Put every model decision on the record.
Inferify writes a tamper-evident evidence record for every inference your model makes: version, input fingerprint, output, confidence, and whether the model was inside its validated operating regime. When a decision gets questioned six months later, the proof already exists.
Your model has a failure mode it cannot see.
Validation certifies performance inside a fixed operating envelope. The moment an input drifts outside it (a new scanner resolution, a rare presentation, a model version you forgot was still deployed) your dashboards still read green. That gap is where unaccountable decisions live.
Accountability is becoming a requirement, not a nicety.
AI is moving into decisions that get audited, disputed, and litigated. The regulatory frameworks now forming around high-stakes AI share one demand: show what the model did, and show it was operating as intended. Today that record is reconstructed by hand, months later, from scattered logs. That window is the opportunity.
Regulation is arriving
Record-keeping and traceability obligations for high-risk AI in the EU, documentation expectations for AI and ML medical devices, model-risk governance in finance, and the push toward measurable, documented AI under emerging US frameworks.
The stakes moved up
Models now decide diagnoses, credit, claims, and autonomous actions. When a single decision can be challenged, aggregate accuracy is not a defense. The individual decision has to be explainable on its own terms.
Disputes follow deployment
Audits, lawsuits, and customer challenges arrive after the fact. The evidence has to already exist at the moment of the decision. You cannot manufacture a trustworthy record once the question is already being asked.
One line wraps every inference.
No pipeline rewrites. Wrap your prediction and Inferify fingerprints the input, records the output and confidence, and evaluates the regime signal before the response leaves your service.
Capture
The SDK hashes the input and logs the model version, output, and confidence. A structured record, not a log line you will parse later.
Validate the regime
Each inference is checked against the model's validated operating envelope. Inside is VALID; outside is FLAGGED with the reason.
Export
One click turns the window into a tamper-evident, SHA-256-chained package, built for regulatory submission, audit, and legal discovery.
Instrument it in an afternoon.
Python or TypeScript. The capture call returns the regime verdict inline, so you can route, hold, or escalate a decision the moment it leaves the model.
import inferify verdict = inferify.capture( model="novadx-v2.3.1", input=xray_512, output={"pneumonia": 0.87, "normal": 0.13}, confidence=0.87, ) if verdict.regime != "VALID": escalate(verdict) # reason: out_of_distribution # record inf_a3f921 · sealed in 24ms
Anatomy of a single decision.
Not a log entry. A structured, signed artifact you can hand to a regulator, an auditor, or opposing counsel, and that they can verify was never altered.
Your model
The capture call wraps your existing prediction. The SDK runs inside your service, in your environment.
Fingerprint + verdict
Input is hashed, the regime is checked against the validated envelope, and a verdict is returned inline.
Hash-chained store
Each record commits to the one before it. Any later alteration breaks the chain and is detectable.
Export package
Any time window exports as a signed, independently verifiable evidence package.
Every inference, accounted for.
The aggregate ledger: live model decisions, their regime verdicts, and exactly why anything was flagged. Illustrative data from a fictional radiology model. The real one runs on your inference stream.
| INFERENCE ID | TIME | MODEL | OUTPUT | CONF | REGIME |
|---|
The moment someone asks what your model did.
The cost of missing evidence is not abstract. It shows up as a specific request, on a specific deadline, that you either can answer instantly or spend weeks reconstructing.
Where a wrong decision has consequences.
Teams shipping models into workflows where any single decision may later have to be explained, audited, or defended.
Healthcare
Diagnostic and triage models facing FDA submission and clinical liability.
Fintech
Credit, fraud, and underwriting decisions under fair-lending and model-risk rules.
Insurance
Claims and pricing models that have to justify each call to a regulator.
Enterprise AI
Agentic and decision systems where a customer dispute needs a paper trail.
An evidence layer, not another monitor.
