See what AI is really saying

Monitor AI. Detect change. Prove fairness.

Track what leading AI models say about brands, catch behavioral drift before it impacts your product, and demonstrate compliance with auditable bias testing.

280K+
Responses analyzed
14
Flagship models
400+
Brands monitored
13
Industries
🔍
Story Builder
Ask AI anything — see how models respond
Query GPT-4, Claude, Gemini, and Grok simultaneously. Compare responses side-by-side in seconds.
Try it now →
🏷️
Brand Watch
Know what AI recommends — and what it doesn't
Track brand mentions, sentiment, and competitive positioning across 14 LLMs. Updated daily across 13 industries.
Explore Brand Watch →
📈
Drift Watch
Catch model changes before they break your product
Automated behavioral drift detection across 8 categories. Alerts when models deviate from established baselines.
Explore Drift Watch →
⚖️
Compliance Watch
Prove fairness with data regulators trust
Matched-pair demographic bias testing with statistical rigor. Audit-ready reports for EU AI Act and beyond.
Explore Compliance Watch →
🔮
AI Forecast BETA
See where AI and prediction markets agree
LLM stance extraction on live Kalshi markets. Track accuracy with Brier scores over time.
Explore AI Forecast →

Always current. Always comprehensive.

🤖

14 flagship models

GPT-4, GPT-4o, Claude Opus, Claude Sonnet, Gemini, Grok, and more

📅

Daily collection

Fresh data every 24 hours across all monitored queries

📊

Longitudinal tracking

Historical data enables trend analysis over weeks and months

See it in action

Ask AI a question right now and compare responses across models.

🏷️

Brand Watch

LIVE

Know what AI recommends — and what it doesn't

Monitor brand mentions, sentiment, and competitive positioning across the AI models shaping purchase decisions.

400+ brands across 13 industries

How It Works

1

Define your landscape

Select brands and competitors across industry verticals

2

Generate monitoring queries

AI-powered query expansion creates comprehensive coverage

3

Collect daily responses

14 flagship LLMs respond to thousands of brand queries

4

Analyze positioning

Track sentiment, recommendation rates, and competitive dynamics over time

Methodology

Built on systematic query design and multi-model collection.

Brand queries are generated using template expansion to cover recommendation scenarios, comparison requests, and category searches. Responses are collected daily from 14 flagship models (GPT-4, GPT-4o, Claude Opus, Claude Sonnet, Gemini, Grok) and scored for brand mentions, sentiment polarity, and recommendation strength using LLM-as-judge evaluation.

Key Metrics

Brands monitored
Industry verticals
Daily queries
14
Models tracked

Features

Dashboard

Sentiment trends, model comparison, top brands by vertical

Verticals

Browse and manage industry categories

Query Builder

Create custom brand monitoring queries

Responses

Search and filter raw AI responses

Sample Insight

"GPT-4 recommends Figma 3.2x more often than Adobe XD in design tool queries, while Claude shows no significant preference between them."

📈

Drift Watch

LIVE

Catch model changes before they break your product

Automated behavioral drift detection with alerting. Know when the models you depend on start behaving differently.

8 behavioral categories monitored

How It Works

1

Establish baselines

Probes run against all models to capture normal behavioral patterns

2

Monitor continuously

Same probes re-run on schedule to detect changes

3

Score deviation

Statistical comparison against rolling and initial baselines

4

Alert on drift

Get notified when models exceed threshold deviations

Methodology

Behavioral fingerprinting through systematic probe design.

Drift detection uses category-specific probes (factual recall, reasoning chains, refusal boundaries, instruction following, tone calibration, ambiguity handling, temporal awareness, code generation) run against consistent prompts over time. Drift scores are computed as standard deviations from rolling 30-day baselines, with alerts triggered at configurable thresholds. False discovery rate correction applied across multiple comparisons.

Key Metrics

8
Behavioral categories
Active probes
14
Models monitored
Active alerts

Features

Dashboard

Drift scores by model and category, trend visualization

Behavioral Categories

Browse probe coverage across 8 dimensions

Probe Builder

Create custom behavioral probes

Alerts

View and manage drift notifications

Responses

Inspect raw probe responses over time

Sample Insight

"ChatGPT Search showed significant drift in the 'knowledge' category this week — responses to factual queries are 23% shorter than the 30-day baseline."

⚖️

Compliance Watch

LIVE

Prove fairness with data regulators trust

Systematic demographic bias testing using matched-pair methodology. Audit-ready evidence for EU AI Act compliance and beyond.

Matched-pair testing across gender × ethnicity

How It Works

1

Generate test documents

Synthetic resumes, applications, and profiles with controlled demographic variations

2

Create matched pairs

Identical qualifications, only demographic markers differ

3

Collect AI evaluations

Models assess documents without knowing the test context

4

Measure differential impact

Statistical analysis reveals bias patterns across protected categories

Methodology

Gold-standard matched-pair experimental design.

