Mentions
Topic-level signals showing what a company is talking about and prioritizing
What is it
Mentions are topic-level signals detected from document sources that reveal what a company is talking about, investing in, or prioritizing — not what specific products they've installed.
Think of Mentions as the conversation layer. They capture themes like Machine Learning, Autonomous Vehicles, Edge Computing, Generative AI, and ESG (Environmental, Social, Governance).
The key distinction: technographics and verified installs answer "what products does this company use?" Mentions answer "what topics is this company focused on?"
What problem it solves
Installs tell you what a company already bought. They don't tell you what it's planning. Mentions close that gap by quantifying the themes a company is actively discussing — so you can find an initiative while it's still forming, before a competitor's product shows up in the stack.
They also solve the "how serious are they?" problem. Mentions are counted, categorized, and rolled into maturity stages, so interest is measurable rather than anecdotal.
What you can do with it
- Identify strategic priorities: See what themes a company is investing in (AI, IoT, 5G, sustainability) before they've bought anything
- Qualify for capability-fit: Selling an AI platform? Find companies with high Machine Learning and Deep Learning mentions
- Spot transformation initiatives: Cloud Migration, Microservices and Edge Computing signals indicate modernization projects
- Segment by maturity: HG rolls Mentions up into Operating Signal stages such as autonomous, 5g-transitioning and cloud-native
- Time account outreach: High automation_stage intensity plus low current AI installs equals a greenfield opportunity
Real-world example
Finding retailers investing in AI and automation
You're selling an AI/ML platform and want retailers investing heavily in automation and AI. You pull Walmart's Operating Signals.
automation_stage comes back as Autonomous with an intensity of 36,250 — Machine Learning (4,499), NLP (3,100), Deep Learning (2,728), Generative AI (1,032). iot_posture is Advanced Industrial at 3,323, cloud_posture is Private-First at 5,630, and network_modernization is 5G-Transitioning at 6,217.
Walmart is an AI leader actively talking about ML, NLP, GenAI, autonomous systems and edge computing at massive scale. This isn't a company dipping their toes — they're running. Your opening line writes itself, and it isn't a generic AI pitch.
What the data looks like
Mentions file — sample rows
| Company name | Mention name | Mention ID | First verified | Last verified | Intensity | Locations |
|---|---|---|---|---|---|---|
| The Home Depot Inc | Remote Work | 20894 | 2000-10-01 | 2025-04-30 | 770 | 19 |
| Allianz SE | Blockchain | 17530 | 2015-11-01 | 2024-03-13 | 43 | 2 |
| Walmart Inc. | Software Defined Network (SDN) | 17551 | 2011-02-01 | 2024-03-20 | 474 | 2 |
| CVS Health Corporation | 5G | 17626 | 2019-08-23 | 2024-03-27 | 35 | 2 |
| Vattenfall Energy Trading GmbH | Generative AI | 29765 | 2024-07-31 | 2024-07-31 | 1 | 1 |
Key fields you get
- Company name: the account the signal is attached to, resolved to a single HG Company ID
- Mention name: the topic being discussed — e.g. Remote Work, Blockchain, 5G
- Mention ID: stable identifier for the topic, so it joins cleanly across files and refreshes
- Mention description: HG's definition of the topic, so everyone reads the signal the same way
- First / last verified date: when the topic first appeared and when it was last seen
- Intensity: how much the company is talking about that topic
- Locations: how many company sites the topic was detected at
How to read it
High intensity + recent last-verified date
Active, ongoing focus — Home Depot at 770 on Remote Work through April 2025 is a live priority, not an old signal.
High intensity, older last-verified date
Walmart's SDN signal is strong (474) but last seen in early 2024 — the conversation has cooled or moved on.
Low intensity, single location
Vattenfall's Generative AI mention (1) is a first flicker — early exploration, worth watching rather than pursuing.
Locations count
19 locations on Remote Work means the topic shows up across the org, not in one team or one job posting.
Long first-to-last span
Allianz has talked Blockchain since 2015 — a sustained theme, not a reaction to a news cycle.
Mention ID
The same topic carries the same ID everywhere, so signals join across accounts and refreshes without name matching.
Business impact
Increases: Better targeting on accounts already talking about what you solve
Increases: More relevant messaging tied to the themes an account is discussing
Increases: More expansion signals surfaced inside the existing customer base
Decreases: Less time spent on accounts with no topic activity in your space
Directional outcomes HG customers commonly report. Actual results vary by program and data application.
Under the hood
How it's unique to the market
Mentions come from HG's proprietary document corpus — job postings, corporate websites, press releases, filings and social content — rather than a third-party intent panel like Bombora or G2.
Typical intent data gives you a topic and a score. Mentions give you topic-level themes rolled up into derived maturity stages such as autonomous or 5g-transitioning, refreshed by continuous document processing rather than a separate intent subscription.
The edge: Mentions aren't just "they're interested in AI." They're quantified, categorized and staged — so you can see how serious and how advanced an account actually is.
How it's derived
Document ingestion: HG ingests millions of documents — job postings, corporate websites, press releases, SEC filings and social content.
NLP extraction: Natural language processing identifies topic references — themes, not product names.
Topic mapping: Detected topics are mapped to HG's topic taxonomy, so "we're building a GenAI chatbot" becomes Generative AI and Conversational AI.
Aggregation: Topic signals are rolled up to the company level with counts and recency.
Stage classification: HG algorithms classify companies into maturity stages based on topic mix and intensity.
Continuous refresh: New documents are processed and signals updated on an ongoing basis.
The key point: Mentions come from what companies say about themselves. Job postings are a goldmine because they reveal actual hiring priorities and tech investments.