Company GPT for Financial Institutions

All data stays within your infrastructure — we deploy on-premise or in your private cloud with no data sent to external LLM providers. The system uses encryption at rest and in transit, integrates with your existing SSO and identity management, and maintains complete audit logs of every query and response. We follow financial industry security standards including SOC 2 and GDPR requirements.

We support both deployment models. On-premise deployment keeps all data and processing within your data center, which is preferred by most financial institutions for regulatory and security reasons. Private cloud deployment on AWS, Azure, or GCP is also available with VPC isolation and no public internet exposure. The architecture is identical in both cases — only the infrastructure layer differs.

The system integrates with virtually any structured or unstructured data source: SharePoint, Confluence, internal wikis, document management systems, relational databases, PDF libraries, email archives, and ticketing systems. Each source gets a tailored ingestion pipeline optimized for its format and update frequency. We prioritize sources during the knowledge audit phase based on business value and usage patterns.

Compliance documents receive special treatment in the pipeline. We implement version tracking so the assistant always references the current policy version, flag outdated documents, and include source citations with every response. Role-based access ensures only authorized personnel can query sensitive regulatory content. The audit trail records every compliance-related query for regulatory examination purposes.

Our RAG architecture grounds every response in your actual documents — the model retrieves relevant passages before generating an answer, and citations are included with every response. We implement confidence scoring, so when the system cannot find a reliable answer it says so rather than guessing. A re-ranking layer ensures the most relevant documents are prioritized, and continuous monitoring flags low-confidence responses for human review.

Yes. We build integrations with the communication and productivity tools your teams already use. Common integrations include Microsoft Teams, Slack, internal web portals, and ticketing systems. The assistant can be accessed through chat interfaces, browser extensions, or embedded widgets — meeting your employees where they already work rather than requiring them to switch to a new tool.

Update frequency is configurable per data source. Critical sources like compliance policies can be synced in near real-time, while less frequently changing repositories might update daily or weekly. Our ingestion pipelines detect new, modified, and deleted documents automatically. You control the schedule, and the system handles incremental updates without requiring full re-indexing.

A focused Company GPT covering a defined set of knowledge sources typically takes 8–14 weeks from knowledge audit to production deployment. Broader implementations spanning multiple departments and data sources range from 3–6 months. Cost depends on deployment model, data volume, and integration complexity. We start every engagement with a scoped discovery phase so you have clear budget and timeline expectations before committing to a full build.