Industry
AI for finance and financial services
Run analysis, monitor risk, and surface insights across portfolios — with sensitive data kept fully inside your environment.
Financial institutions sit on enormous volumes of sensitive data and operate under rules that punish mistakes. That combination is exactly why general-purpose assistants struggle here — and why a private, context-aware system is the right approach.
Soren builds AI that runs analysis, monitors risk, and surfaces insight across portfolios and documents, deployed inside infrastructure you control so client and market data never leaves your perimeter.
Why Soren
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Data stays inside your perimeter
Deployed in your cloud tenant, VPC, or on-premise — client records and positions never leave your control.
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Traceable answers
Every output is grounded in your authoritative sources, with a citation back to the record it came from.
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Built for the rules you operate under
Designed against GLBA, SOC 2, and sector expectations from bodies like the SEC and FINRA.
Where AI fits in financial services
The highest-value workflows are usually the document-heavy and analysis-heavy ones: summarizing filings and research, monitoring risk and exceptions, drafting first-pass memos, and answering questions against policies and systems of record.
Each of these works best when the system is grounded in your own data and tuned to how your analysts actually reason — not a generic model guessing from public text.
Designed against
- GLBA
- SOC 2
- SEC / FINRA expectations
- ISO 27001
Frequently asked questions
- Can we use AI in finance without sending data to a third party?
- Yes. A private deployment keeps your models, data, and infrastructure inside your own cloud tenant, VPC, or on-premise environment, so client and market data never leaves your perimeter and no third party trains on it.
- Which regulations apply to AI in financial services?
- Financial services fall under GLBA and sector rules from bodies like the SEC and FINRA. Most institutions also expect SOC 2 and often ISO 27001 from vendors, with the NIST AI Risk Management Framework adding AI-specific governance on top.
Putting private, context-aware AI to work in a regulated environment? We should talk.
Book a demo