Finance-Focused Generative AI Strategy
Budget: ₹400 – ₹750 INR
I’m building a suite of custom language models for the finance sector and need an experienced partner to steer both the technical and business aspects of the rollout. The immediate goal is to integrate commercially available LLMs—think OpenAI, Anthropic, Cohere or similar—then fine-tune or extend them so they speak the language of finance fluently.
Core use-cases we’re targeting include risk assessment, predictive analytics, credit underwriting, data capture automation and score-card creation. The models will ingest and reason over three main data streams: financial transaction data, customer data and live market data. Beyond accuracy, we must meet strict compliance and explainability requirements typical of regulated environments.
Here’s what I need from you:
• A practical roadmap that weighs build-versus-buy choices, cloud versus on-prem deployment and the right mix of OSS and proprietary models.
• An end-to-end technical design covering data pipelines, retrieval-augmented generation, guardrails, monitoring and model governance.
• Guidance on team structure, milestone phasing and KPIs so leadership can track ROI from proof-of-concept through production.
• Optional hands-on prototyping or code reviews in Python, LangChain, Azure OpenAI or comparable stacks if time allows.
You’ll work directly with me and a small data engineering team. If you’ve shipped LLM solutions in finance—or another heavily regulated industry—let’s talk.
Core use-cases we’re targeting include risk assessment, predictive analytics, credit underwriting, data capture automation and score-card creation. The models will ingest and reason over three main data streams: financial transaction data, customer data and live market data. Beyond accuracy, we must meet strict compliance and explainability requirements typical of regulated environments.
Here’s what I need from you:
• A practical roadmap that weighs build-versus-buy choices, cloud versus on-prem deployment and the right mix of OSS and proprietary models.
• An end-to-end technical design covering data pipelines, retrieval-augmented generation, guardrails, monitoring and model governance.
• Guidance on team structure, milestone phasing and KPIs so leadership can track ROI from proof-of-concept through production.
• Optional hands-on prototyping or code reviews in Python, LangChain, Azure OpenAI or comparable stacks if time allows.
You’ll work directly with me and a small data engineering team. If you’ve shipped LLM solutions in finance—or another heavily regulated industry—let’s talk.