How does brand mention tracking work across AI engines?

DemandSphere uses regex-powered entity matching to identify mentions of your brand, product names, and competitors across 10+ AI engines. Matches are highlighted visually in the full response content.

Can I track variations of my brand name?

Yes. Regex pattern matching lets you define multiple brand variations, abbreviations, and product names. You can also track social properties and partner brands alongside your own.

How many brands can I track?

There is no limit. You can track your own brands, partner brands, and competitors across all monitored AI engines with real-time alerts when mention patterns change.

Brand & Mention Tracking

Track Every Brand Mention Across AI

Sophisticated entity management with regex patterns, social properties, and brand categorization. Track own brands, partners, and competitors across every AI engine.

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Unlimited

Brands tracked

Regex

Pattern matching

10+

AI engines

Real-time

Alerts

app.demandsphere.com – Brand Mentions

BRAND TRACKING - YOUR BRAND VS COMPETITORS

Overview

By Platform

Sentiment

YOUR MENTIONS

1,847

18%

COMPETITOR AVG

1,240

12%

MENTION SHARE

32%

2.4%

SENTIMENT SCORE

8.4/10

0.3

BRAND MENTION SHARE

Your Brand32%

Competitor A24%

Competitor B18%

Competitor C14%

Competitor D12%

SENTIMENT TREND

Positive 72%

Neutral 21%

Negative 7%

The real product

Competitive brand positioning across AI search.

See where your brand sits relative to competitors in market presence and momentum. Track how brand mentions shift across AI platforms over time.

The Challenge

Simple Tools Can't Handle Complex Brands

Most AI visibility tools were built for single-brand, single-market tracking. Enterprise reality is far more complex.

Name Collision

"Jordan" matches Michael Jordan, Jordan the country, and Jordan Peele. Simple text matching creates noise that buries your actual Air Jordan mentions.

Product Line Sprawl

Pegasus, Vaporfly, Alphafly, ZoomX, Air Max, Free Run - each sub-brand needs its own matching rules. Competitors track "Nike" but miss 60% of product mentions.

Social Blind Spots

AI cites your YouTube running channel and Strava club, but competitors only track nike.com. You're missing owned-media citations that drive brand perception.

Entity Management

Configure Brands the Way You Actually Work

Text patterns, regex, social properties - and categorization for own brands, partners, and competitors.

Brand Entity Manager

Configured Entities

Nike Running

Own Brand

Pegasus

Own Brand

Strava

Partner

Adidas

Competitor

Hoka

Competitor

Brooks

Competitor

Text Patterns

Nike Running Nike Pegasus Air Pegasus Pegasus 41 nike.com/running

Regex Patterns

/[Nn]ike\s?(Pegasus|Vaporfly|Alphafly|ZoomX)/gi

Matches all product lines Case insensitive

Social Properties

Core Capabilities

Built for Enterprise Complexity

Sophisticated entity management that competitors simply don't offer.

Regex-Powered Entity Matching

Simple text matching fails for global brands. Our regex engine handles spelling variations, product lines, localized names, and complex patterns. Configure "Nike\s?(Pegasus|Vaporfly|Alphafly)" once and match every product mention across languages, markets, and AI engines - including "Nike Pégasus" in French responses.

Multi-language support Unicode handling Product line grouping Negative patterns

Brand Categorization

Tag entities as Own, Partner, or Competitor. Filter dashboards by relationship type, segment share-of-voice reports, and build alerts around competitive mentions.

Own brands Partners Competitors

Social Property Tracking

AI cites YouTube channels, Instagram handles, Strava clubs, and more. Track all your owned media properties - not just your primary domain.

YouTube channels Instagram / TikTok Strava / fitness apps

Global Multi-Brand Scale

Track hundreds of brands across dozens of markets. Nike, Jordan, Converse, Hurley - each with regional variations, product lines, and athlete endorsements.

Unlimited brands 200+ markets

Visual Intelligence

See Exactly What Matched

Visualize brand matches down to the prompt level. Know precisely which patterns triggered, in what context, and how you appear relative to competitors.

Prompt: "What are the best running shoes for a marathon in 2026?"

