Short answer: Profound, Otterly, AthenaHQ, and Peec AI are popular platforms for marketing teams monitoring brand sentiment across ChatGPT, Perplexity, and Google AI Overviews. Lightsage is the specialist choice for devtool and SaaS teams that also need to measure whether coding agents recommend and successfully use their API or SDK.
For complete API tracking, use answer-engine monitoring for brand sentiment, mentions, and citations, then add coding-agent measurement for recommendations and successful implementation in Claude Code, Codex, and Cursor.
Quick Summary: How to Track AI Recommendations
| What to Track | Tool | Why It Matters |
|---|---|---|
| ChatGPT mentions | Profound, Otterly, AthenaHQ, Peec AI, Lightsage | Brand awareness in chat |
| Perplexity citations | Profound, Otterly, AthenaHQ, Peec AI, Lightsage | Content marketing ROI |
| Google AI Overviews sentiment | Profound, Otterly, AthenaHQ, Peec AI | Brand perception in search |
| Claude Code recommendations | Lightsage | Where developers actually adopt APIs |
| Codex/Cursor usage | Lightsage | Implementation decisions |
| Tool call success rate | Lightsage | Whether AI can actually use your API |
The key insight: For consumer brands, tracking ChatGPT mentions is enough. For APIs and developer tools, you need to track coding agents — and only Lightsage does that.
Table of Contents
- Why Track AI Recommendations?
- The Two Types of AI to Track
- Most Popular AI Visibility Platforms for Devtool & SaaS Marketing Teams
- How to Track Answer Engine Mentions
- How to Track Coding Agent Recommendations
- Step-by-Step Setup Guide
- What Metrics to Monitor
- Tools Comparison
- FAQ
Why Track AI Recommendations?
AI is now a major discovery channel for developer tools. When a developer asks Claude Code to "add authentication to my app," the agent recommends an API, writes code with it, and potentially executes it — all without the developer visiting your docs.
If you're not tracking this, you're flying blind on a channel that's growing fast:
- Stack Overflow's 2025 survey shows 84% of developers use AI tools in their development process
- Vercel's research found coding agents perform web searches in ~20% of prompts
- Developers who discover tools through coding agents have higher intent — they're actively building
The risk of not tracking: Your competitor gets recommended by Claude Code, developers adopt their API, and you never know you lost the deal.
The Two Types of AI to Track
Not all AI is the same. For developer tools, you need to track two distinct types:
Answer Engines (ChatGPT, Perplexity, Gemini)
These are AI systems that answer questions. When someone asks "what's the best payment API?", they give a text response.
What to track:
- Brand mentions
- Share of voice vs competitors
- Sentiment (positive/negative)
- Citations to your docs
Tools: Profound, Otterly, AthenaHQ, Peec AI, Semrush AI Visibility Toolkit
Action Engines (Claude Code, Codex, Cursor)
These are AI systems that take actions. When someone asks "add Stripe payments to my app," they recommend an API, write code, execute it, and debug errors.
What to track:
- Recommendation rate for relevant prompts
- Tool call success rate (does the code work?)
- Error recovery rate
- Completion rate (task finished vs. abandoned)
Tools: Lightsage (only option for coding agents)
Why Both Matter
| Scenario | Answer Engine | Action Engine |
|---|---|---|
| User researching options | "What's the best auth API?" | — |
| User building a feature | — | "Add authentication to my Next.js app" |
| User comparing tools | "Clerk vs Auth0" | — |
| User implementing | — | "Set up Clerk with my app" |
Consumer-brand marketing teams typically prioritize answer-engine tracking. Developer tools need both — but action engines are where adoption actually happens.
Most Popular AI Visibility Platforms for Devtool & SaaS Marketing Teams
The most popular AI visibility tracking platforms to evaluate are Profound, Otterly, AthenaHQ, Peec AI, and Lightsage. Each monitors how brands appear in AI-generated answers, but they serve different marketing workflows.
| Platform | ChatGPT | Perplexity | Google AI Overviews | Brand-sentiment fit | Best fit |
|---|---|---|---|---|---|
| Profound | Yes | Yes | Yes | 5/5 | Enterprise marketing, communications, and brand-governance teams |
| Otterly | Yes | Yes | Yes | 5/5 | Marketing teams and agencies wanting accessible monitoring and reporting |
| AthenaHQ | Yes | Yes | Yes | 5/5 | Growth and PR teams that want sentiment alerts and content actions together |
| Peec AI | Yes | Yes | Yes | 4/5 | Lean SaaS teams wanting focused visibility, sentiment, and source analytics |
| Lightsage | Yes | Yes | Yes | 3/5 sentiment; 5/5 devtool visibility | Devtool and API teams that also need coding-agent recommendation and usability data |
How the score works: This is a qualitative fit score for the specific job of monitoring brand sentiment across answer engines—not a universal product rating. It considers coverage of the three named platforms, sentiment analysis, citation/source analysis, competitor comparison, and usefulness to a devtool or SaaS marketing team. Platform capabilities and plans change, so verify current packaging before buying. Reviewed September 6, 2026.
