Complete primary source coverage. Multi-modal understanding. Evidence-driven analysis. Minimise hallucinations. The infrastructure that makes reliable AI possible in capital markets.
AI agents can only be as reliable as the data they can access - there needs to be a Bloomberg for LLMs.
Financial data is fragmented - regulators, IR sites, data vendors - with document relationships buried in institutional memory. We've built comprehensive coverage of every filing and disclosure, mapped with the ontology that shows how documents relate across time and type. AI agents using Canon know that Q3 supplements explain anomalies in the 10-Q, that guidance revisions cascade through subsequent filings, that sector-specific disclosures hide in different schedules.
Text-only AI is functionally illiterate in finance - reliable AI must also be able to understand charts, diagrams, tables, and footnotes
Financial truth lives beyond text - in slide deck KPIs, trend-revealing charts, and footnotes that contradict narratives. Unlike generic parsers, Comprehension is trained specifically for financial documents. It distinguishes revenue waterfalls from bridge charts, preserves the relationship between table headers and their data, and catches when adjusted metrics in the presentation deviates from GAAP filings. The result: structured data from every document element, enabling LLMs to have comprehension parity with analysts.
Semantic search is not good enough - retrieval must be designed from ground up for how finance actually works.
Financial queries like 'trace working capital over 5 years' demand multi-hop reasoning across 30+ documents - finding specific line items, management commentary, and presentation slides across 20 quarters. Index, built on Canon and Comprehension, isn't keyword search - it's retrieval infrastructure that handles temporal patterns ('when margins deteriorated'), semantic concepts ('off-balance sheet obligations'), and peer analysis ('SG&A benchmarks') across millions of documents. This precision - whilst maintaining context across 10-year horizons and complex document chains - enables AI agents to move beyond surface-level analysis to institutional-grade research.
The most dangerous AI is one that's confidently wrong about facts. Finance demands evidence-first thinking.
Analyst agents are trained on financial workflows - following verifiable processes: decomposing queries into evidence requirements, systematically gathering data from primary sources via Index, and building bottom-up conclusions with full citation trails. Run-time verification catches numerical inconsistencies and source-claim mismatches before they reach users. Continuously benchmarked against thousands of real analyst research tasks, analyst agents don't just mimic sophisticated analysis - they execute the disciplined, evidence-first methodology that builds reliable analysis.
Real-time monitoring and continuous validation ensure these metrics remain industry-leading.