Persona: Serious Runner • Jan 15, 2026 • 2:34 PM

For marathon racing, the Nike Vaporfly 3 and Nike Alphafly 3 remain top choices among elite runners. The Vaporfly offers a lighter weight while the Alphafly provides more cushioning for longer distances.

Strong alternatives include the Adidas Adizero Adios Pro 4, Hoka Rocket X 3, and Asics Metaspeed Sky+. For training leading up to race day, the Nike Pegasus 41 is excellent for daily miles, and many runners pair it with Strava training plans.

Brooks and New Balance also have competitive options if you prefer more traditional cushioning over carbon plates.

Own Brand (5 matches)

Competitor (6 matches)

Partner (1 match)

Unlimited Personas

Track How Different Audiences See You

Configure unlimited personas to understand how AI responds to different user types. Each persona simply adds to your tracking volume - no artificial caps.

Serious Runner

Marathon, ultra

Beginner Runner

Couch to 5K

Budget Shopper

Value-focused

Elite Athlete

Pro, sponsored

EMEA Market

Regional focus

China Market

Regional focus

Sneakerhead

Collector, resale

Add More...

No limits

No persona limits. Unlike competitors who charge per-persona or cap your tracking, we simply count personas toward your total tracking volume. Add as many as you need.

API & Data Access

Prompt-Level & Row-Level Granularity

Full programmatic access to mention and citation data - from aggregate summaries down to individual prompt responses.

REST API

Query mention and citation data by brand, date range, engine, persona, or any combination. Paginated responses with complete metadata for every record.

JSON responses OAuth 2.0 Rate limiting Webhooks

Prompt-Level Data

Every API response includes the exact prompt, full AI response, match positions with character offsets, sentiment, and context. Perfect for qualitative analysis.

Full response text Match positions Sentiment scores Citation URLs

Row-Level Export

Export individual mention records for your own analysis. Each row includes all dimensions: brand, engine, persona, timestamp, sentiment, position, match pattern, citation URLs, and the complete response context. Stream to your data warehouse or download as CSV/JSON.

// Sample row-level data structure
{
  "mention_id": "m_8f2k9x3",
  "brand": "Nike Vaporfly",
  "brand_type": "own",
  "engine": "chatgpt-4",
  "persona": "serious_runner",
  "prompt": "best marathon shoes 2026",
  "position": 1,
  "sentiment": 0.87,
  "match_offset": 142,
  "pattern_matched": "Nike\\s?Vaporfly",
  "citations": ["nike.com/vaporfly", "runnersworld.com"],
  "timestamp": "2026-01-15T14:34:22Z"
}

All dimensions included Stream or batch CSV / JSON / Parquet Historical backfill

Native Data Warehouse Integration

Mention and citation data flows directly into Search Intelligence - our fully-managed BigQuery data warehouse. Build dashboards in your BI tool of choice, or replicate to Snowflake for unified enterprise analytics.

BigQuery-native storage - Your mention data lives in a fully-managed BigQuery instance. Query with SQL, join with your own datasets, build ML models.

BI tool connectors - Native connections to Looker Studio, Power BI, Tableau, and any tool that speaks SQL. No ETL pipelines to maintain.

Snowflake replication - Connect your Snowflake warehouse to our BigQuery instance and replicate data into your existing Snowflake datasets.

Automation-ready - Trigger workflows in Zapier, n8n, or custom scripts when mention thresholds are hit. Feed data into marketing automation platforms.

BigQuery

Looker Studio

Power BI

Tableau

Snowflake

DATA FLOW ARCHITECTURE

Brand & Mention Data

Search Intelligence (BigQuery)

Looker

Power BI

Tableau

Snowflake

Custom SQL

Why DemandSphere

What Competitors Can't Match

Purpose-built for enterprise complexity, not retrofitted from simple tools.

Regex Matching

Most tools offer text-only matching. We handle product lines, regional spelling, and Unicode.

Unlimited Personas

Competitors charge per-persona. We include unlimited - just adds to tracking volume.

SERP + LLM Unified

One platform for traditional search and AI visibility. No tool-switching.

BigQuery + Snowflake

Native warehouse integration. Others offer CSV export at best.

Get started

See it with your own data.

30-minute demo. We'll run it on your domain - no prep required.

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