Monitoring tells you how your model is doing in aggregate, over time. Logging tells you what happened, if you can find it. Neither produces a per-decision, regime-aware, tamper-evident record built to survive an audit. That is the gap Inferify fills.
| Capability | Drift & performance monitoring | MLOps logging | Manual audit | Inferify |
|---|---|---|---|---|
| Per-decision record | aggregate only | unstructured | after the fact | ✓ native |
| Regime validity at decision time | ✗ | ✗ | ✗ | ✓ |
| Tamper-evident and signed | ✗ | ✗ | ✗ | ✓ |
| Built for audit and legal export | ✗ | DIY | manual | ✓ |
| One-line integration | agents | ✓ | ✗ | ✓ |
| Aggregate model health over time | ✓ | partial | ✗ | complementary |
Built for environments that cannot leak.
The teams that need evidence most are the ones with the strictest constraints. Inferify is designed so that adopting it never means relaxing them.
Fingerprint, not payload
Records store a SHA-256 hash of the input, not the raw data. Sensitive inputs stay where they already live.
Runs in your environment
The SDK runs in-process. VPC and on-prem deployment are first-class targets, so data does not have to transit to us.
Tamper-evident by design
A SHA-256 hash chain links every record. The export package is verifiable independently of Inferify.
Compliance-aware
Data residency and access controls are core to the design. SOC 2 Type II is on the roadmap as we move into production deployments.
The regime signal is not a heuristic.
Inferify productizes peer-reviewed work on the structural limits of model evaluation: the formal reason whole classes of failure stay invisible to validation.
The regime validity signal is that research, productized into infrastructure.
Built by the people who formalized the problem.
The two papers above are ours. Inferify is not a wrapper on someone else's idea; it is the productization of research we wrote on why model evaluation has structural blind spots.
- First-author, CVPR 2026, presented in Denver. Top-tier computer vision research.
- First-author oral, AIMLSystems 2025, from a Google Quantum AI collaboration.
- Co-author of the ICML 2026 work Inferify is built on (EIML and PhilML).
- Built a prior product to 15,000+ users across 20+ countries, unfunded.
- Technical founder who ships the product end to end, from SDK to dashboard.
- Aerospace engineering at UIUC: signal processing, radar imaging, controls.
- UAV synthetic aperture radar research at MIT.
- Co-author, EIML @ ICML 2026 and a Stanford PAI 2026 paper.
- Systems-engineering depth in reliability and high-stakes imaging pipelines.
- Owns the rigor that an evidence product lives or dies on.
Most teams selling AI accountability are repackaging monitoring. We are shipping the infrastructure version of our own published research on why those monitors miss what they miss.
Where this goes.
A clear path from a working demo to the system of record for high-stakes model decisions.
The core record
- Python SDK, one-line capture
- Regime validity verdict
- Evidence dashboard
- Signed, hash-chained export
Into production
- TypeScript SDK
- VPC and on-prem deployment
- First paid pilots
- Custom envelope definitions
The standard
- SOC 2 Type II
- Regulator-ready templates (FDA, model risk)
- Evidence API and webhooks
- Cross-team audit workspace
Early, and honest about it.
A founding team with the research credibility to own this category, a working demo, and the first design partners testing it on real workflows. We are raising to turn that into production deployments.
The short version.
No. Logs are unstructured and prove nothing about whether a decision should have been trusted. Inferify produces a structured, signed record per inference, with a regime verdict, built to survive an audit or discovery.
One capture call around your existing prediction. No pipeline rewrite, no model retraining. The SDK runs in-process and the verdict comes back inline, typically in tens of milliseconds.
The capture path is designed to add single-digit to low tens of milliseconds and can run without blocking your response. The record is sealed in the background while your model returns as usual.
Inferify still captures the full signed record, and helps you define a validated envelope from your own historical inputs, so the regime signal becomes meaningful rather than guessed.
Yes. Any model that produces an output and a confidence or score can be captured. For agents, the record anchors each decision in a chain you can replay and defend.
Records are hash-chained, so each one commits to the one before it. Any alteration breaks the chain and is detectable, and the export package is verifiable independently of Inferify.
The record captures an input fingerprint, not the raw input. The design target is teams under strict data-residency and compliance constraints, so the sensitive payload stays where it already lives.
Stop reconstructing what your model did. Start proving it.
Six months of manual audit reconstruction, replaced by an evidence record that is already written, on every inference.