What was breaking before?
A growth stage business was steering strategy on outdated, hand collected data. That is the problem in one line: leadership meetings ran on competitor information gathered by hand, and by the time it reached the table it no longer described the market. The mechanics of the old process explain why. An analyst opened competitor websites, copied pricing and product changes into a spreadsheet, and built a summary slide when a meeting demanded one.
Collection happened in bursts, so the picture was only fresh on the day it was compiled. Sources multiplied faster than any person could watch them: websites, product pages, announcements, job posts, public filings. Important changes surfaced late or not at all, and the team could never tell which. Decisions still had to be made, so leadership made them on the best data available, knowing the data was stale. That gap between market reality and boardroom view is what we were asked to close.
What did we build?
We built an AI research workflow that monitors competitors and delivers a weekly briefing. The system watches an agreed list of sources, competitor websites, product and pricing pages, public announcements and other signals the client cares about, on a schedule instead of on request. When a source changes, the workflow captures the change, and an AI layer summarises what changed and why it might matter, in plain language rather than raw diffs.
Summaries collect into a single weekly briefing with the noise already filtered out, delivered automatically to leadership on the same day each week. Nothing publishes itself blind: the briefing format, the source list and the relevance rules were shaped with the client, and a human can review before anything lands in front of decision makers. The stack follows our usual pattern, automation for the plumbing, AI models for reading and summarising, and the client's own accounts holding everything.
Client identity and specific commercial metrics stay under NDA. What we publish is the shape of the system and the outcomes the client has approved. On a call we walk through live systems, not slides.
How does the system decide what to do?
Deciding what to include is a set of explicit rules, not a black box. Every monitored source carries tags: which competitor it belongs to, what kind of signal it produces, and how much weight a change from that source deserves. When the workflow detects a change, rules run first, an obvious update like a copyright year is discarded, a pricing or product change is kept and scored for relevance.
The AI layer then writes the summary and assigns context, what changed, how it compares to the previous state, and which strategic question it touches. Anything ambiguous escalates rather than guesses: unclear signals are flagged for a person to judge instead of being silently included or dropped. The weekly briefing therefore contains items a rule admitted, a model explained, and a human could veto. Control stays with the client, and every item can be traced back to its source.
What changed for the team?
Weekly strategic insights now arrive automatically, and several analyst hours are saved each week. Those are the two outcomes the client has approved for publication, and both change how the team works day to day. Leadership no longer waits for someone to compile a view of the market; the briefing lands on schedule whether the week was busy or not, so strategy conversations start from current information instead of a request for research.
The analyst time that used to go into copying, pasting and formatting is released for the work only a person can do, interpreting the findings, testing them against the company's plans, and recommending a response. The same shift shows up in meetings: less time establishing what the facts are, more time deciding what to do about them. The system did not replace the analyst. It removed the collection work sitting in front of the analysis.
How would this look in your business?
An AI research workflow fits any business where decisions depend on information someone currently collects by hand. Competitor monitoring is one shape of it. The same parts, scheduled monitoring, AI summarisation, relevance rules and a human gate, apply to supplier pricing, regulation changes, tender publications, industry news, or customer reviews. The pattern to look for is simple: a person periodically checks sources, copies what matters into a document, and distributes it, while everyone quietly knows the picture is out of date.
If that describes part of a business, the build conversation is short. We map the sources, agree what counts as a signal worth reporting, decide who approves the briefing, and wire delivery into the channel the team already reads. Everything runs on accounts the business owns, so there is no lock-in. Tell us which decisions run on stale information, and we will give an honest read on whether this pattern fixes it.
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Send one message describing where decisions wait on hand collected information. We reply with an honest read on what a research workflow can fix, what it will take, and whether it is worth building at all.
