Enterprise AI Adoption Tracker

A dashboard that aggregates and scores public signals of enterprise AI product-market fit (pricing announcements, contract filings, job postings) to surface inflection trends early.

Difficulty: 1-week | Stack: Python, FastAPI, SQLite, HTMX, Playwright, OpenAI API

Who this is for

Analysts, investors, and enterprise AI buyers who want data-driven signals instead of anecdote-driven narratives when evaluating AI vendor traction.

Build steps

1. Define and hard-code a seed list of sources to monitor: SEC filings, vendor press pages, LinkedIn job-post APIs, and key tech journalist RSS feeds.
2. Write scrapers/parsers (Playwright for JS-heavy pages, feedparser for RSS) that extract structured fields: vendor, deal size if mentioned, industry vertical, date.
3. Build a lightweight scoring model that weights signals (pricing page change = high, press release only = low, renewal mention = highest) and stores scores in SQLite.
4. Expose a FastAPI backend with endpoints for time-series score data per vendor and per vertical.
5. Build an HTMX frontend showing a timeline chart of aggregate ‘enterprise traction score’ and a filterable feed of raw signals with source links.
6. Add a weekly email digest (SMTP + Jinja2 template) summarizing top movers.

Risks

• Public signal volume is sparse — vendor pricing pages don’t change often enough to give a useful time series, so the dashboard may look static for weeks.
• Scraper fragility: vendor and news sites change HTML structure frequently, breaking parsers silently and poisoning historical data.
• Scoring weights are inherently subjective; without ground-truth outcomes to calibrate against, the ‘traction score’ may just reflect your priors.