Private AI · Finance
Client records, credit files and trading data are the last things you want in a public AI tool. We supply right-sized on-premise AI hardware, and our partners deploy open-weight LLMs on it inside your own data centre, so your data never leaves your building.
The risk
Staff paste client, account and deal data into public AI chat tools (shadow AI)
Cloud AI services can count as outsourcing and add third-party risk reviews
Customer data may be processed or cached in other countries
Model behaviour and data handling are hard to audit inside a third-party service
Use cases
Open-weight LLMs deployed by our partners, working on your documents inside your network.
01
Ask questions across internal policies, circulars and procedure manuals, and get answers that cite the source paragraph. Compliance and operations staff find the rule they need without sending documents to an outside service.
02
Summarise credit applications, financial statements and KYC documents, and flag missing items for an analyst to check. The files stay on your own storage and are processed on your own GPUs.
03
Draft credit memos, board papers and client letters from internal data and approved templates. People review and sign off every draft; the model does the first pass.
04
Give agents a private assistant that searches product terms and past cases while they talk to customers. Call notes and customer details never go to a public AI provider.
05
Give developers code completion and review help on hardware you own, so proprietary source code and internal APIs are not shared with an external service.
Compliance context
On-premise AI helps support compliance by keeping processing on hardware you control. It does not replace your own legal and compliance assessment.
Banks must obtain the Central Bank’s prior non-objection before outsourcing any material activity, and must keep ownership of data given to an outsourcing provider. Running AI on your own hardware can reduce how much AI processing is outsourced in the first place.
Guidelines for adopting enabling technologies, issued jointly by the CBUAE, SCA, DFSA and FSRA, ask financial institutions to assess the materiality of cloud arrangements and keep them auditable, secure and protected from unauthorised access. An on-premise AI platform keeps that evidence in-house.
Firms in the Dubai International Financial Centre follow their own data protection law, supervised by the DIFC Commissioner of Data Protection. Transfers of personal data outside the DIFC need an adequate destination or appropriate safeguards, so on-premise AI keeps that analysis simpler.
Entities in Abu Dhabi Global Market follow the ADGM Data Protection Regulations 2021. Its guidance covers impact assessments for high-risk processing, security of processing and safeguards for international transfers, all of which are easier to evidence when AI runs in-house.
This is general information, not legal advice. Rules change and depend on your licence, location and data. Ask your legal or compliance adviser which rules apply to your organisation.
Recommended starting point
Typically 25–200 users, running open-weight LLMs up to ~70B parameters. Every bundle is quoted to your users, documents and site.
Most requested
Typically 25–200 users
Shared AI for a whole department, with several use cases on one platform.
FAQ
Request a quote
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