AI agents for data analysis and reporting
Connect models that run SQL and Python against your warehouses and spreadsheets, return structured output, and process rows in batch. Wire results into dashboards, pipelines, or scheduled reports.
What this looks like in practice
Natural-language SQL and formulas
Describe the question in plain English and the agent drafts SQL against your warehouse or formulas for a sheet, runs them via code execution, and returns the result with the query attached. Reasoning models can iterate on schema mistakes, fix joins, and explain what each CTE is doing before you commit it to a saved query.
Batch row-level enrichment
Run the same prompt across thousands of rows in a CSV, table, or warehouse: classify support tickets, extract entities from free-text fields, normalize messy company names, or score leads. Structured output keeps each row's response in a fixed schema so it lands cleanly in a downstream column or table.
Scheduled reports and digests
Schedule an agent to pull last week's metrics from connected sources, run a defined analysis in Python, and emit a report as Markdown, a Slack message, or a structured payload to BI. The agent can flag anomalies, compare period-over-period, and link back to the underlying query for verification.
Why yno.ai for Data Analysis
- Code execution lets the model run Python and SQL on your data instead of guessing at numbers
- Structured output (JSON Schema) so downstream pipelines, sheets, and BI tools get predictable fields
- Multi-step tool use: query a warehouse, transform with pandas, validate, then write back results
Recommended models
Key concepts
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Frequently asked
- Can AI write SQL or spreadsheet formulas from natural-language descriptions?
- Yes. Modern reasoning models draft SQL against a provided schema and generate spreadsheet formulas from plain-language descriptions. With code execution enabled, the agent runs the query, checks the result, and self-corrects on errors like wrong joins or type mismatches. You see both the final answer and the underlying query, so analysts can review before saving it as a reusable view.
- How does AI handle large datasets without dumping them into a prompt?
- The agent connects to your data store through MCP or a database tool and runs queries against it directly, returning only aggregates or sampled rows to the model. Code execution handles row-level transformations server-side. This keeps token usage bounded and means a 50M-row table is queried, not pasted into the context window.
- Can AI automate weekly and monthly reporting?
- Yes. Define the queries, the analysis steps, and the output format once, then schedule the agent to run on a cadence. It pulls fresh data, executes the analysis in Python, and delivers the report to Slack, email, a sheet, or a webhook. Reruns are deterministic on the data side; the narrative summary is regenerated each cycle.
- Should I paste sensitive data directly into prompts?
- No. Connect the agent to your warehouse, lake, or on-prem store through the platform's MCP integration so queries run against the data in place and only the needed result rows leave your environment. Pasting raw PII or financial records into a prompt removes that boundary. Use scoped credentials and row-level filters at the MCP layer to control what the agent can read.
- Which AI model is best for data wrangling and analysis?
- For multi-step SQL, Python, and reasoning over schemas, frontier reasoning models from the Claude, GPT, and Gemini families tend to perform best because they can plan tool calls and recover from errors. For high-volume row-level enrichment where each call is short, smaller and faster models in those same families are usually more economical. yno.ai lets you route per task.