Discover. Select. Convert.
One platform for the whole agent channel. See which AI agents discover and read your content, whether they pick you over rivals when a buyer asks, and which of those visits convert to signups and revenue — the traffic your web analytics can't see.
See which agents reach and read you
Before an agent can pick you or send you a buyer, it has to find and read you — and that traffic never touches your web analytics, because agents don't run JavaScript. Discovery reads the server side: which AI agents crawl your site, which pages and posts they pull, and which files they look for and can't find.
Agent traffic
who's reading you
Which AI agents crawl your site and how often — GPTBot, ClaudeBot, PerplexityBot, and the rest, named and counted.
Content reads
which pages they pull
The posts, docs, and landing pages agents actually read, so you know which content is feeding their answers.
Probe / Serve gap
the fixable leak
The difference between what AI crawlers request and what you serve them. High probe, low serve is a gap you can close.
Adoption signals
from your own stack
Private GitHub adoption tracking and log drains feed the picture from your own traffic, not just ours.
The Discovery score
Your Discovery score is the Probe/Serve gap read straight from your access logs: of the distinct paths AI crawlers actually requested, the share you served back. A 404 where a bot asked is a specific, fixable miss, and it is invisible to every tool that only checks public URLs — seeing it requires your traffic. Alongside it we show your agent readiness level from Cloudflare's scanner, rescanned weekly, with a ranked punch list of what to add.
Adoption signals from your own stack
Private GitHub adoption tracking shows whether agents that pick you actually keep you, and log drains feed the Probe/Serve gap from your own traffic — so the picture runs on your data, not just ours.
Know whether agents pick you
Pick Rate is the share of the time an agent picks your tool over its competitors on a real, unbranded task. We ask "add transactional email," never "use Postmark," and run it many times across Claude, GPT, Gemini on pinned model versions. You win or you don't. The proportion you win is your Pick Rate, reported with a confidence interval.
Pick Rate
the headline metric
How often agents choose you when they must choose. On coding tasks we parse the package actually imported; on conversational tasks a separate judge marks the primary recommendation.
Default & Shortlist Rate
the supporting cuts
Default Rate: how often agents commit to you without deliberating. Shortlist Rate: how often you're considered at all. Known-but-not-picked is a different problem than unknown.
Per-model breakdown
where you win, where you don't
Your number on each model and each surface (conversational vs coding agent), so you know whether a loss is universal or one ecosystem's.
Public leaderboards
the scoreboard
Every category has a live, public leaderboard on a rolling 28-day window. Your buyers — and their agents — can already see it.
Trace revenue back to the agent that drove it
Pick Rate tells you agents choose you. Agent Attribution tells you when that choice became a signup or a sale, and which agent did it. Install it on your own product and every conversion traces back to the agent that drove it. The headline is your Agent-Attributed Conversions — and the part no other tool can show is that we tie it to your Pick Rate, so selection and revenue sit in one place.
Every conversion is labeled by confidence
Most tools imply a precision they don't have. We tell you how sure we are about each conversion, and we lead with the ones we can prove.
Confirmed
highest certainty
The customer clicked a tagged agent link. A deterministic chain, not a guess.
Likely
strong signal
They arrived straight from an AI assistant's domain.
Inferred
lowest certainty
Self-report or timing lines up. Stated, never claimed as proof.
Influenced Conversions
the agent-era view-through
An agent read your page, handed the customer no link, and a conversion followed. The sale your analytics files under "direct" — surfaced as its own line, marked as correlation, never dressed up as a click.
Agent Reads & Top Agents
who reads, who converts
Which AI agents pull your pages mid-answer, and which ones actually convert — so you know where to invest your agent-facing surface.
Run it like a channel, not a report
Custom Evals
your matchup, your prompts
Any competitor set, your own or AI-generated prompts, the models you care about, one-off or recurring. Trace-level data in the viewer — the full prompt, the response, the scoring.
Alerts
know the moment it moves
Set a Pick Rate threshold or a rank-move trigger and get an email the moment a run crosses it. No dashboard babysitting.
Exports & share links
take the data with you
CSV/JSON export on evals and attribution, share links for reports, and webhooks that push conversions into your own stack as they happen.
Team-ready claims
one login, every tool you rep
Verified company claims gate the private data. One account can hold claims on multiple companies and switch between them in the dashboard.
Built API-first — for your stack and for agents
Everything on the dashboard is reachable by machine. That includes the agents themselves: Pickrate publishes the same discovery surfaces we audit you on.
REST API
public + authenticated
Free public endpoints for reports and leaderboards; Bearer-keyed /api/v1 for evals; secret-keyed export API for attribution.
Webhooks
push, signed
Conversion events POSTed to your endpoint as they happen, HMAC-signed (X-Pickrate-Signature), managed self-serve from settings.
SDK & browser helper
@pickrate/attribution + pr.js
A zero-dependency npm SDK for the server, a drop-in script for the browser. Publishable keys can't send revenue events.
