First, I love this concept and I think your demo is great! Collaboration with existing harnesses makes a ton of sense. Just had a conversation with some folks in the non-tech world raving about using Claude.
A few questions:
- How do you think about competing with ChatGPT Canvas or Anthropic's artifacts, when these are shareable, native experiences in their products where users already work?
- Is a "dashboard" limited to analytics or are you trying to expand it to include written reports?
Since teams are connecting MCPs like Granola, Slack, I imagine BitBoard would facilitate sharing demos, PRDs/briefs, or customer reports. This seems like a natural expansion and trivial functionally, so I'm wondering if that's part of the sell now or something you're looking at expanding into as you grow.
Looks cool! It's a lot of work to get a full data stack set up and people are losing interest in stitching the pieces (ETL, warehouse, BI) together.
> Agents made bad inferences because they had no context on the business
We've been working on this since before the chatgpt launch.
We started with a semantic layer since there were already good open source options and LLMs at the time were good at writing the JSON (remember function calling?) to run a semantic query.
But as LLMs have gotten smarter and people wanted to do more data work in agents, we found we needed something more flexible, so we built an "Ontology" that lets you store all the terms you use in your company and connect them to the data points (e.g. tables, columns, metrics) that matter.
Highly rec going after a specific vertical - healthcare might be the right spot given your experience. Why did you use DuckDB instead of CockroachDB/Snowflake?
Our outreach is vertical-specific, and healthcare is indeed on the list! But what we learned working a vertical is that the primitives underneath (shared queries, permissions, caching, refresh semantics) repeat across industries.
We use DuckDB internally because we like its ergonomics - it's flexible, runs well in memory, manages a lot of file structures under the hood, but we do work with Snowflake (and Databricks and other warehouses) as well.
A few questions:
- How do you think about competing with ChatGPT Canvas or Anthropic's artifacts, when these are shareable, native experiences in their products where users already work?
- Is a "dashboard" limited to analytics or are you trying to expand it to include written reports?
Since teams are connecting MCPs like Granola, Slack, I imagine BitBoard would facilitate sharing demos, PRDs/briefs, or customer reports. This seems like a natural expansion and trivial functionally, so I'm wondering if that's part of the sell now or something you're looking at expanding into as you grow.
> Agents made bad inferences because they had no context on the business
We've been working on this since before the chatgpt launch.
We started with a semantic layer since there were already good open source options and LLMs at the time were good at writing the JSON (remember function calling?) to run a semantic query.
But as LLMs have gotten smarter and people wanted to do more data work in agents, we found we needed something more flexible, so we built an "Ontology" that lets you store all the terms you use in your company and connect them to the data points (e.g. tables, columns, metrics) that matter.
https://www.definite.app/blog/ontology-ai-analytics
We use DuckDB internally because we like its ergonomics - it's flexible, runs well in memory, manages a lot of file structures under the hood, but we do work with Snowflake (and Databricks and other warehouses) as well.