🛡️

Oracle Score

A single number (0–100%) measuring structural alignment between your training and deployment data. Low score = structural mismatch the model cannot recover from.

✅

Verdict

PASS, CAUTION, or RED_FLAG. A machine-readable deployment decision based on oracle score, coverage, and anomaly density.

🗺️

Coverage Analysis

What fraction of the training distribution does your deployment data actually reach? Uncovered regions = blind spots where the model is guessing.

📋

Recommendations

Prioritised actions with evidence. HIGH / MEDIUM / LOW priority. Each recommendation includes the structural evidence that triggered it.

No labels required. No model weights needed. Works with embeddings from any ML framework.
Vision · Language · Action models

VLMs and VLAs: the hidden transfer risk

Each component passes its own benchmark. But when they run together in production, the joint space is where transfer fails — and standard accuracy hides it completely.

Multimodal audit showing vision, text, and joint embedding spaces with per-component oracle scores

Vision space

Does the image encoder produce the same feature distribution on deployment data as it did during training? Scanner shift, lighting, and resolution all live here.

Text / language space

Does the language backbone handle deployment vocabulary, phrasing, and domain jargon the same way it handled training prompts?

Joint (fused) space

Even when vision and text individually look fine, the joint embedding can diverge. This is where cross-modal transfer breaks — and standard accuracy cannot detect it.

Transfer Oracle audits all three in one call. Reports which component has the biggest structural shift — so you know exactly where to intervene.

The numbers speak for themselves

Real benchmark results from MedMNIST datasets run through the Transfer Oracle API.

96%18%

BloodMNIST: val vs oracle

96% published validation accuracy. 18% oracle score. Only 50% structural coverage. Standard metrics miss this.

50%coverage

PathMNIST → BloodMNIST

Half of deployment samples land in map regions with no training coverage — a silent failure waiting to happen.

68%oracle

Same domain (PathMNIST)

Same dataset, same domain — 100% coverage, 68% oracle. A healthy transfer baseline.

Explore all benchmarks →

Transfer Oracle is Growt

At its core, Growt is a general mathematical framework for structural alignment. When two data sources observe the same underlying reality, their internal structures should agree. Growt measures whether they do — and when they don't, it tells you exactly where and how much.

AI model auditing is the most visible application, but the framework applies anywhere you need to compare two distributions structurally: sensor calibration, clinical trial equivalence, simulator validation, temporal drift detection. The mathematics are the same.

“Every existing tool tells you two datasets differ.”

“Transfer Oracle tells you how — which regions, which dimensions, how much coverage is at risk.”

Pre-deployment, not post-incident.

From cloud to silicon

Five deployment tiers. Same structural analysis. Choose the level of integration your use case demands.

☁️

Cloud

SaaS API

Send embeddings, get a structural audit report. Pay per call. Zero infrastructure.

Any model, any size

📓

Notebook

On your GPU

For models too large for API payloads. Run the audit on your own GPU alongside extraction. We never see your model.

Large foundation models (40B+)

⚡

Edge

FPGA co-processor

Hardware board monitors every inference in under 10 microseconds. Tamper-proof hash-chain audit trail.

Deployed models in production

🔒

Integrated

Model + audit on one chip

Your model and our audit run on the same SoC. Zero-latency. Your model stays private. Our methods stay private.

High-security edge deployment

🛡️

Autonomous

Offline, tamper-proof

Same chip, no cloud required. Hardware-enforced monitoring with blockchain-anchored evidence. Insurable.

Military, medical, automotive

Talk to the Transfer Oracle agent

Describe your use case. The agent will run a demo audit, check whether your domain is covered, and help you get started.

Sign in to chat with the Transfer Oracle agent.

Sign in

Get started in 2 minutes

No account required for your first audit.

01

Install the MCP tool

uvx mcp-growt-audit or npx -y mcp-growt-audit — adds Transfer Oracle tools to your AI agent.

02

Run a demo audit

Ask your AI agent: "Run a demo audit". It generates a synthetic RED_FLAG scenario so you can see the output format.

03

Audit your own models

Point audit_from_files at your .npy, .npz, or .csv embeddings. Or use audit_from_huggingface with a model ID.

API Pricing

One API call to know if your model transfers safely. Start free, scale as you grow.

Free

Try it out

$0

1,000 / month credits

  • 1,000 API credits
  • Core audit endpoint
  • Community plugins
  • GitHub support
MOST POPULAR

Pro

For professional teams

$49/mo

50,000 / month credits

  • 50,000 API credits
  • All audit + monitoring endpoints
  • Quantization comparison
  • Novelty detection
  • Email support

Enterprise

For large deployments

$299/mo

500,000 / month credits

  • 500,000 API credits
  • All endpoints + editability
  • Real-time monitoring
  • Priority support
  • Custom integrations

All purchases require acceptance of our Terms of Service and Data Processing Agreement

Start free demoTalk to sales

Want to resell Transfer Oracle under your own brand? Become an operator →

📄 Download the Operator Franchise Deck (PDF)

Apply for access

Tell us about your use case. Your operator will review your application and get back to you.

Contact us

Have a question? Want to discuss your use case? Send us a message and we'll get back to you.

Your message goes directly to the Transfer Oracle team. We typically respond within 24 hours.

🤝

Operator Referral Programme

Recruit a new operator — earn based on their results. When you bring a new operator into the Growt network, you earn a performance bonus tied to their activity. The better they do, the better the bonus for you.

Organisation performance bonuses. There are also bonuses based on how your operator organisation as a whole performs — total revenue, growth, and customer outcomes. The network rewards collective success.

Were you referred by someone?

If an existing operator or contact brought you here, enter their details below so they receive credit for the referral.