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Chat With Your Data: Excel and Google Sheets
Finance teams, ops teams, and analysts spend hours writing formulas and cross-referencing data in spreadsheets. A custom AI interface lets them ask "What was our Q3 gross margin by product line?" and get the answer directly, without writing a pivot table or knowing SQL.
The system learns your spreadsheet structure: which columns mean what, how sheets relate to each other, and what your business logic actually says. Then it translates plain language questions into calculations, runs them, and returns both the answer and the formula used.
Works with Excel, Google Sheets, and CSV data. Deployable as a Google Sheets sidebar add-on or a standalone web interface.
Tell us about the spreadsheets you want to query.
Six components that turn your spreadsheets into something you can ask questions of in plain English.
The system reads your spreadsheet structure (column names, data types, relationships between sheets, and named ranges) and builds a schema model. This lets the AI understand what each column means and how to write correct calculations against your specific layout.
Questions are translated into either Python (via pandas) or SQL depending on your data shape and complexity. The system executes the calculation, returns the result in plain text, and shows the formula or query used, so analysts can verify and learn from each answer.
Most real-world spreadsheets span multiple sheets with different schemas. The system maps relationships across sheets (joining data from a "Sales" sheet with a "Products" sheet when needed) without the user having to specify the join manually.
Standard analytical operations: sum, average, count, group by, year-over-year comparison, running totals, top N, percentiles. The system handles these in plain language without the user knowing which formula to use.
For trend analysis questions ("Show me monthly revenue for the last 12 months"), the system generates a chart alongside the answer. Charts are returned as images or embedded in the interface, no manual chart building required.
Deploy as a Google Sheets sidebar add-on that works directly in the sheet, a standalone web interface for Excel files uploaded on demand, or an embedded widget in your internal tooling. We match the deployment to your team's workflow.
From data connection to deployed interface, the build sequence.
Connect your data source
For Google Sheets, we connect via the Sheets API with read-only access. For Excel, files are uploaded through a secure interface or synced from SharePoint or OneDrive. CSV data is supported via direct upload or S3/cloud storage sync.
Schema mapping and annotation
We work with you to annotate ambiguous column names and define business logic your spreadsheets encode implicitly. For example: what "Net Revenue" means in your specific model, which columns are costs vs revenue, and how months are formatted.
Build and test the calculation pipeline
The translation pipeline is built and tested against a sample of real questions your team asks. We measure accuracy on the test set and tune the schema annotations and prompt engineering until the results are reliable for your data shape.
Deploy the interface
The interface is deployed where your team works: Google Sheets sidebar, web app, or embedded widget. For teams that want to query multiple files, a file management layer lets users upload and query different spreadsheets from one interface.
Handle edge cases and formula transparency
Every answer includes the calculation used to produce it. If the system cannot confidently interpret a question, it asks for clarification rather than guessing. Edge cases (empty cells, inconsistent date formats, merged cells) are handled with explicit error messages rather than silent failures.
Teams with structured spreadsheet data that people need to query repeatedly, without needing to know formulas or SQL.
CFOs and finance analysts spend hours navigating multi-tab Excel models to answer questions from the business. A natural language interface means "What was our gross margin in Q2 broken down by product line?" gets answered in seconds, without building a new pivot table.
Ops teams maintain dashboards in Sheets that only the person who built them can navigate. A chat interface means any team member can ask "How many orders shipped late this month compared to last month?" and get the answer without needing a formula guide.
Not every analyst writes SQL or Python. A natural language interface over CSV and spreadsheet data gives non-technical analysts the ability to ask ad-hoc questions that previously required a data engineering request.
We’d rather decline than take a project that won’t deliver value.
Spreadsheets with constantly changing structures
The system requires a stable schema to work reliably. If columns move, sheets are renamed, or the data structure changes every week, the schema model becomes stale and accuracy degrades. This works best for spreadsheets that have a consistent structure even if the data updates frequently.
Teams who need real-time BI dashboards
If your primary need is a live dashboard that updates automatically and visualises KPIs, a BI tool like Metabase, Looker, or Google Looker Studio is a better fit. This system is for ad-hoc question answering, not for replacing a dashboard. We can build the BI tool integration if needed.
Tell us which spreadsheets your team queries most often, the approximate number of rows, and the top questions people currently need to answer from those sheets. We'll reply within one business day.