Tensorlake

Challenge

When we joined Tensorlake, the product was initially built for engineers — an API-first platform intended to be deployed directly into user infrastructure. While this approach offered speed and flexibility, it became clear that we needed more robust user-facing tools to support scale, usability, and broader adoption.

We began by designing and building the Document Ingestion UI, evolving it from a basic upload interface into a powerful, flexible tool that supported:

• Custom schema input for structured data extraction

• Support for advanced features like signature detection

• A schema builder UI so users could create extraction schemas visually instead of pasting raw code

• Collection creation, allowing users to reuse configurations across large document sets

  
  

What did we work on?

Following that, we focused on the Workflows interface — the frontend layer for managing serverless data workflows. This view made it easy to visualize:

• The sequence of functions and tasks in a pipeline

• The output of each task

• Task status and errors during execution

  
  

We shipped these features through continuous iteration, grounded in real user feedback and tight cross-functional collaboration.

Business Impact

These product improvements significantly enhanced how customers interacted with Tensorlake and led directly to meaningful business outcomes:

• Helped close $$$$+ in enterprise deals

• Supported acquisition of 5–7 new high-value customers

• Contributed to Tensorlake’s strong competitive positioning in the fast-growing unstructured data infra space