Scientific Workflow

From buyer prompt to evidence-backed action

A 5-stage pipeline engineered around statistical validity, reproducible sampling, and actionable content playbooks.

Define Your Question Panel

Targeted Queries

Instead of scraping arbitrary keywords, AmpliRank focuses on the exact questions B2B buyers ask when evaluating software. Organize questions across Discovery, Comparison, Alternatives, and Implementation categories.

Technical Detail: Teams select 10–50 core prompts representing high-intent evaluation stages. Prompts are versioned and immutable per run to ensure subsequent samples remain mathematically comparable.

Run Concurrent Sampled Scans

Multi-Model Execution

Our distributed queue worker dispatches concurrent queries to official provider APIs (OpenAI, Anthropic, Google, and Perplexity) using fresh, stateless sessions.

Technical Detail: Every response is captured in full with token usage, latency, provider model identifiers, and exact timestamps. Responses are cryptographically fingerprinted to ensure tamper-proof audit trails.

Extract Mentions & Citation Ground Truth

Source Provenance

Our extraction engine identifies brand mentions, placement order, sentiment context, and every cited web URL grounding the answer.

Technical Detail: Citations are verified against live domains, categorizing them into owned brand assets, competitor collateral, directory listings (G2, Capterra), and editorial publications.

Compute Statistical Confidence Intervals

No Inflated Scores

Unlike platforms that claim a single 'magic score', AmpliRank calculates Wilson score 95% confidence intervals on your mention rate based on sample size.

Technical Detail: If you appear in 8 out of 20 samples, your interval is [21% – 64%]. When your sample size grows to 100, the margin of error narrows to [31% – 50%]. This transparently reflects statistical reality.

Decide, Assign, & Verify Follow-up

Closed-Loop Actions

AmpliRank analyzes competitor citations to identify missing content assets, assigning clear hypotheses and review dates to team members.

Technical Detail: After publishing content changes, trigger a follow-up sample using the exact same protocol to observe whether models begin citing your new assets.

Start your first sampled baseline today

Create a workspace, define your core questions, and receive your first intelligence readout in minutes.

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