Most company data is one Excel away from being a dashboard. That's literally our whole job.
For businesses sitting on a stack of spreadsheets with no plan to hire a data team. An AI analyst does the work — building your dashboards and answering questions of your data — while you keep the day job.
From file to dashboard
A cleaner way to work with business data
1 file
to start
AI
data check
Live
dashboard
Analytics for teams whose data starts in spreadsheets.
Most businesses already have the information they need. It is just sitting in spreadsheets, monthly exports, copied formulas, and files nobody wants to rebuild again. blueberry. turns those tables into dashboards that can grow with every upload.
Bring the spreadsheet
Start with the Excel or CSV your team already uses. blueberry. reads real column names, mixed formats, currencies, dates, and duplicate rows.
AI on rails
The AI does the thinking; deterministic code does the math. It proposes the structure, KPIs, and breakdowns, but every number on a chart is computed from your real rows — never invented — and you can reshape any of it just by asking.
Compound the dashboard
The first upload builds the dashboard. Later uploads append or update the same project, so reporting improves month after month.
Make the power of analytics straightforward enough for every business to start.
We work in analytics and BI consulting — big enterprises, scrappy startups, everything in between. One thing keeps proving itself: a good dashboard genuinely changes how a team thinks. When they can get to one.
And then there's everyone else. Plenty of smaller businesses sitting on data that could change their week, with no realistic path to a dashboard. Not because they can't — because nobody showed them where to start, and bringing in a consultant is overkill for what they actually need.
blueberry. exists for that first step: the teams that don't need a complex data platform yet, but do need a clear, trustworthy way to turn everyday files into decisions.
We're not here to replace consulting or pretend it's useless. Bigger, more complex projects absolutely need experienced teams, engineering, governance, and deep business understanding. We want to make analytics accessible where that level of setup isn't required.
Why Simão and Nina founded blueberry.
Same loop, different client: data sitting in spreadsheets, no plan to do anything with it. For the bigger problems, a real engagement made sense. For the smaller ones — and there are a lot of smaller ones — the usual answers were way bigger than the problem.
For some companies, a real consulting project is the right call — engineering, governance, the works. For most, the actual problem is way smaller: read the file, catch the messy values, build a dashboard that doesn't take a quarter to ship.
So we built the product we kept wishing existed in those meetings: upload the file, sanity-check what the AI saw, get back to running the business. Less ceremony, more useful answers.
— Simão and Nina, co-founders
Business intelligence should feel lighter.
Useful beats impressive
A good dashboard answers a real business question. It doesn't need a glossary.
Trust needs visibility
AI is going to make calls about what your columns mean. The numbers it shows are computed from your actual data, not guessed, and it flags the calls it's unsure about — so you can trust the figures and correct the rest by asking. Honest caveats over confident nonsense.
Small teams deserve leverage
A two-person shop, a clinic, a corner retailer — none of them should need a data department to know what changed this month.
Bring one spreadsheet. Leave with a dashboard.
Drop in a clean table. Walk out with a dashboard you'll actually use.