How Maia Automates Report Generation

From Query to Stakeholder Report in One Prompt

Every analytics team knows this cycle: run a query, export to CSV, paste into a slide deck, tweak the charts, add context, share a link… and repeat it all next week when the data refreshes. The "last mile" of analytics, turning results into something a stakeholder can actually read, is manual, tedious, and error-prone.

What if an AI report generator could do all of it, automatically?

Maia's AI Report Generator Skill

Maia can generate self-contained, interactive HTML reports directly from your warehouse data, upload them to a shareable location, and refresh them on a schedule. It's data pipeline automation applied to the last mile of reporting, and no BI tool is required.

Here's how it works:

• You describe what you want. "Build me a retention report showing quarterly cohort dropoff, with a line chart and a segment breakdown."
• Maia reads your pipeline. It inspects the transformation you've already built (the tables, joins, filters, and aggregations) and auto-detects the columns, data types, and what makes sense as a dimension vs. a measure.
• Maia writes the report. A Python script that queries your warehouse, builds a styled HTML page with Chart.js visualizations, stat cards, comparison tables, and narrative context, then uploads it to a Snowflake stage (or Google Cloud Storage, Google Drive, or Confluence).
• It's a pipeline. The report generation is an orchestration pipeline with a single Python Pushdown component. Schedule it daily, weekly, or on-demand. Every run overwrites the previous file with fresh data and a "Last refreshed" timestamp.

Under the Hood: How the Automation Pipeline Is Built

The skill, called html-report-to-stage, is a Markdown instruction set that tells Maia exactly how to assemble the pipeline. It's invoked explicitly (@html-report-to-stage) rather than auto-activating, so it only runs when you actually ask for a report. A few technical details worth knowing:

• Requirements gathering is mostly automatic. By default, Maia reads whichever pipeline you have open, follows the sources chain to the terminal component, and samples it to learn the output columns, data types, and likely dimensions vs. measures. It only stops to ask you about chart types, narrative/context, the target stage location, the output file name, and whether to overwrite each run or keep versioned copies (e.g. report-2026-08-25-1402.html).
• One component, one script. The pipeline is always Start → Python Pushdown. The Python Pushdown component runs on Python 3.11 with a 600-second timeout and needs no extra packages: just the standard library (io, datetime) plus the Snowpark session object that's already available inside the pushdown.
• The script does five things: query the warehouse table via session.sql(...).to_pandas(), extract the values it needs, stamp a UTC "last refreshed" timestamp, build the HTML as one big f-string (Chart.js loaded from CDN, styles and script inline), and push it to the stage with session.file.put_stream(...).
• Upload settings matter. The upload always uses auto_compress=False and source_compression='NONE' so the HTML isn't gzipped, and overwrite=True by default. Every run finishes with ALTER STAGE ... REFRESH, which registers the file's metadata so Snowflake serves it with the correct content type in a browser.
• Stage setup is a separate, one-off pipeline: just a CREATE STAGE IF NOT EXISTS with DIRECTORY = (ENABLE = TRUE), a FILE_FORMAT set to prevent binary interpretation, and a refresh. It's deliberately kept apart from the report-generation pipeline since it only needs to run once.
• The HTML template follows a fixed structure every time: title and timestamp, a plain-English definitions section, stat cards for headline numbers, a comparison table with a "what it means" column, Chart.js charts, a methodology note, and a footer. Styling is inline and self-contained (system fonts, card layout, a small colour palette: green for positive, amber for warning, indigo as the primary colour) so the file never depends on anything outside a single CDN script tag.
• F-strings need careful brace-escaping. Because the HTML is built as a Python f-string, literal CSS/JS braces have to be doubled ({{ / }}) so they render as single braces in the output, while single braces are reserved for variable interpolation ({value}). Getting this wrong is the most common way a generated report breaks.

The result is that a single Python Pushdown component, reviewed and version-controlled like any other pipeline component, does the work that would otherwise take a BI tool, a scheduler, and a manual export step.

What a Report Looks Like

The reports Maia generates aren't throwaway HTML. They're designed to be stakeholder-ready:

• Stat cards with headline KPIs, colour-coded green, amber, or red
• Chart.js visualizations: bar charts, line charts, retention curves, loaded from CDN, interactive with hover tooltips
• Comparison tables with a "What it means" column explaining each metric in plain English
• Definitions section so non-technical readers understand the terminology
• Methodology footer explaining how the data was calculated
• Responsive layout: clean, card-based design that works on desktop and mobile

Everything is inline: one .html file, no external CSS, no dependencies beyond a Chart.js CDN link. Open it in any browser.

Real-World Examples

We've been using this internally at Matillion to track Maia's own adoption:

Cohort Retention Analysis

A quarterly retention report that segments users into "Still Active", "Used Then Left", "Tried Briefly", and "Left Platform Entirely", with retention curves showing the percentage of users retained at 7, 30, 60, and 90 days from first use. One prompt to Maia, one pipeline, auto-refreshed weekly.

Expansion Barriers Report

A comparative analysis of customer accounts that expanded their AI usage vs. those that stalled, evaluating pipeline execution frequency, user growth trajectories, and team size as key predictors. Side-by-side stat cards make the differences immediately visible.

Both reports are generated by a single orchestration pipeline: Start → Python Pushdown. That's it. Two components.

Share Anywhere

The HTML file lives on a Snowflake stage by default, but Maia can also build upload steps for:

• Google Cloud Storage / S3: generate a pre-signed URL or serve via a bucket
• Google Drive: automated upload via service account, shareable link for the team
• Confluence: attach directly to a wiki page so it's always up to date

The stage setup is a separate one-off pipeline (just a CREATE STAGE IF NOT EXISTS), keeping concerns cleanly separated.

How to Use It

In any Maia conversation:

• Open the transformation or orchestration pipeline whose output you want to report on
• Ask Maia: "Generate an HTML report from this pipeline's output with a bar chart showing X and a table of Y. Upload it to MY_DB.MY_SCHEMA.MY_STAGE."
• Maia will auto-detect your output columns, suggest charts, build the Python script and orchestration pipeline, and validate it
• Run the pipeline: your report appears on the stage, ready to share

To refresh, just re-run the pipeline (or schedule it). The timestamp updates automatically.

Why This Matters

The bigger win here is closing the loop between data engineering and data consumption inside a single platform. Saving time on formatting is just the side effect:

• No context switching: you don't leave Maia Foundation to build a report
• No BI tool overhead: for focused, single-purpose reports, a self-contained HTML file is faster to build, easier to share, and simpler to maintain than a dashboard
• Version-controlled: the Python script and pipeline YAML live in your project's Git repo, reviewed and auditable like any other pipeline
• Composable: chain it after any transformation. Your ELT pipeline finishes, then the report refreshes automatically
• AI-assisted iteration: want to add a chart? Change the narrative? Just ask Maia to update the report

The Bigger Picture

This is agentic data engineering in practice: the HTML report skill is one example of a broader pattern, where Maia skills are reusable instruction sets that extend what the AI agent can do. They're Markdown files in your project repo, and you can create your own, customise existing ones, and share them across teams.

The goal isn't to replace BI tools. It's to make the 80% of reporting that doesn't need a full dashboard (the weekly status update, the one-off analysis, the "can you send me the numbers?" request) as easy as describing what you want.

Your data is already in the warehouse. Your pipelines already transform it. Now the last mile is automated too.