Honeyb API. Everything the platform knows, one GET away.

    The rank tracking API
    for AI search.

    How often ChatGPT, Gemini, Claude, Perplexity and the rest of the eight engines Honeyb tracks recommend you, who they cite, and what to do about it today. Clean JSON, evidence attached.

    See your data first
    Engines covered
    ChatGPTAI ModeAI OverviewsGeminiClaudePerplexityGrokDeepSeek

    Why this API exists

    Every data API ends at a chart. This one ends at an action. The same engine that tells Honeyb customers what to do each morning, exposed over HTTP.

    7

    endpoints, from raw mentions to a ranked next action

    Daily

    scheduled scans; every aggregate ships its sample count

    1 GET

    between your product and your customers' AI visibility

    The headline endpoint

    Recommendations, over HTTP.

    Honeyb weighs everything it measured today, ranks what would move your AI visibility most, and hands you the action with its reasoning and evidence. The platform shows it on a dashboard; the API hands it to whatever you are building.

    • Ranked, not raw. One next-best action per day, ordered by expected impact, instead of forty charts to interpret.
    • Reasoned, not oracular. Every recommendation carries its why: the prompt, the gap, the sources driving the current answer.
    • Executable. When the action is content, a draft from the content engine can come with it.
    GET /v1/recommendations
     
    {
    "date": "2026-08-12",
    "rank": 1,
    "action": "publish_comparison_page",
    "reason": "Competitor named in 8/10 sampled answers for
    your core buying prompt; cited from two roundups
    you are absent from.",
    "evidence": {
    "prompt": "best project management tool",
    "your_recommend_rate": 0.10,
    "competitor_recommend_rate": 0.80,
    "driving_sources": ["g2.com", "reddit.com"]
    },
    "draft_available": true
    }

    Endpoints

    Seven ways to ask one question: does AI recommend you?

    From the answer a buyer sees, down to whether the crawler behind it could reach your site, up to what to do about it.

    GET/v1/recommendations

    Today's ranked next-best action with the evidence attached. The endpoint the rest of the category does not have.

    GET/v1/visibility

    Recommend rate, average position and presence per engine, from repeated daily samples rather than one-off checks.

    GET/v1/mentions

    Every answer that named your brand: the engine, the prompt, the full text and how you were described.

    GET/v1/citations

    The sources shaping your category's answers: which domains engines cite, how often, and whether you are in them.

    GET/v1/share-of-voice

    Your slice of the answer against named competitors, tracked over time per engine and per prompt group.

    GET/v1/sentiment

    How the engines frame you: positive, cautious or negative, with the answer text as evidence.

    GET/v1/crawler-access

    Whether GPTBot, ClaudeBot, PerplexityBot and friends can physically reach your site, re-tested daily.

    The measurement layer

    Numbers you can put in front of a client.

    Recommend rate is the metric this category was missing: of the times buyers asked, how often were you the answer? Because AI engines answer differently run to run, every figure is computed across repeated samples, and the sample count is part of the payload. No mystery scores, no unexplained indexes.

    GET /v1/visibility
    {
    "brand": "acme.com",
    "engine": "chatgpt",
    "recommend_rate": 0.63,
    "sentiment": "positive",
    "sampled_runs": 30
    }

    Build with it

    Wherever you are building, the recommendation goes in.

    Inside your product

    Building a marketing platform, a CMS, an agency portal? Serve AI visibility and next-step recommendations to your users as a native feature, without building a five-engine sampling pipeline.

    Inside your agents

    Your automations and AI agents can pull today's recommendation, act on it, and re-check the recommend rate after. A closed loop with no human copy-pasting between dashboards.

    Inside your reports

    White-label the whole story for clients: here is where you stand, here is why, here is what we are doing next. The reason column comes straight off the endpoint.

    Inside your alerts

    Recommend rate dropped, a competitor took your slot, a crawler got blocked by a CDN update. Route it to Slack or a ticket the day it happens, not at the monthly review.

    Why it holds up

    Built for a surface where the same question gets different answers.

    Sampled, not spot-checked

    AI answers change between identical runs, so single checks are noise. Every number is computed from repeated scheduled samples, and the sample count ships with the response.

    Evidence included

    Aggregates come with the receipts: the underlying answers, the prompts, the cited sources. Your integration can show the why, not just the score.

    Decisions, not just data

    The recommendations endpoint returns what to do next, ranked, with the reasoning attached. Data APIs end at the chart; this one ends at the action.

    FAQ

    Frequently asked questions.

    Free to start

    Get recommended by AI search models.

    Run a free AI search visibility check, then let the Honeyb agent do the work that gets you into the answers.

    ChatGPTClaudeGeminiPerplexity