Back to HN Launch HN: BitBoard (YC P25) – Analytics Workspace for Agentsby arcb | 58 points | 25 comments | 2026-06-12 11:58:03 Central Open Source Link | Read Source Here Open on Hacker News PostWe're Connor and Ambar from BitBoard
(https://bitboard.work). BitBoard is an agentic
analytics workspace. We give you the infrastructure and
visualization layer to analyze data with AI.Today, we're
launching dashboards that you and your agents can work
on together. You can connect your coding agent or AI
chat to BitBoard and build live reporting. Here's a
demo: https://www.youtube.com/watch?v=HPl0K565a7c.AI
tools treat data analysis as ephemeral, making it hard
to report or collaborate. Legacy BI tools weren't
intended for AI users, so they bolt on chatbots and
can't offer meaningful control to your agents. Software
can now make far more of a business legible than BI ever
could, but neither legacy BI nor chat bots are built to
handle it.Our original product was AI agents for
administrative tasks in healthcare
(https://news.ycombinator.com/item?id=44237769), but
customers kept pulling us toward their data analysis
problems: queries scattered across disparate sources,
spreadsheets floating everywhere. We kept building
tooling for addressing that, and at a certain point
those tools were becoming our product.We ran into
several problems. Agents made bad inferences because
they had no context on the business. They couldn't be
trusted to make decisions because nothing checked their
work. And anything one agent or one person figured out
was invisible to everyone else. In BitBoard, humans and
agents interact with the same data primitives but get
tools designed for their own work.We're building
dashboards to make the human reading experience better.
These dashboards progressively use intelligence -
starting from code or SQL queries and leading to full
embedded apps. Humans and agents will need to agree on
methods to interpret data, so we're letting both
contribute to canonical sources, entities, and measures
(using your favorite semantic model or ours). Every
answer comes with provenance, and the same call with the
same parameters returns the same number.Looking ahead,
these shared primitives let long-running agents operate
inside a business, and we're building those agents too.
An agent needs a measurable goal and a way to verify its
work. BitBoard gives it both. The agent takes a problem
like a metric drifting or a funnel leaking and figures
out what to do next. Its work becomes datasets,
dashboards, and traces that the team can observe and
sign off on.Technically, we're building a collaboration
engine with isomorphic updates for humans and AI,
columnar analysis (we use DuckDB and Apache Arrow),
grounding and verification infrastructure, and enabling
long running tasks with agent containers and traces. For
agentic work we're big fans of applying LLM judgement to
discover problems, and then generating deterministic
software to automate them.Try it out at
https://app.bitboard.work. (We require an email so we
can set up your account).We're excited about how data
analysis and science can change in the age of LLMs, and
welcome all your thoughts! Commentsbaetylus
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.
| > infakelife
Thanks! Non-OP BitBoard cofounder here. Would love to
hear your thoughts when you get a chance to check it
out.> 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?The flexibility is amazing for
static content and playing around with visuals, the
experience is just more like a whiteboard than a
dashboard. It's hard to do both well in the same
place. For reporting I want live connections,
consistent logic, the ability to trace provenance, and
a more opinionated starting point for the UI.We
started with an extremely flexible surface but there
are just a ton of things you don't want to leave up to
the agent to implement and we gradually layered those
in. It's no fun having to prompt the agent to expose a
"view source" affordance, "run" button, or working
data labels. But it's a lot of fun building whatever
visualization you want and generating a dashboard
without a billion clicks in some SQL-abstraction UI.>
Is a "dashboard" limited to analytics or are you
trying to expand it to include written reports?We
weakly support written reports today (technically
possible with markdown blocks in dashboards for
commentary) but will do more to support them in the
future for exactly the reasons you called out.We
actually built a more notebook-like artifact for this
but cut it to focus on dashboards since they seemed to
be a bigger pain point for users. One-off reports can
be hit or miss with a chat or coding agent today but
static reporting is at least supported with some
effort. Live reporting with connection infrastructure,
provenance, etc. is much harder to pull together.
| rancar2
I do exactly this (and more since my role is much broader
and so is my approach) as a fractional head of product,
data, and operations for multiple companies all in
healthcare (fast growing self-funded to series D/IPOing
soon). I saw your initial launch and felt validated by you
all working on it, and now I'm further validated by the
pivot. I have more work than I can handle, so I'm happy to
share tips. You can find me via a bit of googling my HN
handle or just adding a dot com to the end.
| > infakelife
Appreciate the validation. Would be great to connect
and exchange notes - will reach out directly.
| dennis16384
Nice, I recently did a similar but much simpler thing and
open-sourced it under MIT, maybe some bits and pieced will
be useful https://github.com/eatmydata-org/eatmydataFor
example, MIT-licensed sqlite vector search
extension.Overall, I have a orchestrator - sql coder - js
coder - dashboards, all without backend, running locally
in the browser. It's mostly tested on small analysis and
question answering with Gemini Flash Lite, and the overall
target was speed from question to answer, including data
sharing and waiting.
| > arcb
There are a lot of cool and useful things in there.
