Internals of building a Customer Sentiment Intelligence with SerpAPI + System One

Description and use

The CSI pipeline automatically collects, normalizes, scores, and routes feedback from Yelp and Google News, eliminating manual triage and speeding up critical issue response.

Problem

Manual review triage is a bottleneck; noisy data, inconsistent triage, late escalation, and unstructured records hinder CX teams.

Outcome

The CSI pipeline reduces manual triage workload, provides deterministic scoring, and routes feedback into clear actions, enabling faster and more consistent customer issue handling.

Workflow

• Collect data via SerpAPI (Yelp search, Yelp reviews, Google News) without scrapers
• Normalize data into a single schema
• Score each item deterministically across seven dimensions using a rule engine
• Route items into DISCARD, QUEUE, ANALYZE, or ESCALATE based on score
• For ANALYZE/ESCALATE items, optionally run LLM-based deep analysis if API key available

Evidence

Manual review triage is a critical bottleneck. As review volumes grow daily, high-priority signals like churn risks and serious complaints routinely get buried under a mountain of noise.

Source

CSI aggregates public data like Yelp reviews and Google News into a single command. It instantly normalizes, deduplicates, and scores each item across seven dimensions, using an explainable rule engine to route data into four clear business actions: DISCARD, QUEUE, ANALYZE, or ESCALATE.

Source

Three design decisions worth noting: Per-item failure isolation, Backend-agnostic scoring, LLM spend is gated.

Source