One intelligence layer across paid, earned, and owned.
Most teams do not have a data problem. They have a making-sense-of-it-fast-enough problem, spread across tools that do not talk to each other. Meltwater is the single platform where monitoring, analysis, reporting, and AI live together, with Mira on top to turn it into action.
Media monitoring
Comprehensive online news, trade, print, and broadcast coverage, with journalist and outlet intelligence built in.
Social listening
Real-time conversation tracking across X, Reddit, and more, with sentiment, reach, and the people driving the narrative.
GenAI Lens
How your brand shows up inside the LLMs, prompt by prompt, model by model, with the sources shaping each answer.
Reputation intelligence
Narrative risk scoring, deepfake and impersonation detection, and a command center for issues and crisis.
Reporting
Automated daily and weekly reporting, plus executive-ready summaries with no manual compilation.
Mira, the AI layer
Ask in plain language across every dataset. Summaries, themes, recommendations, every answer cited. Reachable from Teams or Slack.
What we heard, in your words.
This is not a generic pitch. Everything below traces back to our conversations with your team. The job is one platform where issues and crisis, editorial, SEO / Geo, and brand all see what is theirs without losing the bigger picture.
How that maps to Meltwater
GenAI Lens, the way your team saw it.
The same flow we walked through together, tuned to TI's prompts and competitors. The third layer of visibility, alongside the news and social monitoring you already know.
Build the prompts
We reverse-engineer what your end consumer actually asks an LLM, across business units. Things like which US semiconductor manufacturers are most globally dominant, or who leads in AI computing hardware. Suggested variations widen the coverage.
Run them daily across the models
Each prompt runs every day across the major models, and every response is aggregated.
Layer on enrichments
Sentiment, key phrases, organizations, brands, people, products, and links sit on top, so you read themes about TI and about the competitors you name, not just raw responses.
Benchmark by model and prompt
See where TI shows up versus competitors and how it trends. In the sample, TI was strong in DeepSeek and ChatGPT but weaker in Claude, exactly the kind of model-specific gap your team can isolate and work. Cuts go all the way down to a single prompt.
Trace the sources
The most important part. Every answer is traced to what shaped it: earned media linked to the publication for journalist pitching, plus LinkedIn and Reddit posts for content ideation. Find unfavorable or missing citations, then fix them at the source.
Get weekly AI recommendations
A short, ranked list of actions each week. Hold zero negative sentiment where you are clean, press a share-of-voice lead where you are ahead, and convert unlinked brand mentions into citations back to ti.com. The output your team opens on Monday morning.
The cost is the fragmentation, not the tools.
TI is winning on financial discipline while ceding earned-media share of voice on AI-at-the-edge and software-defined vehicles to ADI and NXP. Meanwhile the comms stack is split across Muck Rack, TVEyes, Sprinklr listening, Doppel, and manual Google Alerts. Every handoff between those tools is where speed and signal leak out.
- Broadcast, social, news, and journalist data split across separate tools
- No view of how TI shows up inside the LLMs
- Manual aggregation on Google Alerts feeding leadership summaries
- Geo and issues teams working from different, disconnected data
- Brand-protection coverage isolated in a single-purpose tool
- One intelligence layer across paid, earned, and owned
- Daily LLM visibility scored by model, prompt, and competitor
- Mira drafts leadership-ready summaries, every claim cited
- Geo, editorial, and crisis in one workspace with governed permissions
- Reputation Intelligence absorbs the brand-protection workflow
Where we see the impact
Vendor spend
Folding three to four point tools into one platform typically removes a meaningful share of annual comms-tooling spend, often in the range of 20 to 40 percent once overlap is gone.
Analyst hours
Replacing manual aggregation and report-building with Mira gives comms teams back hours every week, time that moves from compiling to acting.
Reputational risk
Catching a leaked asset or a hostile narrative hours earlier, in the same place you already watch, is the kind of save that pays for the platform on its own.
Ranges are directional, framed for planning. Exact figures get built together against TI's actual tool list and team structure.
A global semiconductor leader runs its comms intelligence on Meltwater.
TSMC sits in the same world as TI: long product cycles, geopolitically sensitive coverage, and a comms function that has to track narrative across regions and analysts at once. It is one of several semiconductor and manufacturing references we can put in front of your team. We can arrange a peer conversation when the timing is right.
Your requirements, answered line by line.
This is built from the capability list your team scoped. Current state on the left, what Meltwater covers on the right. The honest notes are kept in, because that is what makes the rest credible.
Broadcast and social
| Capability | Current state | With Meltwater |
|---|---|---|
| Broadcast monitoring | TVEyes and Critical Mention, overlapping | Yes Recent broadcast, TV and radio, with minimal delay to aggregate. De-duplicated in one place. |
| Searchable transcripts and clips | Separate broadcast tool | Yes Keyword search inside transcripts, plus quick clip download and sharing for stakeholders. |
| Real-time social monitoring | Split across listening tools | Yes X, Reddit, and more in real time. LinkedIn is limited to syndications via X, an industry privacy limit, not a Meltwater one. |
| Sentiment and trend spikes | Manual, after the fact | Yes Sentiment, trending narratives, reach, and who is driving the conversation. |
Media, journalists, and reporting
| Capability | Current state | With Meltwater |
|---|---|---|
| News and trade coverage | Multiple sources | Yes Online news, trade, and journalist activity. We cross-reference any sources you want to confirm. |
| Journalist database | Muck Rack, current strength | Yes We can provide references from clients who switched from Muck Rack on accuracy and strength. |
| Automated and executive reporting | Manual compilation, Google Alerts | Yes Recurring daily and weekly reports, plus concise leadership summaries via Mira, in Teams or Slack. |
Unified view, AI, and risk
| Capability | Current state | With Meltwater |
|---|---|---|
| Cross-channel unified view | Split across tools | Yes Broadcast, social, and media in one place. Core competency. |
| Social driving media coverage | Not connected | Yes Our Social Echo metric connects narratives across platforms. |
| AI across monitor, summarize, report | Missing across tools | Yes AI in analysis, alerts, news briefs, journalist pitches, and Mira. Governed; not trained on your data. |
| Predictive risk on emerging coverage | Reactive | Yes Predictive analytics to flag a mention or new coverage forming. |
| Deepfake and impersonation | Doppel, single-purpose | Yes Impersonation and risk monitoring with real-time alerts. Expanded live with our partner. |
| Clear platform roles and permissions | Tool sprawl, unclear ownership | Yes User permissions by team. Social, journalist DB, and sensitive crisis searches each scoped to the right eyes. |
Source: the Meltwater requirements matrix scoped with your team, April 2026. We walk any line item live to show exactly what is on par, superior, or out of scope versus the incumbent.
Ready when you are to bring it together.
You have seen the platform and the team liked what they saw. The next step is shaping how this rolls out across issues and crisis, editorial, SEO / Geo, and brand, and how the consolidation comes together on a timeline that suits you.
Continue the conversation