Sibli transforms investment views into an insight generating data asset. It addresses cognitive overload from processing massive information volumes, isolated models, and lack of historical traceability. The platform centralizes forecasts with access control and auditability, connects Excel forecasts and research into a unified structured asset, and provides full auditable access to track forecast quality and monitor drift. It ingests Excel forecasts without templates, maps cause-and-effect relationships via causal discovery, and orchestrates AI-powered research through text parsing and agent chains. Deployment occurs in private cloud with API connectivity or Model Context Protocol integration. Data integration covers internal forecasts, broker data, text inputs like news and transcripts, and vendor datasets.
Ingests Excel forecasts without needing templates or rework.
Provides secure private cloud deployment in user's VPC.
Offers full audit trails for forecasts and assumptions.
Integrates proprietary and external data sources seamlessly.
Requires integration into existing workflows via API or MCP.
Deployment limited to private cloud environments.
Depends on user-provided forecasts and research as inputs.
Customization aligns to specific client methodologies.
