The context
Investment research is a data business. Before a hedge fund or asset manager can test an idea, its team has to source data from several vendors, clean it, align it and load it into the tools it uses for analysis. Then the idea has to be coded, tested against history and compared with alternatives.
At most firms, those steps live in different systems and different people’s spreadsheets. The thinking takes hours; the plumbing takes weeks.
The challenge
The goal was to collapse that cycle. Investment teams needed one environment in which they could describe a strategy idea, have it built and tested against clean data, and review the results, without waiting on engineering for every step. It had to meet institutional standards: reliable data, reproducible results, and integration with each firm’s own systems and data.
What we did
We designed and built an AI operating system for investment teams, bringing four capabilities into a single platform:
- Strategy generation. Turning a research idea, expressed in plain language, into a testable strategy.
- Backtesting. Testing strategies against historical data in a consistent, reproducible way.
- Multi-vendor data. Connecting and aligning data from several providers, so teams start from a clean, common view.
- Workflow orchestration. Automating the repetitive steps between idea and result.
For institutional clients, the platform connected directly to their own databases, so research ran on the firm’s data inside its own environment.
The result
The platform became a multi-million-dollar venture. It was demonstrated to more than 60 hedge funds and asset managers, used or validated by funds managing from $500 million to more than $150 billion, and signed institutional clients with direct database integration. The work was covered by With Intelligence (S&P Global), the leading intelligence provider to hedge funds and institutional allocators.
Why it worked
- It removed the plumbing, not the judgement. Investment professionals kept control of ideas and decisions; the platform removed the weeks of data and engineering work between them.
- It met institutional standards from the start. Reproducibility and integration with each firm’s own data were designed in, not added later.
See our financial institutions practice.