Room for the full question.
Up to one billion logical worksheet rows, including compact patterns. Explore large datasets without dividing every analysis into small worksheets.
THE NEXT CONVERSATION IN SPREADSHEETS
Big-data analysis.
Spreadsheet familiarity.
A local analytical workspace that brings database-backed calculations, Python and repeatable workflows to a familiar grid.
Windows desktopDatabase-backed calculationsNative PythonWorkflow automation
01 / THE PLATFORM
A familiar way to work.
A different scale of ambition.
Keep the flexibility of a spreadsheet as the analytical workload grows. Explore source data, build formulas and run automation in one local application.
Actual application capture from an earlier development build. The current demo includes subsequent engine and workflow updates.
Local by design.
Run supported calculations and data workflows on your own computer. No mandatory upload to a hosted spreadsheet service.
Code when you need it.
Use native Python alongside formulas to prepare data, calculate indicators and automate repeatable work.
Logical row capacity is distinct from physically populated or calculated rows. Practical limits depend on the data, operation, memory and hardware. User-selected scripts or external services may access the network.
02 / FROM ANALYSIS TO PROCESS
Build the workflow.
Bring the work back to it.
Turn a sequence of spreadsheet tasks into a process you can save, review and run again.
WORKFLOW BUILDEROne process.
One process.
Repeatable execution.
Arrange supported imports, scripts, formula fills, checks and exports into an ordered workflow. Reuse the definition instead of reconstructing the process for every dataset.
- Create, edit and save ordered steps
- Run the workflow with stage progress
- Review completion, errors and cancellation
WORKFLOW SCHEDULERYour folder.
Your folder.
Your workflow. Your schedule.
Connect a saved workflow to a selected input folder. Set a schedule and process matching workloads with a separate destination for results.
- One-time and recurring schedules
- New or changed input-file tracking
- Run history and manual execution
03 / THE ENGINE UNDER THE GRID
Designed around the work.
Built to reduce the overhead.
GalaxyGrid combines a Python and Qt application with DuckDB-backed execution. Eligible workloads follow optimized paths while unsupported cases retain conservative fallbacks.
Local execution. Controlled concurrency.
One coherent analytical workspace.
Batch and column execution
Push compatible calculations into database-backed operations rather than Python cell-by-cell loops.
Compact representations
Represent supported sequences and formula ranges without materializing every cell individually.
Dependency-aware scheduling
Run eligible work concurrently as inputs become available, within worker and memory limits.
Bounded result publication
Stage compatible outputs and publish them together to reduce repeated workbook updates.
04 / MEASURED, NOT JUST DESCRIBED
Bring your workload.
See the evidence.
Discuss the calculation paths, inspect the results and evaluate a representative dataset in a private demonstration.
MEASURED ON A LOCAL PC
12-column calculation · GalaxyGrid 0.16.5
1,047,576rows across 12 columns
10.8sreported calculation time
3independent trials on the same computer
Test conditions and measurement scope
Measured by GalaxyGrid’s creator on his own computer using GalaxyGrid 0.16.5: 1,047,576 rows across 12 columns, with a reported calculation time of 10.8 seconds and three independent trials.
Hardware specifications, calculation formulas, worker and memory settings, individual trial timings and the exact timing boundary have not yet been published. The 10.8-second result is reported as supplied; it is not identified as a mean, median or best run. Independent trials do not imply independent third-party validation. Performance varies by workload and hardware; this is not a comparative Excel or Row Zero benchmark.
Download the reported benchmark summary ↗05 / QUESTIONS WORTH EXPLORING
A platform for demanding data.
Explore a concrete workflow, then discuss the fit for your customers or organization.
Keep the history. Explore the signals.
Use synthetic minute-by-minute market data to demonstrate moving averages, true range, volume calculations and repeatable Python workflows.
Discuss your use case
More observations. More room to investigate.
Explore measurement and sensor histories using formulas, statistical calculations and custom Python preparation.
Discuss your use case
Follow the transactions. Find the exceptions.
Explore order histories, inventory movements and operational records with lookups, calculations and repeatable reporting preparation.
Discuss your use case
Illustrative use cases, not customer deployments or endorsements.
CONCEPT PREVIEW / PLANNEDMake automation
Make automation
more approachable.
The planned Visual Python Builder would turn selected actions and parameters into readable Python, with reusable components for common analytical processes.
A future product direction. This builder is not included in the current application.
06 / START A CONVERSATION
See what GalaxyGrid
could bring to your business.
Explore the product and the engineering behind it in a direct conversation with its creator.
Product licensingStrategic partnershipsAcquisition discussions
GalaxyGrid is in active development. Private demos cover demonstrated capabilities, current limitations and the roadmap.
DIRECT CONTACT
Kyle Glass
Creator of GalaxyGrid
Request a demo kyle@galaxygrid.appInclude your company and the workflow or opportunity you’d like to explore.