Sibli makes an institutional investment process legible. Forecasts, research and financial models become structured data, which then supports scenario analysis, dynamic consensus against the market, company scoring, controversy monitoring and alpha capture across a whole portfolio.
Tracking forecast accuracy is the uncomfortable and valuable part. Investment teams produce numbers constantly and systematically fail to check afterwards whose numbers were right, because the models live in individual spreadsheets and nobody collects them. Turning that into a record makes the process assessable, which is the argument for the whole platform.
SR AI deploys to private cloud with API and Model Context Protocol support, using causal discovery mapping and financial model parsing underneath. No pricing is published, it assumes an institutional process with models worth ingesting, and measuring forecast accuracy is only useful in an organisation prepared to act on what it finds.
