Media intelligence that reasons, not just reports.
Most monitoring tools hand you a dashboard and leave the thinking to you. Mintel.AI reads across news, broadcast, social, podcasts and open data, works out which story is actually moving, and explains why — with every claim traceable back to the piece it came from.
Meaning, not a metrics wall
A chart tells you a number went up. It does not tell you who moved it, whether it will keep moving, or what to say. Mintel.AI is built to answer the second question, and to show its working while it does.
Book a Walkthrough- Topics that assemble themselves, with no keyword list
- Every finding anchored to the item it came from
- Alerts that explain what moved, and what to do next
- Tone and share of voice against competitors you name
- A daily executive brief, written to be read in two minutes
- Export to PDF, slides or raw data, plus an API
What Mintel listens to.
Six surfaces, one reasoning layer. The failure mode of media monitoring is fragmentation — social in one tool, broadcast in another, and nothing that can see a story crossing between them.
Online news
Global and local outlets, wires, and the print editions that still set the agenda in their markets.
Broadcast & radio
Television and radio, transcribed, so a segment is as searchable as a headline.
Social platforms
The major networks, including the short-form video surfaces where most stories now actually accelerate.
Podcasts
Long-form audio transcribed and indexed, where an hour of conversation often says more than a press release.
Forums & communities
The threads where a story usually exists for a day before anybody reports it.
Open data feeds
Public registers, filings and APIs, for the signals that never appear as coverage at all.
What Mintel can do
Six capabilities, each designed around the same rule: never report a number without the reasoning and the source that produced it.
capabilities
Signal Discovery
Stories cluster themselves out of what is actually being published, so a narrative you never thought to track still reaches you. No keyword list to guess at, and no blind spot where you forgot a spelling.
Entity & Influence Graph
People, outlets and organisations as profiles rather than mentions: who amplifies whom, how reach travels between them, and how a voice's weight shifts over a quarter rather than a day.
Tone & Share of Voice
Sentiment broken out by claim instead of flattened to one score, measured against the competitors you name. You see which argument is winning, not just whether the mood is good.
Escalation Radar
Velocity, spread and the shape of the curve scored together, so a story is flagged while it is still accelerating and there is time to answer it, rather than after it has peaked.
Explained Alerts
Every alert arrives with why it fired, what changed, who is involved and a recommended next step. Thresholds are yours, and routing goes to email, Slack or Teams.
Briefings & Reports
A daily brief written for an executive reader, and a full report with the underlying coverage attached. Export to PDF, slides or raw data, on a schedule or on demand.
Ask it a question a dashboard cannot answer.
Not a chatbot bolted onto a reporting tool. Mintel.AI has read the coverage, so it answers in conclusions and shows the figures the conclusion rests on.
Mintel Assistant
Grounded in live coverage
Why is this story spreading?
Coverage of the pricing change is up sharply, but it is not being driven by the trade press — it started in a community thread and was picked up second.
- Volume vs. 7-day avg
- +310%
- Origin
- Community forum
- Peak surface
- Short-form video
- Window to respond
- ~36 hrs
- The first substantial post predates the trade coverage by nineteen hours, which is why the usual alerts missed it.
- Three mid-tier creators summarised that post for a much larger audience, and their framing is the one now being quoted.
- Trade outlets are reporting the reaction rather than the change itself, so correcting the trade story would not reach the people actually discussing it.
The leverage is with the original thread and the three creators, not with the publications.
Try another question:
Illustrative example. The figures above are written for this page, not drawn from a customer account.
Why not just use what you already have.
Every answer carries its sources
A general-purpose model will write you a confident paragraph about your coverage and cite nothing. Mintel.AI retrieves first and reasons second, so every sentence it produces is anchored to a specific item you can open and read.
Retrieval trace
Each claim lists the articles, clips and posts it was drawn from, with publication, author and timestamp.
Open the original
Click any supporting item and land on the piece itself, with the relevant passage marked.
Stated confidence
Where the evidence is thin, it says so rather than smoothing the gap over with fluent prose.
No silent drift
Summaries are regenerated when the underlying coverage changes, and the change is logged.
One reasoning layer over every surface
Fragmentation is the real failure of media monitoring: social in one tool, broadcast in another, and nothing that sees a story crossing between them. Mintel.AI puts every surface through the same engine, so a podcast mention and a front page are the same kind of evidence.
Published and broadcast
Online news, wires, print editions, television and radio, with transcripts for everything spoken.
Social and community
The major platforms plus the forums and communities where a story usually starts before it is news.
Long-form audio
Podcasts transcribed and indexed, so an hour-long conversation is as searchable as a headline.
Open data
Public feeds, registers and APIs, for the signals that never appear as coverage at all.
How it works.
Tell it what you care about
Your organisation, your competitors, your markets and the issues you are exposed to. Not a Boolean query — a description.
It reads everything
Across news, broadcast, social, podcasts, forums and open data, continuously, in the languages your markets actually use.
It works out what matters
Stories cluster, entities resolve, tone is scored by claim, and anything accelerating gets flagged with its reasoning attached.
You act, then report
Alerts where your team already works, a brief each morning, and a full report with the evidence attached when the board asks.
Built for the people who answer for it.
Communications teams
Knowing a story is turning while there is still a window to respond, and having the evidence to brief a principal in two minutes rather than two hours.
Public affairs & policy
Tracking how an issue is being framed across outlets and constituencies, and which voices are moving the frame.
Risk & security
Early warning on reputational and operational exposure, with escalation scored on trajectory rather than raw volume.
Agencies
Running coverage for a roster of clients out of one workspace, with per-client reporting that goes out without being rebuilt each month.
See it read
your coverage.
Send us the organisation, the competitors and the issues you watch. We will run them through Mintel.AI and walk you through what comes back — including anything it gets wrong.
Got questions? We’ve got answers.
How is this different from a social listening tool?
Listening tools are built to count. They are very good at telling you that mentions rose 40% and that sentiment is 62% positive, and they stop there. Mintel.AI is built to explain: which cluster of stories drove the rise, who amplified it, whether the curve is still climbing, and what a sensible response looks like. The counting is still there — it is just not the product.
Why not just ask a general-purpose model about our coverage?
Because it has no live access to your coverage and no obligation to cite anything. A general model will write a fluent, plausible paragraph about your media position that is partly invented, and fluency hides the invention. Mintel.AI retrieves the actual items first and reasons over them, so every claim has a source you can open.
Can it work in languages other than English?
Yes. Clustering, entity resolution and tone scoring run across the languages your markets use, and summaries can be produced in a different language from the source material. Terms of art are mapped rather than translated literally.
Where do the alerts go?
Email, Slack or Microsoft Teams, on thresholds you set per topic. Alerts are deliberately not uniform: a routine volume bump and an accelerating crisis do not deserve the same notification, so severity drives both the formatting and the routing.
Can we get the data out?
Yes. Reports export to PDF, slides or raw data, scheduled or on demand, and there is an API for pulling mentions, entities and scores into your own warehouse or dashboards. Nothing is locked in the interface.