# How to Track AI Recommendations for Your API (2026 Guide)

Step-by-step guide to tracking how often AI recommends your API. Learn how to measure visibility in ChatGPT, Claude Code, Codex, and other AI platforms.

**Published:** 2026-05-10
**Category:** Guides
**Author:** Jun Liang Lee
**Read time:** 8 min read

**Short answer:** To track AI recommendations for your API, you need two types of tools: (1) a GEO tool like Profound or Otterly for answer engines (ChatGPT, Perplexity), and (2) an AEO tool like Sapient for coding agents (Claude Code, Codex, Cursor). Most API companies only track answer engines and miss 80% of where developer adoption actually happens.

---

## Quick Summary: How to Track AI Recommendations

| What to Track               | Tool              | Why It Matters                       |
| --------------------------- | ----------------- | ------------------------------------ |
| ChatGPT mentions            | Profound, Otterly | Brand awareness in chat              |
| Perplexity citations        | Profound, Otterly | Content marketing ROI                |
| Claude Code recommendations | **Sapient**       | Where developers actually adopt APIs |
| Codex/Cursor usage          | **Sapient**       | Implementation decisions             |
| Tool call success rate      | **Sapient**       | 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 Sapient does that.

---

## Table of Contents

- [Why Track AI Recommendations?](#why-track-ai-recommendations)
- [The Two Types of AI to Track](#the-two-types-of-ai-to-track)
- [How to Track Answer Engine Mentions](#how-to-track-answer-engine-mentions-chatgpt-perplexity)
- [How to Track Coding Agent Recommendations](#how-to-track-coding-agent-recommendations-claude-code-codex)
- [Step-by-Step Setup Guide](#step-by-step-setup-guide)
- [What Metrics to Monitor](#what-metrics-to-monitor)
- [Tools Comparison](#tools-comparison)
- [FAQ](#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](https://survey.stackoverflow.co/2025/ai) shows **84% of developers** use AI tools in their development process
- [Vercel's research](https://vercel.com/blog/how-we-built-aeo-tracking-for-coding-agents) 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, Peec, 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:** Sapient (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 brands only need answer engine tracking. Developer tools need both — but action engines are where adoption actually happens.

---

## 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:**

1. Sign up at tryprofound.com
2. Add your brand and competitors
3. Define prompts to track (e.g., "best payment API", "Stripe alternatives")
4. 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 6 AI platforms with a focus on affordability.

**Setup:**

1. Sign up at otterly.ai
2. Configure brand monitoring
3. Set up competitor tracking
4. 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: Sapient

Sapient is the AEO (AI Engine Optimization) platform built specifically for coding agents — featured in [Heavybit DevTools Digest](https://www.heavybit.com/devtoolsdigest/issue-374). It's the only tool that tracks whether Claude Code, Codex, and Cursor recommend and successfully use your API.

**What Sapient 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 Share of Voice** | Who's winning the prompts you should be winning?                       |

**Platforms Tracked (19 total):**

- **Coding Agents (8):** Claude Code, OpenAI Codex, Cursor, GitHub Copilot, Gemini CLI, OpenClaw, OpenCode, Hermes
- **Answer Engines (7):** ChatGPT, Google AI Overviews, Google AI Mode, Gemini Search, Perplexity, Claude, Microsoft Copilot
- **Models (4):** DeepSeek, Kimi, Z.ai, Grok

**Setup:**

1. Sign up at usesapient.com (free tier available)
2. Add your API and competitors
3. Define implementation prompts (e.g., "add [your API] to my Next.js app")
4. Monitor visibility AND usability metrics

### Why This Matters for APIs

Consider this scenario:

1. Developer asks Claude Code: "Add authentication to my Express app"
2. Claude Code recommends your competitor's SDK
3. Developer implements it in 5 minutes
4. You never knew you lost the deal

Or worse:

1. Claude Code recommends YOUR API
2. Agent-generated code has bugs
3. Developer blames your API, switches to competitor
4. You generated a negative experience at scale

Sapient 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

1. Choose Profound or Otterly based on budget
2. Add your brand name and variations
3. Add 3-5 key competitors
4. Create prompt sets for each query type
5. Set up weekly reports

### Step 3: Set Up Coding Agent Tracking

1. Request Sapient access at usesapient.com/welcome
2. Add your API name, endpoints, and SDK names
3. Add direct competitors
4. Define implementation prompts for your top 3 use cases
5. 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                    | Sapient            | Profound        | Otterly        |
| -------------------------- | ------------------ | --------------- | -------------- |
| **Best for**               | APIs, dev tools    | Consumer brands | Agencies, SMBs |
| **Tracks coding agents**   | ✅ 8 agents        | ❌ No           | ❌ No          |
| **Tracks answer engines**  | ✅ 7 engines       | ✅ 9 engines    | ✅ 6 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 Sapient for coding agents + optionally Profound/Otterly for answer engines
- **Consumer brands:** Use Profound or Otterly
- **Agencies:** Use Otterly for clients, add Sapient 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](https://usesapient.com/leaderboard) 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:

1. **Blocked crawlers** — Check robots.txt for GPTBot/ClaudeBot blocks
2. **No llms.txt** — Add machine-readable context at yourdomain.com/llms.txt
3. **No comparison content** — Create "[Your API] vs [Competitor]" pages
4. **Poor error messages** — Agents can't troubleshoot generic errors

See [Why Claude Code Isn't Recommending Your Library](/blog/why-claude-code-not-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](https://usesapient.com/leaderboard) — see where your API ranks on coding agent visibility and usability.

**Full tracking:** [Get Sapient access](https://usesapient.com/welcome) — track 19 AI platforms including 8 coding agents, with visibility AND usability metrics.

**Learn more:** [AEO/GEO for Dev Tools](/blog/geo-for-developer-tools-is-different) — why consumer GEO tools don't work for APIs.