Most e-commerce brands are flying blind on competitive intelligence. They rely on a team member manually checking a few competitor sites once a week — if they remember. A competitor drops prices on a Friday afternoon. The team doesn’t notice until Monday. That’s an entire weekend of lost sales to an alert you never got.
The manual approach doesn’t scale. It doesn’t run on weekends. And it can’t simultaneously watch pricing pages, Amazon listings, product catalogs, review trends, and ad activity across five competitors at once.
That’s the problem this project set out to solve.
The Build Story
The Competitor Tracker Agent started as a personal frustration. Running a brand means constantly asking: what are competitors doing right now? Are they running a sale? Did they just launch something new? Are their reviews tanking — and is that an opening to capture market share?
The only honest answer used to be: “I don’t know, and finding out takes too long to be worth it.”
Here’s the thing — the data isn’t hidden. Competitor pricing is public. Amazon reviews are public. New product launches on Shopify stores are detectable. Google Ads transparency data is accessible. The problem isn’t access to the data. The problem is that gathering it, comparing it to what you saw last week, and then reasoning about what it means — that’s a full-time job.
So the question became: what if an AI agent could do all of that automatically?
Building small AI automations has been a recurring theme in this workflow — the insight from working on mini AI automations was that the highest-leverage moves are rarely the complex ones. You chain a few reliable steps together, automate the repetitive parts, and let the AI handle the reasoning layer. That’s exactly the architecture here.
What the Agent Actually Does
The Competitor Tracker Agent runs on a 6-hour scan cycle, 24 hours a day. It monitors four intelligence pillars:
Price Monitoring
Tracks competitor pricing across DTC websites and Amazon ASINs. Configurable thresholds mean you only get alerted when it actually matters (say, a change greater than 5%), not every minor fluctuation. It catches flash sales, coupon activity, and Buy Box changes.
Product Intelligence
Detects new product launches before they’re announced publicly. Shopify stores expose their full product catalog via a public endpoint — a new SKU showing up there at 11pm on a Thursday gets flagged immediately. Discontinuations, variant expansions, and positioning copy changes are all tracked.
Review and Sentiment Analysis
Monitors Amazon review counts and star ratings over time. When a competitor’s ratings start declining — say, dropping from 4.3 to 4.0 over 30 days — that’s a signal. It means customers are unhappy, and if you’re selling in the same category, that’s an opening. The agent surfaces these trends before they show up in your own sales data.
Ad and Campaign Monitoring
Tracks competitor advertising activity via Google Ads Transparency Center and Amazon Sponsored placements. When a competitor pivots their messaging or launches a new campaign targeting terms they’ve never used before, that signals a strategic shift worth knowing about.
The Tech Behind It
The agent is built in Python with Claude AI as the reasoning layer. Here’s the stack:
- Web scraping layer — Custom scrapers for competitor DTC sites, Shopify catalog endpoints, and Amazon product pages. Rotating request intervals to stay within reasonable limits.
- Amazon monitoring — ASIN-level tracking for pricing, review counts, BSR, and ad placements via public data and optional SP-API integration.
- Ad intelligence — SerpAPI for Google Shopping and Ads Transparency Center data; Amazon Sponsored Brands detection from search result pages.
- Claude AI for analysis — Raw data gets fed into Claude with context about what changed since the last scan. Claude reasons about whether a change is significant, what it likely means strategically, and what action to take. This is the part that makes it genuinely useful rather than just another data dump.
- Slack integration — Alerts fire within minutes of a significant change being detected. The daily briefing is a structured report generated every weekday at 8am.
The agent also maintains persistent memory across scans — tracking trends over weeks and months, not just comparing today against yesterday. That historical context is what lets it say things like “Acme’s prices are at a 6-month low” rather than just “price changed.”
This fits into a broader pattern of thinking about AI as infrastructure rather than as a one-off tool. The post on growth engineering with Claude Code explored this — when you treat AI as the reasoning engine inside a persistent automated system, you get compounding returns that a prompt-and-response workflow never will.
What the Morning Briefing Looks Like
Every weekday at 8am, a structured report lands in a dedicated Slack channel. Here’s what a typical Friday briefing looks like:
Price Intel: Acme Corp dropped Widget Pro from $34.99 to $27.99 (-20%). Flash sale, likely ends Sunday. Their lowest price in 6 months.
Product Intel: BrandX quietly added “Pro Max Bundle” to their Shopify store. Not announced publicly. $89 price point — a new premium tier.
Review Intel: No major rating changes. BrandX trending slightly down: 4.1 to 4.0 stars over 30 days.
Ad Intel: Acme Corp added 3 new Google Shopping ads this week targeting “budget widget” and “affordable widget 2026” — consistent with their price drop strategy.
Recommended Actions:
- Consider a targeted counter-promotion this weekend while Acme’s prices are low — capture price-sensitive shoppers before they return to normal pricing.
- Investigate BrandX’s Pro Max Bundle. If it gains traction, it could pressure mid-tier SKUs.
- BrandX’s review decline is an opening — consider increasing PPC bids on their branded terms.
The key distinction is the recommendations section. Raw data is noise. The agent uses Claude to reason about what the data means in context and what to do about it. That’s the difference between a monitoring tool and actual intelligence.
The DIY Competitive Advantage
There’s a strong argument for building tools like this rather than buying off-the-shelf software. Enterprise competitive intelligence platforms like Crayon and Klue exist — but they’re built for B2B SaaS companies, start at $15,000+ per year, and track PR and content rather than pricing and Amazon reviews. They’re solving a different problem.
The doing-it-yourself advantage is that custom-built systems can be tuned exactly to the competitive landscape at hand. Which competitors matter. Which price changes actually warrant a response. Which product categories to watch. That specificity is what turns monitoring into actionable intelligence.
What This Becomes
Competitive intelligence at this level of depth and automation wasn’t accessible to small and mid-size e-commerce brands before. It required a dedicated analyst, an expensive platform, or a lot of manual work that was never consistent enough to be reliable.
The agent changes that calculus. Tell us who your competitors are, and we install a monitoring system tailored to your market in under two weeks. Scans run every 6 hours. Alerts arrive in real-time. The morning briefing is waiting before the team starts their day.
The parallel to building AI agent systems that handle complex, multi-step reasoning tasks is clear: the value isn’t in any single AI call, it’s in the architecture that chains intelligence together into something that runs continuously without human intervention.
Full Details and Demo
The full service page — including pricing, the complete feature breakdown, and a sample Slack report — is at mattwarren.co/competitive-intelligence.
If this is a problem your brand is dealing with, book a free 30-minute competitor audit. Walk away with a competitive landscape snapshot whether you buy or not.