Data Processing
Describe the transformation you need in plain English. The agent writes and runs the Python script, shell pipeline, or Node job — you just approve and get clean output.
Impact
What changes
Without Lapu AI
A data analyst spends 20-40 minutes writing a Python script or fumbling with spreadsheet formulas to clean and transform a CSV. Sensitive data gets uploaded to online tools for quick fixes.
With Lapu AI
Lapu AI generates and runs the transformation script in minutes. The original data is preserved, the output is validated, and processing happens locally on your machine.
The challenge
Processing local data files often requires writing one-off scripts, switching between spreadsheet applications, or uploading sensitive data to online tools. Teams working with customer data, financial records, or proprietary datasets need a way to transform and analyze files without sending them to third-party services.
How Lapu AI solves this
Lapu AI reads your local data files (CSV, JSON, TSV, XML, YAML, logs), understands the structure, and performs transformations you describe in plain language. It writes and executes processing scripts — either via shell pipelines (awk, jq, sed) or through sandboxed Python and Node runtimes with automatic package management. The sandbox execution environment runs scripts locally with output capture and validation.
Data processing runs locally via scripts the agent generates. Only structural context (like column names) is sent to the AI model.
Workflow
How it works
Load your data files
Point Lapu AI at your data files. The agent reads the first rows using File Read to understand the schema, column types, and overall structure.
Describe what you need
Tell the agent what to do in plain language — filter, merge, aggregate, reformat. The agent writes the appropriate script (Python, Node, or shell) and shows it to you before execution.
Run, validate, and export
Approve the script and the agent runs it — either through sandboxed Python/Node execution or shell pipelines. It reads the output, reports row counts, shows a sample, and flags anomalies.
Try it yourself
What you would type
Copy any of these into Lapu AI to get started immediately.
>Read the CSV at ~/reports/sales-q1.csv, remove all rows where the amount is zero, and save the cleaned result as a new file.
>Merge all JSON files in the data/ folder into a single CSV, using the 'id' field as the primary key.
>Parse the nginx access log and generate a summary showing the top 20 URLs by request count with a Python script.
Ready to try this workflow?
Download Lapu AI and run it on your own machine. Free to start.
Download for freeFAQ
Common questions
What data formats does Lapu AI support?
Lapu AI can read any text-based format including CSV, TSV, JSON, JSONL, XML, YAML, and plain text logs. For binary formats like Excel (.xlsx), it uses the built-in XLSX skill or command-line tools.
Is my data sent to the cloud for processing?
No. The data processing itself happens locally — via shell commands or sandboxed Python/Node scripts running on your machine. Only structural context (like column names and sample rows) is sent to the AI model to generate the processing logic.
What is sandbox execution?
Lapu AI can run Python and Node scripts in a sandboxed environment on your machine. It automatically installs approved packages if needed, captures output (up to 60KB), and enforces timeouts. This is safer than running raw shell commands for complex data transformations.
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