gapfeed runs a continuous, multi-stage pipeline that listens at scale while maintaining strict evidence standards.

The Process

1. Signal Aggregation

We monitor public discussions, reviews, and forums across different high-signal sources using legitimate APIs and web data.

2. Normalization & Deduplication

Content is cleaned, and duplicates are removed using similarity detection.

3. Intelligent Classification

AI models assign categories, urgency, buyer intent, and sentiment. A fast pre filter removes noise early.

4. Clustering & Gap Formation

Related signals are grouped. Clusters meeting minimum evidence thresholds are turned into gaps.

5. Quality Assurance

Every potential gap is scored for specificity, actionability, and natural voice. Semantic deduplication prevents near duplicates.

6. Enrichment & Publishing

Gaps receive competitor context, heat scores, curated quotes, and static HTML pages for maximum accessibility and SEO.

Why This Architecture Works

The pipeline is designed to get smarter over time through query performance tracking and cross-source correlation; the same pain appearing on multiple platforms receives higher urgency.

This architecture lets us move beyond simple complaint tracking toward desire signals and rebound effects — the second-order problems created when solutions become too effective.

The entire pipeline is powered by multiple frontier AI models that were selected and tuned specifically for market intelligence work.