LiveDocs

Huge congrats on the update and launch @arslnb !

Early days can be wild with numbers are you finding your conversions line up with expectations so far, or does it feel like something isn’t clicking the way you hoped?

That’s often where early traction decisions matter most. :)

This makes life much easier for data scientists 👍👍

"No SQL, no dashboards, no setup" is the right democratization approach. The real barrier to data insights isn't tools – it's the translation layer between business questions and technical queries.

Interested in how you handle data quality edge cases. When users ask ambiguous questions ("show me growth"), does the agent surface assumptions or clarify intent before generating charts? That context awareness would be key for trust in production.

The plain English querying is interesting - does it handle complex aggregations well, or is there a learning curve for getting the right phrasing?

From my experience, the biggest bottleneck with data tools isn’t lack of power — it’s the friction between questions and answers. Most teams don’t want to build dashboards or write SQL just to explore an idea.

I like how LiveDocs keeps the workflow conversational but still allows you to go deeper with notebooks when needed. That flexibility is important — especially for founders and ops teams who want quick clarity first, then depth.

Curious how teams handle versioning or sharing insights across stakeholders once analyses evolve. Overall, this feels very thoughtfully designed. Nice work 👏

Congrats on the launch! this is really cool!

I'm a Senior Data Scientist in Fintech (lending/capital markets), I’ve spent some time building scrappy agentic workflows with aws to answer executive questions like 'What was the loan churn rate trending MoM before the Oct spike?' The friction usually comes from the 'agent' losing context on our specific schema or failing at complex temporal joins.

The Livedocs UI looks incredibly clean, and the 'Notebook + Agent' hybrid approach I can definitely see myself using

From a Fintech perspective, I have two questions:

1. Privacy & Infrastructure: Since we deal with sensitive credit data, what’s the 'Enterprise' story? Can this be self-hosted or deployed within a VPC (AWS/Azure) to bypass the legal hurdles of third-party data hosting?

2. The Databricks Differentiator: We currently use Databricks with Copilot for SQL/Dashboards. How does Livedocs handle the 'governance' layer differently? Is it meant to sit on top of our existing Unity Catalog, or is it a complete alternative for the presentation layer?

I’d love to stress-test this with some of our non-PII datasets. If you're looking for more technical feedback or a user interview with a DS in the lending space, feel free to reach out at cgusti@outlook.com

Upvoted and watching closely!"

This looks super useful for quick insights. What’s the stance on data privacy? Is the uploaded data used to train your models, or does it remain completely isolated within the session?