Show HN: Sourcetable – AI Spreadsheet and Data Platform
Sourcetable is an AI-native spreadsheet that syncs with all your data. Users pair with an AI copilot that helps them do their spreadsheet work, as well as more database-centric analysis and SQL.
Soucetable syncs with databases including Postgres, MySQL, and MongoDB, and over 100+ business applications including Stripe, Zendesk, Hubspot, Quickbooks and Google Analytics. That data is available in a spreadsheet, and any models you build automatically update in near-real-time as new data flows in. The core primitives are AI + spreadsheet + data sync + storage + compute.
If you want to play with Sourcetable today, the easiest way is to upload a CSV and start asking questions.
Who is it for? Sourcetable is for analysts, operators and finance folk doing data-centric work in a spreadsheet. Sourcetable’s spreadsheet-based AI assistant understands workbook range selection and can adjust scope context to the datasets you are working with. You can talk directly to your database and SaaS integrations, which is great for analysis, data search and retrieval, SQL writing & editing (including writing joins across different datasets), and automatic chart creation.
Niching down, if you work in operations at a <50 person startup or SMB and your company relies on a Postgres or MySQL database, Sourcetable is an affordable reporting tool with turnkey data infrastructure that doesn’t require code or engineers to set up.
Spreadsheets are the most used analytical tool on the planet. AI is a platform shift with broad applications. We are staying open-minded about users and use cases since everything is so new.
Backstory: I spent ten years working in de-facto operations and technical roles at startups. Sourcetable draws from that experience of needing better data tooling inside spreadsheets, and constantly hacking ad hoc solutions to fill the gap. Andrew (CTO / co-founder) previously had a deep learning company and was initially drawn to the idea that Sourcetable could be an operating system for the web. We’re both Aussie expats in the Bay Area, which is how we met. Internally, we think of Sourcetable as an application platform, with AI applications being a useful and interesting place to focus.
Features & Use Cases: Talk to your CSV files, spreadsheets, integrations, and datasets using LLMs. AI + data work: Text-to-SQL, search and retrieval from databases, LLM-based data analysis. (This is an entirely different experience to what Copilot/Gemini & Excel/Sheets provide, since they are thin workbooks and not data platforms.) AI + spreadsheet work: formula assist, workbook analysis, data cleaning, chart creation, error handling, summarization, chat, etc. Automated reporting: data is synced, reports you build stay up to date. No-code data access: give the business team safe database access so they will leave you alone! Centralizing data for cross-channel reporting. (e.g. Postgres + Stripe + Mailchimp) Analyzing large CSV files: Sourcetable can handle multi-gigabit files. (Google Sheets can’t handle large data and the experience in Excel is rather cumbersome.)
Technical Details: Sourcetable was built to be fast. It was also built to scale.
AI: LLama 3 (via Groq), Claude, GPT-4o, LiteLLM, custom LLMs
Frontend: DuckDB, React, ShadCN, AntV / Bizcharts, Plotly, CodeMirror, Hookstate
Backend: DuckDB, Python, Cassandra, Redis, NGINX, Cloudflare
Data Eng & Transformations: Fivetran, DBT, Apache Arrow, SQLglot
Distributed Computing & Scaling: Daft, Ray, Cloud Formation
Other: Linux Namespaces, Dill (U.Queensland)
A huge thank you to the open source community, and a special shout-out to DuckDB for being so damn fast. Thank you also to Groq & Anthropic for the rate limit increases in time for this ShowHN post!
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Feedback: Product feedback is welcome! eoin@sourcetable.com
104 comments
[ 4.2 ms ] story [ 182 ms ] threadI'm already using Retool for these kinds of tasks- what does sourcetable do that I can't already do with Retool?
edit: also, did you build your own spreadsheet engine, or use an off-the-shelf one? (also will it be open source ;P)
The primary difference vs table-based solutions is that Sourcetable is a spreadsheet in the common sense of the word, similar to Excel and Sheets. We have A1 notation and cell-based referencing. This is what most users expect, and this flexibility/familiarity has a big impact on the breadth of users and use cases within a team.
