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Practice 01
Generative AI & LLM Implementation for Financial Services
Production-grade LLM systems built for regulated environments — from RAG to fine-tuned models to agentic workflows.
Most banks and insurers want what big-tech has in production: retrieval, document generation, and agentic assistants that meaningfully reduce cycle times. We design and implement those systems with the controls a Fed examiner would actually accept — model documentation, evaluation harnesses, observability, and human-in-the-loop review baked in from day one.
Outcomes
What you walk away with
- Working LLM proofs-of-concept on regulated workloads (compliance, underwriting, claims, KYC, internal knowledge)
- Reference architectures for RAG over policy and procedure libraries with full citation provenance
- Agentic assistants for analysts, with audit trails, tool restrictions, and refusal behaviors
- Fine-tuned or LoRA-adapted models on customer-specific corpora with reproducible eval reports
Engagement shapes
How we work together
- 4–8 week LLM use-case discovery and prioritization sprint
- 12–16 week production pilot of one or two prioritized use cases
- Embedded technical lead support during scale-out
Audience
Who this is for
Regional and mid-size banks without in-house GenAI teams
Credit unions and alternative financial institutions
Fintech startups scaling AI capabilities
Insurance carriers integrating AI into underwriting and claims
Next step
Ready to scope a genai for financial services engagement?
A 30-minute call gets us to a yes, no, or a clear next step.
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