ZettaSheet
AI-Powered Spreadsheet & Large-Scale Data Analysis
ZettaSheet analyses 10M–50M+ row datasets in a familiar spreadsheet interface, in the browser, without SQL or a BI team. Upload a large CSV, get a data-quality score, ask questions in plain English, and build charts and dashboards — while the dataset itself never loads into the browser.
- 50M+
- Rows supported
- 50 GB
- CSV ingestion
- 1 GiB
- Container footprint
- 0
- SQL required
{ why it exists }
Spreadsheets break long before the data does
Business teams work with large CSV exports from CRM, e-commerce and advertising systems. Those files pass the practical limits of spreadsheet software quickly, while the warehouse that could handle them requires SQL and a data team — leaving the people with the questions unable to answer them.
- Spreadsheet tools stop being usable well before ten million rows
- Simple business questions queued behind engineering time
- Loading large datasets client-side is slow and expensive
- AI insights that fail outright when one provider is down
{ what it does }
What ZettaSheet does
Natural-language analysis
Ask "show me Series A SaaS companies in California" and the AI converts it into an analytical query, returning the relevant view without a line of SQL.
Streaming ingestion
CSV data streams into Parquet with Snappy compression, supporting files up to 50 GB on a 1 GiB container because the file is never held in memory.
Intelligent query routing
Queries route to DuckDB below 10M rows and BigQuery above it, so small datasets stay fast and large ones stay possible.
Spreadsheet grid
AG Grid Enterprise brings server-side rows, pivots, range selection and Excel export to datasets far past what a spreadsheet could open.
Charts & dashboards
Apache ECharts visualisations generated from the same analysis, arranged on a thirteen-widget interactive dashboard canvas.
Data-quality scoring
Schema inference, PII scanning and a quality score run automatically before analysis begins, so problems surface before conclusions do.
How ZettaSheet is built
The whole design question is how to give a browser the feel of a spreadsheet over data a browser could never hold. Everything below follows from keeping the dataset out of the client and bounding memory during ingestion.
Ingestion
Streaming CSV parsing into columnar Parquet with Snappy compression, so memory stays bounded regardless of file size.
- Streaming parse
- Parquet conversion
- Snappy compression
- Schema inference
Query Layer
Size-aware routing between an embedded engine and a warehouse, so cost and latency both track the actual dataset.
- DuckDB
- BigQuery
- Size-based routing
- Server-side pagination
AI Layer
Schema-aware text-to-SQL with a provider fallback chain and template matching, so an upstream outage degrades output rather than removing it.
- Text-to-SQL
- Schema grounding
- OpenRouter fallback
- Request queueing
Presentation
A spreadsheet surface and visualisation layer that read through the query engine rather than holding data themselves.
- AG Grid Enterprise
- Apache ECharts
- Dashboard canvas
- Excel export
{ built for }
Who uses it
Sales operations
Analyse large CRM and lead datasets without SQL or warehouse access.
E-commerce & merchandising
Work with large Shopify exports across products, customers and orders.
Business analysts
Ask questions in natural language and build dashboards independently.
Operations teams
Turn large raw operational exports into decisions quickly.
{ what it delivers }
Why it matters
Millions of rows, no browser load
Datasets stay server-side while the interface stays familiar.
50 GB on a 1 GiB container
Streaming ingestion keeps memory bounded at any file size.
Analysis without SQL
Business teams answer their own questions without a BI queue.
Resilient AI workflows
Provider fallback and template matching keep output flowing during outages.
ZettaSheet is live today.
Try it yourself — or tell us what you want built and we will scope it the same way we scoped this.
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