The formula referencing system of these table-based solutions is usually very limited both to columns/rows (not cells), and is a set of SQL-based queries which are much more limited than that 500+ formulas and functions spreadsheet users commonly expect.
Retool specifically: I tend to think of Retool as a lightweight custom-ERP software system, whereas Sourcetable more like Excel + PowerBI + Data Warehouse, so we will generally be much stronger for reporting and analysis. We definitely have some overlap in potential users since technical operators should like us both. FWIW - Retool is an excellent product.
We use a heavily modified licensed engine that prevents us from open sourcing everything (for now). We have plans to open source our agentic/plugin framework, and other parts of the system. We also have a strong ethos of contributing back to open source where we can (contributed back to Arrow, DuckDB etc.).
I'd also add that while everyone knows how to use and work with spreadsheets, we also provide a SQL layer on top that you can use to query data sources as an advanced user (we developed a nomenclature to work within sheets/across sheets/files/our data-warehouse). This allows more technical users to work side-by-side in the same environment as non-technical users without crossing pythonic or reporting boundaries.
On top of this, the AI assistant can answer most of the questions you might have of all this data.
I think as ML gets more sophisticated, we will in general need to be less technical. The "tooling" might even disappear, but we will still need something to communicate important data centric decisions. Whether you like it or not spreadsheets are the foundation of human research and operations and have been for thousands of years, and I feel humanity will need less complicated "tools" and we will keep to our roots.
https://dream-num.github.io/LuckysheetDemo/
https://github.com/dream-num/Luckysheet/issues/1454
I am not related to either sourcetable or luckysheet
https://github.com/ruilisi/fortune-sheet
We released Sourcetable today with the AI chatbot & AI data analysis features, but a very limited cell-based AI (only "summarize" and "fix formula"). We'll be releasing a big AI-based magic-autofill solution in the coming weeks.
I didn't go into details above but a nice thing is that we leverage cloud compute and storage, so you can query billion-row data in sub-second time. (Courtesy of Duck!)
How did you build so many integrations so fast?
Selfishly, would love to see Streak (CRM) integration as well.
And yes! We're definitely of the opinion that as a startup we can outcompete the two trillion-dollar death stars when it comes to product experience. AI is a platform shift!
Technically you can combine these, but it’s a cumbersome experience and difficult for most people. Vertically integrating their equivalents simplifies things a lot.
(Small note: we don’t currently offer Python to users but likely will at some point)
Some links that might be of interest:
- Table semantics: https://www.microsoft.com/en-us/research/project/table-inter...
- Entity semantics (video): https://onedrive.live.com/?authkey=%21AMIdbT4yVFaw2Kk&cid=A6...
- Natural Language in Spreadsheets: https://www.microsoft.com/en-us/research/project/gridbook/
(p.s. I think Andrew, CTO, is going to jump in here as he has more experience in this space.)
> Niching down, if you work in operations at a <50 person startup or SMB and your company relies on a Postgres or MySQL database, Sourcetable is an affordable reporting tool with turnkey data infrastructure that doesn’t require code or engineers to set up.
With the rise of AI, companies like Tembo that help you set up all in one databases, and tools like this, I'm increasingly of the mind that many companies should start bringing things like analytics and observability in-house. I don't see the need to pay Mixpanel or Datadog thousands of dollars per month when a self-serve solution that relies on tried and true tech is more or less at your fingertips.
Many other advantages of this data centralization too. Data + spreadsheets + compute is a nice application base for agents.
Modelling and integrating datasets that you don't own is extremely hard.
Shopify for example updates their API every 3 months.
How much time and money do you think an SMB can afford to spend on this before the ROI becomes so poor that they abandon it entirely.
