How It Works
From signal detection to actionable insights: our transparent, evidence-first approach to competitive experimentation intelligence.
Our Analysis Pipeline
Four steps to transform raw web signals into reliable competitive intelligence
Crawl signals
Feature flags, cookies, DOM changes
We scan websites for experimental signals using deterministic detection methods.
Validate/normalize
LLM as validator
Language models validate and normalize category/ICP fit without inventing data.
Score with transparent weights
Coverage, reliability, recency
Each signal receives confidence scoring based on multiple quality factors.
Present with provenance
Source links and timestamps
All insights include complete source attribution and verification trails.
[Pipeline Diagram]
Visual flow diagram placeholder: pipeline-diagram.png
Our Methodology
Deterministic first
We use feature-flag and cookie signatures, DOM differences, and content analysis for discovery. Our detection methods rely on observable, measurable signals rather than assumptions.
Validation, not invention
A language model normalizes and validates category/ICP fit; it does not invent data. LLMs enhance our analysis but never replace factual observation.
Per-signal confidence
Coverage, reliability, recency, and model agreement determine confidence scores. Every piece of evidence is weighted based on quality and verification.
Provenance everywhere
Source links and timestamps across insights ensure full traceability. You can verify every claim and understand exactly where our data comes from.
Trust & Privacy
Ethical Collection
We respect robots.txt, enforce rate limits, and never collect personally identifiable information.
Transparent Sources
Every insight includes source URLs, collection timestamps, and confidence indicators.
Quality Assurance
Multi-layered validation ensures accuracy and reduces false positives in our analysis.
Experience our methodology in action
See how our transparent, evidence-first approach delivers reliable competitive intelligence.