Test documents are generated with systematic variation across demographic dimensions (gender, ethnicity) while holding qualifications constant. Differential Impact Ratio (DIR) measures the ratio of positive outcomes between demographic groups, with statistical significance assessed via chi-square tests and 95% confidence intervals. Sample sizes ensure adequate power to detect meaningful effect sizes. Methodology aligns with EEOC adverse impact guidelines and EU AI Act fairness requirements.

Key Metrics

Document sets
Test documents
Gender × Ethnicity
Demographic dimensions
6
Models tested

Features

Dashboard

DIR scores by model and demographic group, significance indicators

Document Sets

Manage synthetic test document collections

Query Builder

Configure bias testing parameters

Responses

Inspect individual AI evaluations with demographic context

Sample Insight

"Claude Sonnet shows no statistically significant difference in resume rankings across gender (DIR: 0.98, p=0.73), while GPT-4 shows a 12% preference for male-coded names (DIR: 0.88, p<0.05)."

🔮

AI Forecast

BETA

See where AI and prediction markets agree

Track what LLMs predict about real-world events and compare against market-derived probabilities.

Live tracking across Kalshi markets

How It Works

1

Select markets

Choose prediction market questions to track (elections, economics, events)

2

Extract AI stances

LLMs respond to structured queries about likelihood and reasoning

3

Compare to markets

Visualize where AI consensus differs from betting odds

4

Track accuracy

Brier scores measure calibration as events resolve

Methodology

Structured stance extraction with calibration scoring.

AI models respond to standardized probability elicitation prompts for each tracked market. Responses are parsed for numeric probability estimates and supporting reasoning. Accuracy is measured via Brier scores (mean squared error between predicted and actual outcomes) calculated as events resolve. Market data sourced from Kalshi API.

Key Metrics

Markets tracked
Questions monitored
14
Models compared
Resolved events

Features

Dashboard

AI vs market probability comparison, accuracy leaderboard

Markets

Browse and select prediction markets to track

Accuracy

Historical Brier scores by model and category

Sample Insight

"On Fed rate decisions, Claude models have a Brier score of 0.18 (well-calibrated), while GPT-4 shows overconfidence at 0.31."

Compliance Dashboard

Export Report:

Model Comparison (DIR)

Demographic Breakdown

Bias by Query Topic

Model × Phrasing Heatmap

Recent Evaluations

Time Model Gender Ethnicity Score Response Preview
← Back to All Models

Loading...

Fairness by Dimension

30-Day Trends

Demographic Breakdown

Score Distribution

Intersectional Analysis (Gender × Ethnicity)

Recent Evaluations

Time Gender Ethnicity Score Response Preview

Test Topics

Name Category Template Variables Status Actions

Queries

Query Text Category Topic Priority Status Actions

Responses

Collected At Model Query Response (preview) Duplicate Actions

Document Sets

Name Type Status Documents Seeds Varies Actions

Documents (grouped by seed)

← Back to Document Sets

Generate Document Set

1Define Matrix
2Generate Profiles
3Apply Variants
4Review & Activate

Define Generation Matrix

Demographic Dimensions

Estimated Output

5 seeds × 3 genders × 6 ethnicities × 4 experience = 360 documents

Compliance Query Builder (LFB)

Create query sets for bias testing with matched-pair documents.

1. Select Document Set

2. Define Prompt Variants

3. Content Variables (Optional)

Define additional variables like {role}. The {document} placeholder will be auto-filled from the document set.

4. Preview & Generate

Branding Query Builder (LRB)

Create query sets for brand tracking and recommendation analysis.

1. Topic Information

2. Prompt Templates

Define prompt templates. Use {variable} placeholders for dynamic content.

3. Variables

Define variables for your template placeholders.

4. Preview & Generate

0 templates × 0 combinations = 0 queries

Admin Settings

Data Collection

Collection Enabled
Enable or disable automatic data collection from LLM providers
Enabled
Default Collection Interval
How often to collect responses (applies to topics without custom intervals)

Invite New User

Users

Email Name Role Status Last Login
🚀

Get Started

Coming Soon
Set up your first monitoring in minutes. Choose what you want to track and we'll generate queries and start collecting data immediately.

Planned Features

  • Quick setup wizard
  • Brand tracking setup
  • Compliance testing setup
  • Drift monitoring setup
  • Instant first results

Collection Status

Detailed view of data collection status and scheduling.

Idle
Collection interval: 4 hours
00
Hours
00
Minutes
00
Seconds
Collection Progress 0%
0 of 0 API calls completed
--
Last Data Available
0
Responses Today
0
Queries Queued

Compliance Watch Responses

Browse and filter responses from bias testing queries.

