Bigdata.com

Danske Bank is shifting from centralised AI programmes toward team-level workflows, with Financial Markets, Asset Management, and Group Risk each standing up their own tools. That's real progress - but fragmented content and inconsistent quality controls don't make AI more capable, they make it harder to trust. Teams that build at that level need answers they can actually trace back to a source, not just another tool to govern. Bigdata by RavenPack gives teams at Danske Bank a single, structured intelligence layer - premium content and governed retrieval, built to run natively on AWS.

Macquarie is running AI initiatives across systematic investing, discretionary research, and centralised platforms. That's real progress - but fragmented data subscriptions and disconnected signals don't make AI more capable, they make it harder to trust. Data science, systematic, and AI teams need answers they can actually trace back to a source, not just another integration to manage. Bigdata by RavenPack gives those teams a single, aggregated intelligence foundation - structured, cited financial data delivered as AI-ready data or infrastructure, plugged directly into the environments already in use.

Most AI research tools just hand your model entire documents and let it figure out what matters. Bigdata by RavenPack works differently - it retrieves only the specific, sourced excerpts that answer the question, which in practice cuts token usage by around 90% and cost by roughly 100x. For Danske Bank's Financial Markets, Asset Management, and Group Risk teams, that means research grounded in FT, FactSet, StreetAccount, Finwire, and more - every result sourced, auditable, and ready for regulatory scrutiny. It connects directly via MCP or API, running on the AWS infrastructure already in place.

Most AI research tools just hand your model entire documents and let it figure out what matters. Bigdata by RavenPack works differently - it retrieves only the specific, sourced excerpts that answer the question, which in practice cuts token usage by around 90% and cost by roughly 100x. For Macquarie's systematic, quant, and AI infrastructure teams, that means one queryable layer across FT, S&P, MT Newswires, Preqin, FactSet, and more - not five separate vendor contracts. It connects directly via MCP or standard API, extending what's already built rather than replacing it.

Citi has rolled out generative AI to around 182,000 employees through Stylus Workspaces. That's a real commitment to AI at scale, but using AI broadly and trusting it with Markets, Banking, or Wealth research are different problems. Research teams at that scale need answers they can actually trace back to a source, not just plausible-sounding output. Bigdata by RavenPack gives banking, markets, and wealth teams a single layer of structured, cited financial data that plugs directly into the AI tools already in use, including the models running inside Stylus Workspaces.

Most AI research tools just hand your model entire documents and let it figure out what matters. Bigdata by RavenPack works differently: it retrieves only the specific, sourced excerpts that answer the question, which in practice cuts token usage by around 90% and cost by roughly 100x. For teams in Markets, Banking, and Wealth, that means research grounded in over 200 licensed sources, 2.5 million+ transcripts, and RavenPack's 25 years of entity-resolved data, not open-web retrieval. It connects directly into Claude, ChatGPT Enterprise, or Copilot, so there's no new platform to stand up.