Many banks, insurers and government entities can’t send regulated data to a public AI service. Data Semantics deploys AI agents and assistants in your data center, private cloud or in-country tenant, with the controls and audit trail your regulators expect.
Your regulated data
Core bankingKYC & loan filesPolicies & circularsData warehouseCRM & ERPInside your boundary
Nothing used to train models
Deploy in
Your Azure tenant, in-countryPrivate cloudOn-premisesData Semantics designs and deploys private AI for banks and other regulated organizations. We choose suitable models, connect them to your core systems and documents, and add guardrails, identity-based access and full audit logging, all inside an environment you control. Available on engagement.
Banks and regulated organizations want the same productivity gains from AI as everyone else, but cannot send customer data, credit files or regulator correspondence to a shared public service. Private AI brings the models to the data, so risk and compliance can say yes.
We match the deployment to your regulation, risk appetite and infrastructure. All three are available on engagement.
AI runs in your own Azure tenant in an in-country region, with private networking and no data shared with model providers.
A dedicated environment with private model hosting, for organizations that need stronger isolation than a shared cloud.
Open-weight models running on infrastructure in your own data center, connected directly to core systems.
Starting points we see most often in banking and other regulated sectors.
Assistants that gather customer data, summarize exposure and draft credit memos for officers to review.
Agents that prioritize accounts and prepare or make customer contact, within banking rules.
Staff ask questions of regulations, circulars and internal policies and get cited answers.
Agents that read, check and extract data from KYC, trade finance and loan documents.
Designed with your technology, risk and compliance teams from day one.
Regulation, data classification, infrastructure and priority use cases.
Deployment option, model choice, security, access and audit design.
Deploy one assistant or agent on real data and test it with risk and compliance.
Run it under our AI Agent Operations service and add use cases.
Agents act only within set limits, with people approving anything consequential.
Every use case is tested against real cases and red-team scenarios before go-live, and after every change.
A multi-year enterprise data warehouse on the FSLDM banking data model, with analytical APIs and downstream integrations across the bank.
AI agents remind, call, track promises to pay and reconcile, within contact rules. The same approach can run privately inside a bank.
About private AI
For many focused business tasks, such as summarizing, drafting, extracting and answering from your own documents, well-chosen private models perform very well. We test each use case on real cases before go-live.
Data Semantics deploys open-weight models such as Llama, Mistral, Qwen and DeepSeek on your own infrastructure, and private deployments of commercial models such as Azure OpenAI in an in-country region. Each model is tested on real cases for the use case and can be replaced as better ones arrive.
Deployment and residency
Yes. Open-weight models can run on infrastructure in your own data center. We help choose models and hardware that fit the use cases, and design the environment around your security standards. On-premises deployment is available on engagement.
By deploying in an in-country cloud region or on infrastructure you host in-country, keeping data, models and logs within the required boundary. We design with your compliance team against the rules that apply to you.
It can be. Running Azure OpenAI in your own tenant, in an in-country region with private networking and no data shared for training, is the most common private AI option Data Semantics deploys. Organizations that need more isolation use a private cloud or on-premises open-weight models.
Security and compliance
No. In a private deployment, your data, prompts and outputs stay in your environment and are not used to train external models.
The AI uses your existing identity and permission model, so each user can only reach the data their role allows. Every prompt, answer and agent action is logged with the user, data accessed and any approval, retained for audit and fed into your security monitoring. Data Semantics designs logging and retention with your risk and compliance teams.
Use cases and getting started
Data Semantics most often starts banks with credit memo drafting, collections and recoveries, compliance and policy questions with cited answers, KYC and trade finance document processing, and assistants that help service staff answer customers.
Yes. Data Semantics has worked with core banking platforms such as Temenos T24 and iMAL and connects AI to core systems, documents and the data warehouse through approved interfaces.
Data Semantics starts with requirements: regulation, data classification, infrastructure and priority use cases. We then design the deployment, pilot one assistant or agent on real data with your risk and compliance teams, and expand from there. After go-live it is run by your team, ours or both. Our AI Agent Operations service covers testing, monitoring, model updates and audit reporting for private deployments too.
Tell us your regulatory constraints and the first use case you have in mind. We’ll show you how it can run inside your own environment.
