Technical Documentation

Engine documentation and statistical reference

Explore how AmpliRank samples probabilistic AI answers, computes 95% Wilson confidence intervals, and exposes workspace intelligence via REST APIs.

Getting Started

Quickstart & First Scan

Set up your brand workspace, configure buyer queries, and capture your first sample baseline in minutes.

1. Workspace & Brand Setup

AmpliRank models brand intelligence around multi-tenant workspaces. Within each workspace, you define your primary brand entity and up to 10 direct competitors. Each entity requires an official brand name, canonical domain, and primary industry vertical.

2. Defining Buyer Question Sets

AI answer engines do not rank keywords; they answer natural language questions asked by buyers in research mode. Configure prompt templates across three core buyer intents: Category Exploration ('What are the top solutions for...?'), Head-to-Head Comparison ('Brand A vs Brand B'), and Technical Due Diligence ('Does Brand A support SSO and SOC2?').

3. Executing Your First Scan

Trigger a scan run across ChatGPT, Claude, and Perplexity. AmpliRank queries clean, unconditioned model sessions, parses the generated response text for brand mentions and positioning sentiment, and extracts every cited URL and root domain.

bashAmpliRank API v1

# Execute a synchronous scan via the REST API
curl -X POST https://amplirank.com/api/v1/brands/{brandId}/scans \
  -H "Authorization: Bearer amplirank_live_..." \
  -H "Content-Type: application/json" \
  -d '{
    "provider": "openai",
    "model": "gpt-4o",
    "promptId": "pr_cat_eval_01"
  }'

Methodology

Statistical Sampling & Confidence Intervals

Why single-query testing fails and how AmpliRank computes Wilson score intervals for defensible executive reporting.

The Non-Determinism Problem

Large language models operate probabilistically with non-zero temperature, continuous weight fine-tuning, and dynamic search retrieval augmented generation (RAG). Testing a single prompt once in a web browser produces anecdotal noise, not actionable marketing data.

Wilson Score Confidence Intervals

For every observed brand recommendation frequency, AmpliRank computes a 95% Wilson score continuity-corrected confidence interval. If your brand is recommended in 14 out of 20 samples (70%), the 95% interval is [48.1%, 85.5%]. We report both the point estimate and the interval bounds so executive teams make decisions on statistical evidence rather than false certainty.

API samples are not a complete view of every answer to every user. Every metric represents one answer to one question from one provider at a specific point in time.

Recommended Sample Sizes

For weekly operational tracking, we recommend 10 samples per critical prompt per model (margin of error ~±15%). For monthly executive briefs or board reporting, we recommend 30 to 50 samples per prompt (margin of error ~±8%).

API Reference

Workspace REST API & Webhooks

Integrate AI brand intelligence directly into your data warehouse, BI dashboards, and internal workflows.

Authentication

All REST API endpoints require a workspace-scoped API key passed via the standard Authorization Bearer header. Generate and revoke keys within Settings -> API Keys.

typescriptAmpliRank API v1

import { AmpliRankClient } from "@amplirank/sdk";

const client = new AmpliRankClient({
  apiKey: process.env.AMPLIRANK_API_KEY!,
});

// Fetch latest brand intelligence overview
const overview = await client.brands.getIntelligence("br_acme_corp");
console.log("Mention Rate:", overview.mentionRate);
console.log("Top Cited Sources:", overview.topSources);

Rate Limits & Idempotency

Standard API rate limits are 120 requests per minute per workspace. All write endpoints support Idempotency-Key headers to guarantee safe retries across distributed queue consumers.

Webhook Events

Subscribe to real-time events including scan.completed, citation.discovered, competitor.surpassed, and brief.generated. Payloads are cryptographically signed with HMAC-SHA256.

Agent Standards

llms.txt & Source Fact Publishing

Publish structured machine-readable facts and documentation to guide autonomous AI search agents and retrieval spiders.

What is llms.txt?

llms.txt is an emerging web standard that provides a curated, lightweight Markdown manifest of your company's core value proposition, product architecture, documentation, and pricing facts specifically formatted for LLM context windows and search crawlers.

Evidence Verification Before Publishing

AmpliRank enforces strict evidence linkage: you cannot publish fact claims to your live llms.txt feed unless backed by verifiable public documentation or official product specifications.

Ready to test against live AI answers?

Create a workspace to test prompt sets, inspect citations, and review statistical baselines.

Create workspace Read statistical methodology →