Asghar Shah

Really like the focus on context. In practice, we’ve seen context fragment fast across warehouses and tools. Curious how you're handling consistent data access vs each workflow stitching it together? We ended up exposing datasets as APIs so agents operate on a clean, shared interface. Without that, reliability drops quickly at scale.

Nice direction. From our side, data access becomes the bottleneck quickly when building multiple apps. Curious how you're handling reusable datasets vs each app connecting directly to sources? We ended up exposing structured data as APIs so apps and agents can share the same data layer. Otherwise things get fragmented fast.

Interesting direction. In financial workflows, data consistency becomes the real bottleneck fast, especially when combining portfolio data with external market signals. Curious how you're handling structured data across these layers (user portfolios, market data, custom signals)? We kept running into issues stitching sources together, so we exposed clean datasets as APIs for agents to work...

Interesting direction. From our side, AI testing breaks quickly without consistent data. Curious how you're handling test data sources? Mocked vs real datasets? We ended up exposing structured data as APIs so agents can run against controlled inputs otherwise things get off fast at scale.