Every team has a Power Automate user. They build a flow that watches a SharePoint folder, extracts an Excel, summarizes by region, and emails the result. The flow works for a quarter. Then the source moves, the column renames, and the flow breaks. Someone has to rebuild it — usually not the person who built it.
The flow is the wrong unit. The pipeline — filter, summarize, sort, send — is the unit. Power Automate makes you build the pipeline by hand, connector by connector. We think the AI should build it for you.
Try It Now
Upload an Excel, Word, or PDF (or pick a sample below). Tell the system, in plain English, what to do with it. The AI constructs a pipeline. You review it. You run it. You save it. Next time, it runs from a schedule.
What Just Happened
The demo above did five things in sequence, none of which required you to write code or wire connectors:
- Parsed your document — Excel rows, Word tables, PDF text, or CSV — into a unified shape the rest of the pipeline can read.
- Analyzed it — Groq looked at the headers, sample rows, and column types, then suggested three to five actions specific to your data. Not generic suggestions — actual things you'd plausibly want to do with this particular file.
- Translated your instruction into a pipeline spec — a JSON object with steps (filter, summarize, sort, limit, select, transform), each one a structured operation the runtime can execute. No pseudo-code, no prose.
- Ran the spec client-side — every step executed in your browser. No server round-trip. The result table and chart appeared in seconds, not minutes.
- Offered to email the result — opened your email client with the subject + table pre-filled, so you can edit before sending (which is how every "automated email" should actually work).
What Makes This Different
| Power Automate / Zapier / n8n | Personalized Power Automate |
|---|---|
| You build the pipeline by hand | AI builds it from a sentence |
| Connectors are the unit — one breaks, the flow breaks | Steps are the unit — swap one without rebuilding |
| Schema changes break the flow silently | Pipeline spec is versioned + auditable JSON |
| Vendor owns your workflows | You own the spec — runs on your infrastructure, on your schedule |
| Per-user / per-run licensing | One-time build — the spec is yours |
From Demo to Production
The demo above runs entirely in your browser. The pipeline spec it generates is real — it's a JSON object you can download, save, and import. In production, that spec becomes a scheduled job:
- Static JSON + GitHub Pages — what the demo runs on. The pipeline is a JSON file, executed by a serverless function on a schedule. Right for daily/weekly snapshots.
- Your existing SQL backend — the spec lands in your warehouse, runs as a SQL Agent job or cron, writes results to a table the dashboard queries.
- Managed warehouse + serverless API — Supabase, Neon, Cloudflare D1, Turso. The spec runs via scheduled serverless function. Zero ops on your side.
The spec doesn't change between topologies. The runtime does.
The Honest Part
This is not a finished product. It's a concept demo. The Groq call is rate-limited to ten per browser session so the demo doesn't get abused. The email path uses your email client (mailto:) rather than sending directly — for real production, you'd wire it to a server-side sender. The "save pipeline" feature stores the spec in your browser's localStorage; in production it'd live in a database with auth.
What we're showing is the shape: natural language in, executable pipeline out, saved + scheduled + rerun on demand. The plumbing underneath is whatever your team already runs.
Want this wired to your data?
Bring one stubborn report or one recurring Excel grind. We'll set up the pipeline constructor against your actual sources — and show you what the first saved pipeline looks like before the meeting ends.
Book a Working Session