What are you most excited about?
| > > dennis16384
Fast response. I can upload Excel/csv and iterate
under 10 seconds from question to result. Doing
same thing in Claude with 10x less data takes 5
minutes.
| > > > arcb
I hear you on fast responses. One of the
frustrations I've had using BI / data tools in
the past was not being able to get local
performance... which led to me exporting data
to spreadsheets or local code. We're taking
this to heart for BitBoard as well.
| > > > > dennis16384
Totally. One thing that all major AI
vendors are not doing currently is merging
server AI with edge devices.For example,
there is no way neither in Claude nor in
ChatGPT to run your own WASM or JS or
whatever AI produces directly in user's
browser context as a tool/skill - there is
no call site for that. The only option is
remote server-side.My whole idea was that
AI can perfectly write SQL and dashboard
code knowing only the shape of your data
and not it's contents. With direct upload
to vendor now we're forced to share the
contents.
| > > > > > arcb
I suspect stronger edge performance
will come as a side-effect of local
inference. Your point on edge tool
calls is interesting and I'll think
about that. Features like offline mode
could be a great motivating reason. Re
knowing the shape vs not the internals
- I'm mixed here. It feels like
there's always a sampling period where
you have to look at contents in order
to understand what you want. But edge
AI (like antirez's work running
DeepSeek on Mac) will let you have
both. I'm excited for that future!
| > > > > > > dennis16384
Why would an LLM want to look into
the contents, what for?We have
low-cardinality data and yes this
is safe to share and required to
build an actual query.Then we have
high-cardinality and possibly PII
- there's absolutely no reason to
share that data, there's nothing
for LLM to analyse there. Also
semantic index (vector search)
will find relevant records much
faster and more accurately that
any chain-of-thoughts just with an
LLM-authored search fn
call.Further there are continuous
numerical values and there's not
much LLM needs to see in there
either. We can say, for example,
if you look at data distributions
when building your analysis, it
can drive your analysis logic, but
another point of view here is taht
it creates unnecessary bias
instead.
| > > > > > > arcb
On re-read I think I might have
overreached in my reply. I think
having local LLMs being able run
tool loops to _transform_ data,
rather than just summary or
analysis, will become 1/ great for
non-technical users, 2/ fast.
| sails
> but customers kept pulling us toward their data analysis
problemsI hear this all the time, I still don't think it's
a good justification to build a BI tool, but I hope this
time it is different.Product looks cool! I'm hopeful that
agents do actually unlock business analytics and we can
move on from the BI conceptEdit: a rough explanation of
why you get pulled towards data problems is that they are
intractable symptoms of upstream process issues. Customer
sees a capable startup and co-opts them into trying to
solve their tarpit problems. Happens all the time!
| > arcb
We hear you on getting pulled into tarpit problems,
and on the pattern you're describing leading to them.
The core product motivation we're excited about is
letting humans and their agents act on data together,
but we do think that requires thoughtful tooling to
exist before that becomes desirable (more to come
here). Our newer customers tend to be a little more
technology forward, which helps us focus on the
product we're offering them rather than internal
politics or process issues.
| spmartin823
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?
| > arcb
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.
| mritchie712
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
businessWe'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
| rohand7
That is a fantastic idea
| straydusk
Great concept. Had this idea myself recently.
| > arcb
Thank you! If you try it out let us know how it goes!
| htrp
Is there a way to sign up without going through google
oauth?
| > arcb
Not at the moment but it's in the queue. If there's a
sign up method that works better for you feel free to
DM me.
| BoorishBears
How are you connecting to various data sources?
| > arcb
We're offering secure connections to sources like SQL
DBs, warehouses, file stores, and MCP/API sources like
PostHog or Salesforce. Customers can choose to set up
credentials in our key store. We also support directly
dropping data into BitBoard (where we sync it to
object storage).
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