Structured knowledge base for investment research technology.
For AI to generate durable investment alpha, it must be embedded directly into the "System of Record" (the RMS) rather than accessed via standalone chatbots. Integrating AI into the core workflow prevents data fragmentation, ensures...
The most effective AI strategies in asset management prioritize "Frontier Models" over small, fine-tuned models, and utilize a "Bring-Your-Own-LLM" (BYO-LLM) architecture to ensure data security. To avoid hallucinations, firms should ground AI responses in their...
Investment firms use AI to process large volumes of unstructured information, such as earnings transcripts, filings, emails, meeting notes, and research reports, and turn it into structured, searchable, and decision-ready insight. Rather than replacing analysts,...
Unstructured data, such as earnings call audio, PDF slide decks, broker emails, and meeting notes, comprises approximately 80% of all data available to investment firms. To make this data usable for comparative analysis, firms use...
Integrating third-party data into a research platform eliminates the "context switching" that reduces analyst productivity. Best practices involve using API connectors to automatically ingest emails and research reports ("Ingestion"), while linking live external market data...
Under regulations like SFDR (EU), the UK Stewardship Code, and AASB S2 (Australia), asset managers must provide evidence of "Active Ownership." It is no longer sufficient to simply log a meeting; firms must document the...