Competitor and Market Monitoring with AI: A Weekly Brief People Actually Read

Most companies already watch their competitors, just not systematically. Someone checks a rival's pricing page before a sales meeting, someone else forwards a press release, and a product manager notices a new feature months after it launched. The information is public, but it is scattered, and nobody has time to read all of it every week.

AI can do the reading. It cannot decide what matters to your business, and it should not be trusted with conclusions it cannot point to. This article explains how to set up a weekly market and competitor brief that stays accurate, stays within the rules and is short enough that people actually read it.

Start with the decisions the brief should support

A monitoring project that starts with sources ends up as a news feed. Start instead with three to five questions your team asks again and again. Has a competitor changed its prices or packages? Is it hiring for a new market or capability? Has it won public contracts in our segment? Which complaints about it keep coming up in reviews?

Each question needs an owner who will act on the answer: sales for pricing, management for market entry, product for features. If nobody owns a question, drop it. A brief that serves nobody in particular is the first email people stop opening.

Choose sources you are allowed to use

Public does not mean free of rules. Before you point a crawler at a website, go through these points with whoever handles legal questions in your company.

• Robots.txt and terms of use. The Robots Exclusion Protocol, standardised as RFC 9309, lets site owners say which parts of a site crawlers should avoid. The RFC itself says these rules are not a form of access authorisation, so respect them and read the site's terms as well.
• Text and data mining opt-outs. The EU copyright directive 2019/790 allows text and data mining of lawfully accessible content, but under Article 4 rightholders can reserve that use, for content online for example by machine-readable means. Honour those reservations.
• Databases. The EU database directive protects the investment behind a database. Repeated, systematic extraction from a competitor's product or price database can infringe that right even when every single page is public. Track changes and samples, not full copies.
• Personal data. Names, profiles and posts of a competitor's employees are personal data under the GDPR, even when they are public. Monitor companies, not people, unless you have a lawful basis and have planned for the transparency duties.
• Information from competitors themselves. The European Commission's guidelines on horizontal cooperation set out when exchanging information between competitors becomes a competition law problem. Collecting what anyone can see is different from swapping prices or plans with a rival, directly or through a shared third party. Keep the brief one-directional.

Within those limits there is plenty to work with: pricing and product pages, release notes, press releases, job postings, public tender notices such as those on the EU's TED portal, company registry filings, annual reports and public reviews. The EU trade secrets directive also treats observing or testing a product that has been made available to the public as a lawful way to obtain information.

How the weekly pipeline works

The setups we build in our projects follow the same few steps, whichever tools run them.

1. Collect. Fetch a fixed list of pages and feeds on a schedule. A fixed list is easier to check for permission than open-ended crawling, and it makes the brief repeatable.
2. Detect change. Store each version and compare it with the previous one. Most pages do not change in a given week, and only the differences go forward. This keeps costs down and stops the system from reporting old news as new.
3. Extract. A language model turns each change into a structured record: competitor, type of change, old value, new value, date and link.
4. Classify. Match each record to one of your questions and drop what matches none. Plain rules, such as any change on a pricing page, often work better than asking a model how important something is.
5. Draft. A model writes one or two sentences per item, grouped by question, with the source link next to every claim.
6. Review and send. A person reads the draft, removes noise, adds context the system cannot know and sends it. This takes minutes rather than hours, because the reading has already been done.

Keep the model honest

Two failure modes deserve attention in the design. The first is invention: a model asked to summarise a market will fill gaps with statements that sound plausible. The fix is structural. Every sentence in the brief must come from an extracted record with a link, and the reviewer must be able to check any item in one click. If nothing relevant changed this week, the brief says so.

The second is manipulation. Your system reads text written by other people, including your competitors. OWASP puts prompt injection first in its list of risks for applications built on language models, and describes indirect injection through external content such as websites. Treat fetched pages as data: the model that reads them gets no tools, no access to internal systems and no way to send anything. A hidden line on a web page should never be able to change what your system does.

How to start

Run a pilot for four to six weeks with a small scope: a handful of competitors, three questions and a fixed list of sources. Send the brief every week to the people who own the questions and ask them one thing each time: did you act on anything in it? If the answer stays no, change the questions before you add sources. Once the brief has earned its place, widen it one source type at a time and keep the human review step.

To estimate what a weekly brief could save your team, describe the current process in the ROI calculator. If you are still deciding where AI should start in your company, read five questions that pick the right first automation, or browse common workflows by business area.