Building AI Systems for Capital Markets

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Key takeaways: The "demo versus product" gap: An AI demo is judged on its best day, but an institutional-grade product is judged on its worst. Building reliable financial tools requires rigorous, auditable grounding frameworks to avoid hallucinations and ensure every output can be traced back to a verified source. Don't bet against the model: Developers should avoid wasting resources on complex workarounds for temporary constraints on models that are evolving rapidly. Instead, they should focus on elements that models cannot natively learn: details specific to the firm, such as proprietary data or how data sets are connected to each other. Tackling legacy constraints: Successful innovation…