AI-Powered Data Analysis in a Secure Sandbox

The data analysis bottleneck

You have the data. You have the questions. What you do not have is someone to connect the two.

Most growing startups hit the same wall. The operations team has spreadsheets full of customer data, revenue numbers, usage metrics, and support logs. The questions are straightforward: Which customers are about to churn? What is our actual cost per acquisition by channel? Where are the bottlenecks in our onboarding funnel? But getting answers means either hiring a data analyst at $80K-120K per year or filing a ticket with the engineering team and waiting days — sometimes weeks — for someone to write the SQL query, spin up a Jupyter notebook, and send you a chart.

Non-technical teams are dependent on data scientists for analysis that should take minutes. The data is already there. The questions are clear. The gap is tooling, not talent.

And the workarounds are not great either. You could learn Python yourself (three months of tutorials, minimum). You could pay for a BI tool like Tableau or Looker ($70-150 per seat per month, plus implementation time). Or you could keep asking the engineering team and accept that “quick data question” means “next sprint, maybe.”

How AI agents change data analysis

Here is what happens when you ask LikeClaw to analyze your data: you type a question in plain English. The AI agent translates that question into executable code — Python, SQL, whatever the task requires. That code runs inside an isolated E2B sandbox container. The results come back as charts, tables, or written summaries. All of this happens in your browser. No local setup. No dependencies to install.

The flow is: natural language to code to execution to results, all in a sandbox.

This is not a chat interface that describes what analysis you could do. This is an agent that actually does the analysis. It writes the code, runs it, handles errors, and returns the output. If the first approach does not work, it tries another. If you ask a follow-up question, it builds on the previous context.

For teams already using LikeClaw for code execution, data analysis is the same underlying capability applied to a different problem. Same sandboxed environment, same persistent workspace, same predictable pricing.

What LikeClaw handles

The AI agent is not limited to one type of analysis. Here is what you can do today:

CSV and JSON processing. Upload a file, ask questions about it. “Show me the top 10 rows by revenue.” “Calculate the average order value by region.” “Find duplicate entries in the customer_id column.” The agent reads your data, writes the processing code, and returns clean results.

API data pulls. Connect to REST APIs, Google Sheets, PostgreSQL, MySQL, or S3 buckets. The agent pulls the data into the sandbox, processes it, and returns insights — without exposing your connection credentials outside the isolated container.

Chart generation. Bar charts, line graphs, scatter plots, heatmaps, time series — the agent writes matplotlib or plotly code and renders visualizations in your workspace. Ask for specific formatting: “Make it a stacked bar chart with monthly labels.” It handles the code.

Report creation. Need a weekly summary of key metrics? Describe what you want and the agent generates a formatted report with charts, tables, and written analysis. Export as needed, or set it to run on a recurring schedule.

Trend analysis. Upload a time series and ask: “What is the trend in customer churn over the past 12 months? Are there seasonal patterns?” The agent applies statistical methods — moving averages, regression, decomposition — and explains the results in plain language.

If you need to chain data analysis with other automated workflows, LikeClaw’s task automation lets you build multi-step pipelines: pull data, analyze it, generate a report, and push the summary to Slack or email.

Security matters: your data in isolated containers

This is the part most data analysis tools skip over. When you upload a revenue spreadsheet or connect a production database, that data goes somewhere. On most platforms, “somewhere” is a shared server environment where your data sits alongside other users’ data, processed by shared infrastructure with varying retention policies.

LikeClaw handles it differently. Every analysis task runs inside an isolated E2B sandbox — a container that is created for your session, processes your data, and gets destroyed when the task finishes. Your data never touches another user’s environment. File uploads go directly into the sandbox, not a shared storage layer. Database credentials are encrypted and scoped to the sandbox session.

Compare this to running data analysis on your local machine with an open-source AI agent. Researchers documented malware in open AI agent marketplaces — a real risk when your data is involved. If that agent has access to your local files — including the financial data you just downloaded — you have a serious exposure. With LikeClaw, the analysis runs in the cloud, in isolation, and the container is destroyed after use. Your machine is never involved.

On the Power plan with BYOK (bring your own API keys), even the AI model calls route through your own provider accounts. LikeClaw never sees your prompts or your data in transit. For operations managers handling sensitive business data, this is the difference between “probably fine” and “secure by architecture.”

Pick the right model for the job

Not every analysis task needs the same model. LikeClaw gives you access to 100+ models through one subscription, so you can match the tool to the task:

Claude — strong at complex reasoning over large datasets. When you need the agent to understand nuanced business context (“Which customers fit our ideal profile based on these 15 attributes?”), Claude handles multi-step reasoning well.

GPT-4 — reliable for general-purpose analysis. Good at structured data processing, chart generation, and explaining results in clear language. A solid default for most queries.

DeepSeek — cost-effective for batch processing. If you are running the same analysis across 500 CSV files or processing large datasets where speed matters more than nuance, DeepSeek gets the job done at a fraction of the cost.

You pick the model per task, or let LikeClaw choose the best fit automatically. One subscription covers all of them. No separate accounts, no extra charges, no juggling invoices. No more fragmented AI subscriptions you barely use. One platform, every model, predictable pricing.