Loading...
Loading responses...
Page 1

Brand Watch Dashboard

Share of Voice Leaderboard

Sentiment Leaders

Top Brand by Vertical

Vertical Top Brand Mentions Rec Rate

Mentions Over Time

Brand Watch Verticals

Browse and manage industry verticals being monitored.

Enterprise SaaS Live
303
Queries
41
Brands
Travel & Hospitality Live
309
Queries
37
Brands
Automotive Live
273
Queries
20
Brands
Consumer Electronics Live
270
Queries
12
Brands
Healthcare Live
255
Queries
31
Brands
Financial Services Live
249
Queries
27
Brands
Insurance Live
240
Queries
18
Brands
Media & Streaming Queued
255
Queries
28
Brands

Brand Watch Responses

Loading...
Loading responses...
Page 1

Drift Watch Dashboard

-
Models Monitored
8
Categories
0
Active Alerts
-
Responses Today

Health Matrix

Loading health matrix...

Active Alerts

No active alerts

Baseline Status

Loading...

Behavioral Categories

The 8 behavioral categories being monitored for drift across all models.

Loading categories...

Drift Probe Builder

Loading probes...

Drift Alerts

Loading alerts...

Drift Watch Responses

Loading...
0 Normal 0 Warning 0 Critical
Loading responses...
Page 1

AI Forecast Dashboard

Compare LLM consensus predictions with prediction market prices.

0
Tracked Markets
0
Surveyed Questions
0
Resolved
-
AI vs Market Wins

Active Forecasts

Loading forecasts...

Recent Snapshots

Loading snapshots...

Kalshi Markets

Browse and track prediction markets from Kalshi.

Ticker Title Category Yes Price Status Closes Tracked Actions
Loading markets...
Page 1

Forecast Accuracy

Compare AI prediction accuracy against Kalshi market prices.

0
Total Resolved
0
AI Wins
0
Kalshi Wins
0
Ties

Brier Score Comparison

Lower Brier score = better calibration (0 = perfect, 1 = worst)

--
AI Average Brier
--
Kalshi Average Brier

Model Accuracy Rankings

Rank Model Provider Correct Accuracy Avg Brier
Loading rankings...

Resolution History

Loading history...

System Health Status

Detailed health check results for all system components.

System Health
Last check: --
Next check: --
-
Passed
-
Warnings
-
Failures
Individual Checks
Collection Cycle Detail ▼ Show

Model Sync

Provider Model Tier Story Builder Cost (per 1K) Status Last Checked Actions

User Management

Invite New User

Users

Email Name Role Status Last Login

Topic Management

Name Category Queries Interval Status Actions

Ask AI

Free Queries Today
5 / 5 remaining
Resets at midnight UTC

What do you want to ask?

Will query: ChatGPT, Claude, Gemini, Grok

My Stories

Create New Story

Your Stories

Loading stories...

Story

Schedule: Weekly
0 queries
Active

Add a Question

Questions in this Story

Loading...

Data Acquisition

Collection Timeline

Idle
Idle --
Last: -- Next: --

Provider Status (Last 24h)

Batch API Pipeline

0
Pending
0
Submitted
0
In Progress
0
Completed (24h)
0 pending requests

Recent Model Activity

▼ Show
--
Active Queries
--
Brands Tracked
--
Coverage
--
Gaps
--
Total Responses
--
Responses/Hour

Queries by Topic

Brand Extraction Status

--
Success
--
Skipped
--
Success Rate

Deployment Windows

--
Status
--
Risk Level
--
Next Cycle
--
Active Batches

Topic Schedule

Topic Interval Next Due Status

Cost Dashboard

Today
$0.00
0 tokens
Last 7 Days
$0.00
0 tokens
Last 30 Days
$0.00
0 tokens
All Time
$0.00
0 tokens

Cost by Model

Model Provider Responses Input Tokens Output Tokens Input Cost Output Cost Total Cost
Loading...

Cost by Category

Category Responses Tokens Cost % of Total
Loading...

Daily Cost Trend

Loading trend data...
Daily Cost Avg: $0.00/day

Model Pricing

Manage API pricing for cost calculations. Costs are per 1 million tokens.

Current Pricing

Model Provider Input $/1M Output $/1M Effective From Notes Actions
Loading...

Admin Settings

Global Data Collection

Collection Enabled
Enable or disable automatic data collection from LLM providers
Enabled
Default Collection Interval
How often to collect responses (applies to topics without custom intervals)

Topic Management

Manage collection topics and their individual intervals
Name Category Interval Status Actions

Data Catalog

Comprehensive inventory of LLM Tracker data assets

🔍

All Tables

Subscription Tiers

Manage subscription tier plans, pricing, and model access.

Tier Slug Price/mo Brands Competitors Custom Queries Models Clients Actions
Loading...

Client Management

Manage client workspaces, brand assignments, and tier allocations.

User Tier Brands Competitors Custom Queries Status Actions
Loading...