Market and competitive intelligence, explained
Twenty pages on how this work is actually done: what each term means, where the evidence comes from, how to judge a system, and what none of it can tell you. Written to be useful whether or not you ever buy anything.
What each term means, and where it stops
AI market intelligence
AI market intelligence is the continuous reading of what a market puts on the record, by software rather than by an analyst. What it covers, and what it cannot.
Read →Competitive intelligence
What competitive intelligence is, the four questions it answers, where the evidence comes from, and the ethical line that separates it from corporate espionage.
Read →Market research automation
Which parts of market research software can genuinely do, which parts still need a person, and how to tell an automated report you can trust from one you cannot.
Read →AI research agents
What an AI research agent actually is, how a multi-agent research system divides the work, and the failure modes to check for before trusting the output.
Read →Competitor monitoring
A practical guide to monitoring competitors: which signals are worth watching, which are noise, how often to check each, and how to keep it running past month two.
Read →Buying signals
A buying signal is public evidence that a company is about to spend. Which signals genuinely predict a purchase, which are noise, and how to act on them.
Read →Prospect intelligence
Prospect intelligence is the research done on an account before contact: what to look for, what actually changes a reply rate, and where personalisation goes wrong.
Read →Business intelligence, and what it does not cover
Business intelligence reads your own data; market intelligence reads the world outside. Why the two need different tools, and what happens when teams confuse them.
Read →Technology intelligence
How to work out what technology a company uses from public evidence, which sources are reliable, and what a detected technology does and does not tell you.
Read →Pricing intelligence
How to track competitor pricing from public pages, why a change needs two dated captures, and what a list price does and does not tell you about a real deal.
Read →How the work is actually done
How AI market intelligence works
The five stages an AI market intelligence system runs every night - collection, clustering, scoring, corroboration and writing - and what can go wrong at each.
Read →How to monitor competitors with AI
A practical setup for automated competitor monitoring: which competitors to pick, which sources to wire up first, how to tune the noise, and what to review weekly.
Read →How to find buying signals
Where to look for buying signals, how to tell a real one from noise, the window for acting on each, and how to use a signal without quoting it back.
Read →How to automate market research
A staged approach: what to automate first, the citation rule that keeps output defensible, and the checks to run before any research document is circulated.
Read →How AI research agents work
What happens inside a multi-agent research run, how work is divided and bounded, why most of the pipeline is deterministic, and how failures are contained.
Read →How to track competitor moves
A working method for tracking competitor moves: what counts as a move, how to date it, where to keep the record, and how to stop battlecards going stale.
Read →How to monitor company hiring signals
How to read job postings as intent: which boards to watch, what a cluster of roles means, how to separate growth from backfill, and the window for acting.
Read →How to track company funding
Where funding is announced, how to read a round without over-reading it, the spending window that follows, and which registers carry the record.
Read →How to monitor technology changes
How to detect that a company is adopting or migrating technology from job postings, public code and documentation, and how to avoid over-reading one detection.
Read →Competitive intelligence vs market research
The difference between competitive intelligence and market research, in question asked, method, cadence and output, and how to tell which one your problem needs.
Read →Or see it running.
A working demo on the companies you actually care about, not a recorded video.