Dev, staging and production, properly separated
Every environment has its own data boundary, keys and audit trail. A model in staging cannot reach production data, and a change in one is invisible to the others until you promote it.
KUVAI · MODEL DEVELOPMENT
Build models where the data can't leave the building.
Kuvai gives fintech and healthcare teams isolated, audited inference environments to develop, evaluate and deploy their own models — from an experiment nobody else can see to a production endpoint you can defend to an examiner.
3 checks passed
1 blocking
env: staging
What Kuvai gives you
ENVIRONMENTS
COMPLIANCE GATES
Evaluation that has to pass before promotion
Bias checks, leakage scans and the regulatory tests your team defines run automatically before a model can move from staging to production. A failed gate blocks promotion outright.
RESIDENCY
Data and inference stay where the rules say
Pin experimentation, evaluation and serving to a jurisdiction. PHI and PCI data never has to leave the environment it is licensed to be in.
LINEAGE
Every model traced to its training run
Dataset version, training run, evaluation results and approver are recorded against every deployed model, so a question from an examiner has a documented answer.
ISOLATION
Per-team, per-project boundaries
A research team's sandbox cannot see another team's data or models. Boundaries are enforced by the platform, not left to naming conventions.
AUDIT
A record built for a review, not reconstructed for one
Access, promotion and inference events are captured as they happen, exportable in the format your compliance and audit teams already expect.
Built around the promotion gate, not the demo
Most inference platforms are optimised for getting a model live quickly. Kuvai is optimised for the harder requirement fintech and healthcare teams actually have: proving a model was evaluated properly before it touches real data.
That means the boundary between an experiment and a production decision is not a convention your team enforces by hand — it is something the platform enforces for you.
- Sandboxed experimentationResearchers work against synthetic or de-identified data by default, with a deliberate, audited step to request real data access.
- Promotion, not deploymentMoving a model to production is a reviewed action with a record, not a config change anyone with access can make.
- Built for examinersEvery artefact a regulator asks for — training data lineage, evaluation results, access logs — already exists before the question is asked.
sandbox
open
synthetic data
staging
gate: 3/4 passed
de-identified
production
locked
HIPAA / PCI scope
At a glance
| Deployment targets | Your own cloud accounts, with dedicated capacity per environment and per tenant where isolation requires it. |
|---|---|
| Data types | Structured financial data, transaction history, clinical and claims data, and free text under the residency and redaction rules you configure. |
| Evaluation gates | Bias and fairness checks, PHI/PII leakage scans, latency and accuracy thresholds, and any custom test your compliance team requires before promotion. |
| Environments | Sandbox, development, staging and production, each with its own credentials, data boundary and audit trail. |
| Regulatory alignment | Built to support HIPAA, PCI DSS and SOC 2 control requirements; specific certifications available under NDA. |
| Access control | OIDC and SAML single sign-on, SCIM provisioning, and role-scoped access down to the environment and the model. |
Common questions
Can our data scientists experiment freely without a compliance review every time?
Yes, inside a sandbox with synthetic or de-identified data. Requesting access to real data is a deliberate, logged step, not the default.
What happens if a model fails an evaluation gate?
Promotion is blocked automatically and the failure is recorded against that model version. The team can fix and resubmit; the gate cannot be bypassed from the console.
Do you store our PHI or financial data?
No. Kuvai orchestrates environments inside your own cloud accounts. Data stays inside the environment it is licensed for and does not transit Beztly infrastructure.
How is this different from Mauvi?
Mauvi is the general-purpose control plane for serving open-weight models anywhere. Kuvai is a specialised layer on the same primitives, adding the promotion gates, data boundaries and audit trail that fintech and healthcare model development specifically require.
Bring a model that hasn't shipped yet.
The clearest way to evaluate Kuvai is with a model still in development — we'll set up a sandbox, staging and a promotion gate around it in the same session.