Strong note here that the current state of technology is much better for SMB scale data and not enterprise scale data with messy schemas.
My personal use case tends to involve a lot of Postgres data and transaction events for my reporting. We see "simple" businesses like parts manufacturers, print shops, vineyards, etc. all doing something similar.
And companies are not dumping their SaaS tools and switching to them en masse.
Because (a) data silos have dramatically increased pushing dreams of a unified data schema out of reach, (b) technology stacks have become far more complex necessitating tools like Datadog and (c) competition is stronger than ever meaning that skimping on paying for tools like MixPanel is often short sighted and counter productive.
Companies like this will do fine and there will be always be a demand for them especially in the SMB space. But there simply isn't the business value in bringing a lot of analytics and observability in-house in almost all cases.
We also extensively use synthetic data and examples to guide and constrain our models.
Another way we're ensuring good-quality output is to ensure good-quality _input_ -- by enriching the detail and specificity of the user's question, and asking the user to disambiguate when we determine the question is too broad.
Small plug for the analytics tracker we are using which Andrew (CTO) built and is open source: https://github.com/sfproductlabs/tracker
https://www.dropbox.com/scl/fi/np92pyo0eb0zphysc9wwz/screens...
Very much appreciate the bug report. Thank you!
Anyway, fun fact: it turns out our domain used to be a scam erectile pills website!
- Fundamental gap in skillset, in that if you want to have ultimate flexibility to slice and dice the data and report on whatever you’re seeking, you’ve ultimately needed SQL skills in the past (which isn’t rocket science, but also isn’t something most accounting users can run with on their own).
- Fundamental desire of users to work with unstructured data. This goes back at least as far as Excel vs Lotus Improv in the early 90’s. Joel Spolsky talked about this, how they were terrified that Lotus Improv was going to kill Excel, because Improv was built to work with structured data, which users could then query and ask questions of to get any answer they want. But it turned out, as they observed people using both apps, there were zero users that used 100% normalized, structure data.
- Imperfect translation between spreadsheet and database. I’ve seen these work well 99.9% of the time, but at some point a column gets added or something that throws off formulas. And 0.1% error is basically catastrophic in accounting.
Maybe LLMs help overcome these challenges. Wish you luck.
TL;DR, most technical people massively overestimate the technical / data abilities of regular spreadsheet users. We find simple use cases are best, and with each new LLM release the UX around more complex data improves significantly.
The reason we chose to build as a full-blown spreadsheet instead of just a table-based solution was that we saw that most people want the flexibility of a regular spreadsheet, but access to their (structured) business data. Table-based solutions wedge you into AI and you can never get out of that.
We're generating the SQL to answer natural language questions, so folks can just get answers and results tables if that's all they need, with the option for power users to fiddle with the SQL either directly or via a query editor GUI.
There's a ton of use cases for working with unstructured and semi-structured data and that's coming down the pipe!
The most useful aspect was that I could ask "what was the total contributed amount between January and June of 2020" and get an accurate answer for that as well. Since the date column is provided as an "MM/DD/YYYY" string, I would normally have to do some boilerplate work to sanitize this.
For my particular use case, the charting aspect left a few things to be desired - once I grouped campaign donations by contributor, I could only see the first 10 rows in the AI response, with no option to expand the output. But overall I was truly blown away that something like this is even possible for a small team to build.
Insert it as a table on the page (you should see a button), it will then print the whole table result from that query into the spreadsheet. Also, you can check the SQL first and validate it, then print to table after that.
Try a few million rows and see what happens!
I like that it's able to infer information from the context of the cells, e.g. being able to run a query across continents when the data only contains the country.
Being able to ask it to interpret the results is helpful, it would be cool if it automatically told you if there was enough data to have statistical significance in the conclusions it was presenting.
Curious what will happen if you modify the question to be more explicit?
I have seen that PMs and data-trained folk tend to be very articulate in asking for exactly what they want and that tends to lead to significantly better LLM responses.