Kepler builds verifiable AI for financial services with Claude: 26M+ filings indexed, audit-ready answers

Kepler, founded in 2025 by ex-Palantir engineers Vinoo Ganesh and John McRaven, has built a financial research platform that prioritizes auditability over black-box AI. After speaking with 147 financial firms and hearing "How am I supposed to trust something I can't audit?", they designed a system where Claude acts as the reasoning layer, but all outputs are verified against deterministic infrastructure.

Key architectural decisions

• Scale: Indexed 26M+ SEC filings, 50M+ public documents, and 1M+ private documents across 14,000+ companies and 27 global markets in under three months. Stack: AWS, Rust, Python, containers for orchestration.
• Context engineering: Claude is given precisely defined tasks with structured domain knowledge, definitions, and hard boundaries on what to resolve vs. escalate. The model is treated as one stage in a pipeline, not the whole system.
• Multi-step reasoning: For queries like inventory days outstanding over 8 quarters, Claude must decompose the question, pull correct fiscal periods, handle restatements, and apply the right formula. On benchmarks, all frontier models performed comparably on simple queries, but only Claude held plans together across 4-5+ interdependent steps without dropping constraints.

Handling ambiguity

Kepler found that Claude stops and asks for clarification when a term has multiple meanings in finance, whereas other models silently pick one interpretation. "That behavior matters more than any benchmark score," said CEO Vinoo Ganesh. "One wrong assumption early in a financial analysis breaks everything downstream."

Trust layer design

The platform combines Claude's reasoning with deterministic infrastructure that validates every number to the exact filing, page, and line item. This separation of interpretation from computation ensures that even if the model makes a mistake, the verification layer catches it before it reaches the analyst.