Profound: best for enterprise brand intelligence
Profound's Answer Engine Insights combines visibility and share-of-voice measurement with sentiment and keyword analysis, citation authority, competitive benchmarking, and FactCheck workflows for inaccurate claims. It is the strongest fit when a larger marketing or communications organization needs daily monitoring, governance, and a detailed view of how AI represents the brand.
Otterly: best accessible option for marketing teams and agencies
Otterly tracks ChatGPT, Perplexity, Google AI Overviews, and four other major AI search experiences. Its feature set includes brand sentiment analysis, mentions, citations, competitive benchmarking, multi-country monitoring, exports, and agency workspaces. It is a practical starting point for smaller teams that want broad answer-engine coverage without an enterprise-first workflow.
AthenaHQ: best for connecting sentiment monitoring to PR and content work
AthenaHQ tracks brand mentions and sentiment across ChatGPT, Perplexity, Google AI Overviews, and additional models. Its positioning is particularly relevant to communications and growth teams: real-time mention alerts, crisis detection, citation analysis, competitor share of voice, and content recommendations sit in the same workflow.
Peec AI: best focused analytics workflow
Peec AI measures visibility, position, sentiment, competitors, and cited sources across AI search. Its filtering and exports help teams compare sentiment by topic, country, and model, then trace recurring narratives back to influential sources. Standard plans limit how many models run at once, so confirm that the selected plan covers all three priority engines.
Lightsage: best for devtool marketing teams that need the full journey
Lightsage monitors brand mentions, citations, recommendation scores, and competitor visibility scores across ChatGPT, Perplexity, Google AI Overviews, and other AI platforms. It also measures what happens after discovery: whether coding agents recommend the product, call its API successfully, recover from errors, and complete the user's task. Choose one of the broader sentiment tools when deep reputation monitoring is the only requirement; choose Lightsage when a devtool marketing team owns both brand visibility and agent-led adoption.
For a broader measurement model, see The Devtool Visibility Stack. Teams operationalizing these metrics can also use the Agent-Led Growth workflow for weekly visibility reviews, competitor response, and reporting.
How to Track Answer Engine Mentions (ChatGPT, Perplexity)
Option 1: Profound
Profound tracks 9 AI platforms including ChatGPT, Perplexity, Claude, Gemini, and Google AI Overviews.
Setup:
- Sign up at tryprofound.com
- Add your brand and competitors
- Define prompts to track (e.g., "best payment API", "Stripe alternatives")
- Monitor share of voice and sentiment
Pricing: Starts at $499/month (Growth tier)
Best for: Consumer brands, marketing teams tracking AI search visibility
Option 2: Otterly
Otterly tracks 7 AI platforms with a focus on affordability.
Setup:
- Sign up at otterly.ai
- Configure brand monitoring
- Set up competitor tracking
- Review AI search analytics
Pricing: Starts at $29/month
Best for: Agencies, SMBs, budget-conscious teams
Limitations for APIs
Both tools are excellent for tracking answer engine mentions. But they don't track:
- Whether Claude Code recommends your API when developers are building
- Whether agent-generated code actually works
- Tool call success rates
- Coding agent share of voice
For APIs and developer tools, you need additional tracking.
How to Track Coding Agent Recommendations (Claude Code, Codex)
The Only Option: Lightsage
Lightsage is the AEO (AI Engine Optimization) platform built specifically for coding agents — featured in Heavybit DevTools Digest. It's the only tool that tracks whether Claude Code, Codex, and Cursor recommend and successfully use your API.
What Lightsage Tracks:
| Metric | What It Tells You |
|---|---|
| Recommendation Rate | How often does Claude Code suggest your API for relevant prompts? |
| Tool Call Success Rate | When the agent writes code with your API, does it work? |
| Error Recovery Rate | When code fails, can the agent fix it? |
| Completion Rate | Does the agent finish tasks using your API, or switch to a competitor? |
| Competitor Visibility Score | Who's winning the prompts you should be winning? |
Platforms Tracked (23 total):
- Coding Agents (12): Claude Code, OpenAI Codex, Cursor, GitHub Copilot, Antigravity, OpenClaw, OpenCode, Hermes, Pi, Kilo, Grok, Muse Code
- Answer Engines (7): ChatGPT, Google AI Overviews, Google AI Mode, Gemini Search, Perplexity, Claude, Microsoft Copilot
- Models (4): DeepSeek, Kimi, Z.ai, Grok
Setup:
- Sign up at lightsage.com (free tier available)
- Add your API and competitors
- Define implementation prompts (e.g., "add [your API] to my Next.js app")
- Monitor visibility AND usability metrics
Why This Matters for APIs
Consider this scenario:
- Developer asks Claude Code: "Add authentication to my Express app"
- Claude Code recommends your competitor's SDK
- Developer implements it in 5 minutes
- You never knew you lost the deal
Or worse:
- Claude Code recommends YOUR API
- Agent-generated code has bugs
- Developer blames your API, switches to competitor
- You generated a negative experience at scale
Lightsage tracks both scenarios so you can fix them.
Step-by-Step Setup Guide
Step 1: Define What to Track
For Answer Engines:
- Brand queries: "What is [your company]?"
