AI advisory for regulated financial services.
Rizmi Labs helps banks, insurers, and Fortune 500 enterprises ship trustworthy generative AI — from regulatory-grade model risk management to LLM implementation, governance, and evaluation.
10+
Years inside a top US bank
4+
Years leading enterprise GenAI
SR 11-7
Model risk native
Peer-reviewed
Published AI research
Built on experience at
- Top-4 US bank
- Enterprise AI R&D
- SR 11-7 / Federal Reserve frameworks
- TOGAF · Zachman
Four practices, one focus: AI you can defend.
Each engagement is built around a deliverable a regulator, a board, or an engineering leader can actually use.
Generative AI & LLM Implementation for Financial Services
Production-grade LLM systems built for regulated environments — from RAG to fine-tuned models to agentic workflows.
AI Model Risk Management & Regulatory Compliance
SR 11-7-aligned model risk frameworks that hold up under examination — adapted for the realities of generative AI.
Enterprise AI Governance, Architecture & Strategy
Board- and C-suite-ready AI strategy: target architectures, governance, talent, and the roadmap to get there.
AI Evaluation, Fine-Tuning & LLM Customization
Rigorous evaluation harnesses and fine-tuned models for product teams that need their LLM features to actually work.
An operator's view of regulated AI.
A decade inside one of the largest US banks, four years building enterprise GenAI from zero, and published research that informs how we evaluate the systems we ship.
Inside the bank, not outside
Ten years inside a top-4 US bank, working across fintech and enterprise architecture. We know what an examiner reads, what a CRO signs, and what an engineer can actually build.
SR 11-7 native
Active work on agentic AI for regulatory compliance. Our model risk frameworks are written for examiners and for engineers — not chosen between them.
Hands-on with models
LoRA, fine-tuning, prompt engineering, synthetic data, evaluation harnesses. We build, we don't only advise.
Published research
Peer-reviewed work on hallucination detection and class imbalance — the rigor we apply to every engagement.
Case studies
Anonymized engagements across regulated banking, insurance, and enterprise software.
Extending SR 11-7 for an LLM-powered compliance assistant
A US regional bank introduced a retrieval-augmented LLM into its compliance review workflow. We built the validation framework that let it pass internal model risk review.
- Validation cycle
- 4 weeks
- Documented test cases
- 320
Board-ready AI strategy for a Fortune 500 insurance carrier
Six months from a slide deck to three live use cases, a governance operating model, and an AI roadmap the board approved.
- Engagement length
- 6 months
- Use cases shipped
- 3
Notes from the field.
Practical writing on regulated AI, model risk, and what's actually working in production.
SR 11-7 meets generative AI: what actually has to change
The Federal Reserve framework was written for traditional models. Here is what holds, what breaks, and what to add when you bring it to LLMs.
Read insightTreat the evaluation harness as a product, not a script
The teams that ship reliable LLM features invest in evaluation infrastructure. Here is what that looks like in practice.
Read insightAgentic AI in regulated workflows: what to allow, what to forbid
Tool use is where most of the real value of agentic systems lives — and where most of the regulatory risk does too.
Read insightBring us a problem worth getting right.
A 30-minute discovery call, no slides. We'll tell you whether we're a fit — and if we're not, who is.