Market monitoring with AI: a wiki that keeps itself current

Market monitoring with AI works like this: an AI employee reads the industry sources you pick, on an ongoing basis. It sums up new posts in a few sentences and files them in an internal wiki, sorted by your own topics. The wiki lives in a tool you already use (Notion, Confluence, or SharePoint), and every entry names the source and links to the original. Your team reads curated summaries and finds industry knowledge through search. Nobody has to hope that somebody read the right newsletter.

Why industry knowledge disappears into twenty inboxes

Because in most companies, market monitoring happens on the side. There's no system for it, just habits. Everyone subscribes to whatever crosses their path and reads whatever the week leaves room for. In busy weeks, that's not much.

You know how it goes. The platform newsletter sits in one colleague's inbox, the industry digest with someone on the second team. You've had the trade blog open in a tab yourself since Tuesday. Two people read the same story twice, and a third story nobody reads at all. Whatever does get read stays in the reader's head, or in their archive folder. For the rest of the team it's invisible, and so is it for search.

Here's the maths. With 15 sources and an average of four new posts per source each week, you're looking at 60 posts. Give each one ten minutes and that's ten hours a week. Nobody has those ten hours, so most of it stays unread.

That leaves your radar hanging on individuals. As long as the colleague who reads everything anyway is around, it more or less holds together. When she's on holiday, the thread snaps. After she leaves, the radar is gone entirely. Knowledge management that depends on people disappears with those people. The bottleneck sits in the structure, and structure is something you can build.

What a self-updating wiki actually is

A self-updating wiki is an AI employee with a fixed list of sources and a fixed topic structure, connected to your wiki tool. It reads the sources continuously, checks every new post against your topics, and creates an entry for anything relevant. What sets it apart from the tools your team has probably already tried comes down to how it runs. A chatbot waits for your input. An AI employee takes over the task.

Every entry follows the same pattern:

• a summary in a few sentences, in its own words
• the topic from your structure, say „Platform updates“ or „Law and regulation“
• source and date
• the link to the original

Summarising newsletters with AI is the smaller part of the job. The value comes from the sorting: a stream of stories becomes a reference work built around your own terms. When someone wants to know something, they search, and they don't need to know who originally subscribed to that post. Those 60 posts from the maths above might come out as 15 entries; a person skims those in half an hour, with the option to jump into the original at any point. New entries usually appear the same day the source publishes them. For a closer look at how a build like this comes together, see the Content Wiki use case.

Where does the wiki live? In your existing tool

The wiki builds up in Notion, Confluence, or SharePoint, wherever your team already works. We connect the tool during onboarding, and no extra system with its own login gets added.

This is more than convenience. Automating an internal wiki rarely fails on the technology and often fails on location: a portal nobody opens is dead knowledge with a pretty interface. Keep the wiki in your everyday tool and your existing permissions carry over automatically, so search finds industry knowledge right next to the project docs. You can link individual entries in proposals or file notes, just like any other page in your tool.

Inside the tool, the layout is deliberately plain: one section page per topic, entries below it in date order, newest at the top. It should look like the rest of your docs. That's exactly why it gets used.

Does this stay copyright-clean?

Yes, as long as the agent summarises and links and skips full-text copies. That's precisely how it's built. What lands in the wiki is a summary in the agent's own words, plus source, date, and the link to the original. It doesn't store copies of articles.

The reason lies in copyright law itself. What's protected is the specific wording of a text; the information behind it (a change in the law, a new campaign format) is free. A summary in your own words takes the information and leaves the protected form with its author. Attribution stays visible in every entry, and anyone who wants to read further clicks through to the source. The traffic goes where the text came from.

For automated analysis there's also an explicit rule in Germany: § 44b UrhG permits text and data mining on lawfully accessible works; rightsholders can opt out in machine-readable form, and the agent respects that reservation. It only reads paid newsletters if you subscribe to them. So you still need the subscription: the summary shows you when it's worth looking at the original. If you want to redistribute content externally, say as a press review for clients, that's a separate topic with its own rules, and it leaves the internal wiki untouched.

Who decides which sources and topics get read?

You do. You set the source list and topic structure during onboarding, and both stay changeable at any time: sources come and go, topics get renamed or merged. The agent follows the list. Only your team can extend it.

Good source lists are shorter than you'd think. What works well are the official announcement channels of the platforms you work with, plus a handful of trade blogs and newsletters whose judgement you trust. In regulated industries, add government and court sources. Curating also means cutting: a source that mostly delivers noise makes the wiki worse, however diligently the agent reads. Industry monitoring stands or falls on this list; more on that below under the limits.

What does this look like day to day?

The pattern shows up most clearly where the ground rules of the work change faster than a team can read. Two typical use cases, plus our own.

Agency: platform updates under control

Ad platforms and algorithms change faster than the team can read. Clients still expect you to know. The agent keeps platform updates and industry news curated and current in the wiki, sorted by platform or client group. In the client meeting, the latest state is one search term away. And anyone new to the team reads their way in through the wiki, without someone having to recount the last few months out loud.

Law firm: changes in law and administration without the reading pile

Changes in the law, administrative circulars, new rulings, articles: the pile grows faster than the week. The agent curates the relevant changes by your topic areas, say by practice area or client group. The team reads summaries and picks where to go deeper; the original is always one click away. The professional judgement stays with the lawyer. The reading pile becomes a reading shortlist.

Our own case: the internal radar

We run the same build ourselves. AI tooling changes weekly, and our wiki gathers what our sources report, sorted by topic. The team reads curated summaries, and nobody blocks out reading time in the calendar for it.

How rollout works

Onboarding

You set the source list and topic structure, we set up the agent and connect your wiki tool. We look at the first entries together with you: is the sorting right, are the summaries at the right altitude? Then we adjust until it fits.

Operation

The agent runs as a Brixon Managed AI Employee on our side: we host and operate it, you work with the results. That costs €250 per agent per month. If a source drops out or a platform changes its format, we handle it; that's part of operation.

Approvals

For the first few weeks, someone from your team reviews new entries before they go visible to everyone. Later, spot checks are enough. Flag any entry that's sorted wrong or surplus and we sharpen the rules. Over time the wiki gets more precise, because it's calibrated on your corrections.

GDPR and data limits

The data limits in this case are tight and clear: the agent reads the external sources you've defined and writes into your wiki. It doesn't need access to client or customer data for that, so it doesn't get it. Personal data appears at most in the source texts themselves; what reaches the wiki is summaries with a source link. What the agent may read and write is set down in writing and can be checked at any time.

What the wiki cannot do

The wiki doesn't replace professional judgement. It relays what a source reports, cleanly summarised and linked. Whether an administrative circular has consequences for a specific client, or whether a platform update affects your campaign structure, is still assessed by a person with expertise. Before any recommendation with consequences, a look at the original belongs in the process; that's exactly what the link in every entry is there for.

The wiki curates, but it doesn't prioritise your decisions. It shows what's new, ordered by your topics. What's urgent today and what can wait stays a leadership call. A good wiki makes that call easier to answer. You still have to answer it yourself.

And the quality hangs on the source selection. The agent reads reliably, but it reads what's on the list. A weak list produces a tidy-looking wiki full of noise. So plan the upkeep of the list as a recurring task; a quick look each quarter usually does it.

There are also cases where the build barely pays off: if only one person needs the knowledge and two sources cover what's relevant, an inbox folder is enough. The wiki plays to its strength once several people need the same state and nobody has the reading time.

Whether it adds up for you is quickest to check against your real source list. Bring it to a short conversation and talk the task through with us: we'll go through it and tell you concretely how your wiki would look with it, in which tool and with which topics.