Signals

Emerging Fintech AI Agent Architecture with Multiple Foundational Models

A common challenge faced by many Fintech teams is keeping up with rapidly emerging AI models and effectively applying them to financial use cases.

As a Silicon Valley startup, our team went through this as well. Despite having a technical background, it took us some time to understand new foundational AI models, recognize their strengths, and build a few standard Fintech workflows.

The aim of this experiment was to choose the best architecture in terms of performance, agentic actions, and execution costs for running Fintech applications.

Our team builds AI Agents for Fintech customer support. Answering user inquiries, product onboarding, and performing basic Fintech workflow actions automatically.

These are ideal AI agentic actions, which involve not only looking up documents for the right answers but also doing logical reasoning, to perform human-level tasks.

We evaluated leading industry models such as OpenAI GPT-4o, Deepseek R1, and Claude 3.5 Sonnet to use as foundational models for our AI Agents.

Initial results look promising—Fintech AI Agents can now perform cascaded tasks using multiple underlying foundational models.

A few highlights:

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