Built for regulated institutions, not retrofitted for them

Chefoba is designed around the compliance obligations UK fintech and banking buyers already operate under, not adapted to them after the fact.

Regulatory framing

FCA Consumer Duty and UK GDPR

FCA Consumer Duty

Consumer Duty requires firms to be able to evidence good outcomes for customers, including where automated decisioning is involved. Chefoba's subgroup auditing and automated evidence generation are built to give firms a defensible, documented answer to "can you show this model treats customer segments fairly," rather than an aggregate accuracy number.

UK GDPR

Because raw customer data never leaves the originating institution, cross-institutional model training does not require transferring personal data between organisations. Only model parameters move between participants and the coordination layer. This materially simplifies the data protection analysis compared to approaches that require pooling customer data into a shared dataset.

Deployment

Data residency and deployment options

Where your data and infrastructure sit is a compliance decision as much as a technical one.

Cloud

Managed cloud deployment with standard regional hosting controls, suited to institutions without stricter residency or outsourcing requirements.

Private cloud & on-premises

For institutions under outsourcing regulation, data residency requirements, or internal policy that requires infrastructure control, Chefoba deploys inside your own private cloud or on-premises environment.

Named feature

Automated regulatory evidence generation

This is a specific, real feature of the platform, not a general compliance claim.

What it does

Every training round, aggregation decision, trust-score change, and policy update in Chefoba produces a structured evidence record automatically: what happened, when, under which policy, involving which participants, and with what outcome. That record is built for compliance review and regulator inquiry from the moment it's created, rather than assembled retrospectively when a request comes in. It's designed to answer the question "show me how this model was trained and governed" without a manual reconstruction exercise.

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We're onboarding a limited group of fintech and banking partners before general availability.

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