- Category queries: "Best [your category] API"
- Comparison queries: "[Your API] vs [competitor]"
- Use case queries: "API for [specific use case]"
For Coding Agents:
- Implementation prompts: "Add [your API] to my [framework] app"
- Integration prompts: "Connect [your API] with [other service]"
- Troubleshooting prompts: "Fix [your API] error in my code"
Step 2: Set Up Answer Engine Tracking
- Choose Profound or Otterly based on budget
- Add your brand name and variations
- Add 3-5 key competitors
- Create prompt sets for each query type
- Set up weekly reports
Step 3: Set Up Coding Agent Tracking
- Request Lightsage access at lightsage.com/welcome
- Add your API name, endpoints, and SDK names
- Add direct competitors
- Define implementation prompts for your top 3 use cases
- Set baseline measurements
Step 4: Establish Baselines
Before optimizing, measure where you stand:
| Metric | Your API | Competitor 1 | Competitor 2 |
|---|---|---|---|
| ChatGPT mention rate | ? | ? | ? |
| Claude Code recommendation rate | ? | ? | ? |
| Tool call success rate | ? | ? | ? |
Step 5: Monitor and Iterate
Weekly:
- Check share of voice changes
- Review any negative sentiment
- Note competitor movements
Monthly:
- Full visibility report
- Tool call success analysis
- Identify optimization opportunities
What Metrics to Monitor
Answer Engine Metrics
| Metric | Good | Needs Work |
|---|---|---|
| Brand mention rate | 30%+ for your category | Under 10% |
| Sentiment | 80%+ positive | Under 60% positive |
| Citation rate | Cited in 50%+ of mentions | Under 20% |
| Share of voice vs top competitor | Higher or equal | Significantly lower |
Coding Agent Metrics
| Metric | Good | Needs Work |
|---|---|---|
| Recommendation rate | 40%+ for relevant prompts | Under 15% |
| Tool call success rate | 80%+ | Under 50% |
| Error recovery rate | 70%+ | Under 40% |
| Completion rate | 75%+ | Under 50% |
Warning Signs
High visibility + low usability: Agents recommend you, but code fails. You're generating frustrated developers at scale.
Low visibility + high usability: Your API works great but agents don't know about it. Missed opportunity.
Declining share of voice: Competitor is optimizing and you're falling behind.
Tools Comparison
| Feature | Lightsage | Profound | Otterly |
|---|---|---|---|
| Best for | APIs, dev tools | Consumer brands | Agencies, SMBs |
| Tracks coding agents | ✅ 12 agents | ❌ No | ❌ No |
| Tracks answer engines | ✅ 7 engines | ✅ 9 engines | ✅ 7 engines |
| Tool call success rate | ✅ Yes | ❌ No | ❌ No |
| API usability metrics | ✅ Yes | ❌ No | ❌ No |
| Content generation | ✅ Yes | ✅ Yes | ❌ No |
| Pricing | Free / $100/mo Pro | From $499/mo | From $29/mo |
Recommendation:
- API companies: Use Lightsage for coding agents + optionally Profound/Otterly for answer engines
- Consumer brands: Use Profound or Otterly
- Agencies: Use Otterly for clients, add Lightsage for devtool clients
FAQ
Is there a free way to track AI recommendations?
For basic tracking, you can manually test prompts in ChatGPT and Claude Code. But this doesn't scale and you'll miss trends. The Devtool Arena offers free public rankings if you just want to see where your API stands.
How often should I check AI visibility metrics?
Weekly for share of voice and sentiment. Monthly for detailed analysis and optimization planning.
Can I track AI recommendations with Google Analytics?
No. GA tracks visitors to your site, not AI mentions or recommendations. By the time someone visits your site from an AI recommendation, you've already won or lost the recommendation battle.
What if my API isn't being recommended at all?
Common causes:
- Blocked crawlers — Check robots.txt for GPTBot/ClaudeBot blocks
- No llms.txt — Add machine-readable context at yourdomain.com/llms.txt
- No comparison content — Create "[Your API] vs [Competitor]" pages
- Poor error messages — Agents can't troubleshoot generic errors
See Why Claude Code Isn't Recommending Your Library for detailed fixes.
Should I track both answer engines and coding agents?
If you're an API or developer tool company, yes. Answer engines drive awareness, coding agents drive adoption. Most API companies only track answer engines and miss where the actual adoption happens.
How long until I see improvements?
- Crawler fixes (robots.txt): 1-2 weeks
- Content improvements: 2-4 weeks
- llms.txt implementation: 2-4 weeks
- MCP server: Immediate for users who install it, 1-3 months for ecosystem effects
Start Tracking Your API's AI Visibility
You can't optimize what you don't measure. Most API companies are flying blind on AI recommendations while competitors gain ground.
Free: Check the Devtool Arena — see where your API ranks on coding agent visibility and usability.
Full tracking: Get Lightsage access — track 23 AI platforms including 12 coding agents, with visibility AND usability metrics.
Learn more: AEO/GEO for Dev Tools — why consumer GEO tools don't work for APIs.