MCP server
agents query us directly
Live Pick Rate over Model Context Protocol — getPickRate, getLeaderboard, lookupTool — plus an ARD registry and WebMCP.
Machine-readable site
llms.txt, .md, OpenAPI
Every key page negotiates to clean Markdown, the API ships an OpenAPI spec, and datasets carry schema.org markup.
Developer docs
hosted, current
Integration guides and API reference at /docs, with a machine index at /for-agents.
Which means you never have to open this dashboard
Connect Pickrate as an MCP server and your own agent answers from your live numbers — in the chat window or the terminal you already work in.
Honest measurement, careful data handling
No raw PII
hashed on arrival
End-user emails are hashed the moment they reach us — or send your own opaque user id and no email ever does. We never hold raw PII.
Key separation
pk_ can't forge revenue
Publishable browser keys can touch and identify but never convert; conversions require your server-side secret key. Revoke either anytime.
Signed egress
verifiable webhooks
Every webhook delivery is HMAC-signed so your endpoint can verify it came from us. Signing secrets are shown once and rotate on demand.
DPA & documented processors
procurement-ready
A standing DPA, a published sub-processor list, and a security disclosure policy (RFC 9116 security.txt). See the trust page.
What's in each plan
| Feature | Free | Pro |
|---|---|---|
| Discover | ||
| Discovery — which agents crawl & read you | ✓ | ✓ |
| Content reads — which pages agents pull | ✓ | ✓ |
| Discovery audit & Probe/Serve gap | — | ✓ |
| GitHub adoption tracking & log drains | — | ✓ |
| Select | ||
| Public report card & all leaderboards | ✓ | ✓ |
| Pick Rate, rank, and field size | ✓ | ✓ |
| Per-model & per-surface breakdown | — | ✓ |
| Default Rate & Shortlist Rate cuts | — | ✓ |
| Diagnosis cockpit — why you lose, and the fixes | — | ✓ |
| Competitor tracking & trend history | — | ✓ |
| Convert | ||
| Agent Attribution — which agents drove each signup | ✓ | ✓ |
| Confidence tiers (Confirmed / Likely / Inferred) + revenue | ✓ | ✓ |
| Signed webhooks & CSV export into your stack | ✓ | ✓ |
| History window | 30 days | Unlimited |
| Evals & alerts | ||
| Threshold & rank-move alerts | — | ✓ |
| Custom Evals (your competitors, your prompts) | — | ✓ |
| Eval API (/api/v1) & CSV/JSON export | — | ✓ |
Questions
How is this different from Profound, Peec, or a GEO tool?
Tools like Profound and Peec tell you if AI mentions you: share of voice, how often you're talked about. Pickrate tells you if AI picks you. When an agent must choose you or a competitor on a real task, does it pick you? And with Agent Attribution, whether that pick turned into a signup. Mentioned isn't chosen; chosen isn't paid.
Which models does Pickrate measure?
Pickrate runs real developer tasks across Claude, GPT, Gemini, on pinned model versions, and reports the proportion you win with a confidence interval.
What is the agent funnel?
The three observable stages of the agent channel: Discover (which agents crawl and read your content), Select (whether they pick you over rivals when a buyer asks — your Pick Rate), and Convert (which of those visits become signups and revenue). Pickrate measures all three in one place and regresses the discovery inputs against your Pick Rate to name the lever that moves your number.
Can I run my own evals with my own competitors and prompts?
Yes. Custom Evals (Pro) let you define any competitor set, write or AI-generate prompts, pick specific models, and run one-off or recurring. You get trace-level data in the viewer plus CSV/JSON export, share links, and the /api/v1 API.
What is Agent Attribution?
It's the bottom of the Agent Funnel. Install it on your own product and it traces each signup or sale back to the AI agent that drove it. Your headline is Agent-Attributed Conversions, and the thing only we can do is tie it to your Pick Rate, so the selection score and the revenue it produces live in one place.
How is this different from GA4's AI channel or a brand-visibility tool?
Those count AI traffic or how often you're mentioned. Agent Attribution measures whether an agent's choice converted, names the agent, and labels every conversion by confidence: Confirmed (a tagged-link click), Likely (an AI-domain referral), or Inferred (self-report or timing). It also surfaces Influenced Conversions, the agent-driven sale your analytics files under 'direct.' And it connects all of it to your Pick Rate, which a generic analytics tool can't.
Do you store our users' email addresses?
No. Send an email and we hash it the moment it arrives, then store only the hash. Or send your own opaque user id and no email ever reaches us. Either way Pickrate never holds raw PII.
How do we install Agent Attribution?
It's server-first. Install @pickrate/attribution and make three calls (touch, identify, convert), or POST those events to our endpoint from any language. A browser helper (pr.js) covers client-side funnels. It's rolling out now to teams already on our leaderboards.
See the agents on your site
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