I Made 5 AI Models Argue About Stocks. The Disagreements Were More Valuable Than the Answers.

Building a single-model AI system for financial analysis creates blind spots because one model gives one perspective. Nipun AI, an open-source financial analysis platform, addresses this by running multiple AI models (Google Gemini, Cerebras with Llama 3.3 70B, Cohere Command R+) in a 4-phase pipeline where models are explicitly instructed to provide independent verdicts. The disagreements between models often reveal more insight than any single analysis—for example, one model flagging debt-to-equity risk while another focused on revenue growth. A dedicated fact audit layer uses RAG-based verification to classify each claim in generated reports as grounded, speculative, or unverifiable. Testing across 200 analyses found roughly 10% of claims were unverifiable, representing a silent failure risk in single-model systems. The architecture also includes cascading model fallbacks for reliability and cost optimization across free-tier APIs.