Turn reviews
в prompts that work.

A different approach to product review analysis.

Product review analysis is the practice of using speech and text analytics, natural language processing, and machine learning to turn the reviews your customers write, star by star, into insight: what they value, what breaks, and how they actually feel about you. Product managers, CX teams, and market researchers have used it for years to spot patterns, catch quality issues early, and shape a roadmap around real customer voice instead of a hunch.

Why review analysis breaks down

For most teams the practice never matched the promise. Reviews piled up across Amazon, Shopify, Trustpilot, and a CSV export nobody opened twice. One analyst read a sample, wrote a summary, and called it done. The tools that could process volume stopped at keyword counts: a word cloud with no sense of which complaint was rising, which reviewer was a repeat buyer, and which one-star review was actually a shipping problem, not a product one.

Reading reviews, not just counting words

Speak AI treats every review the way a sharp product analyst would, at machine speed. Each review is read in your language, with 100+ supported, and analyzed on three layers: the words themselves, the tone and emotion behind them (frustration, delight, hesitation), and, where reviews arrive as video or voice, the visual and vocal cues too. Feature mentions, sentiment, competitor comparisons, and recurring pain points are extracted into structured fields your systems can use. Speak AI also picks the right model per task, whether that is Claude, ChatGPT, or Gemini, so the analysis is never locked to a single vendor.

Prompts teams actually run

• “Analyze the overall sentiment of this batch of reviews toward our product.”
• “Extract every mention of [feature] and summarize what customers say about it.”
• “Compare our reviews against [competitor]’s to show where we win and lose.”
• “Identify the recurring pain points across this quarter’s reviews.”
• “Pull the most quotable customer lines for a case study or landing page.”
• “Track how sentiment on [feature] has shifted month over month.”

From a pile of stars to a trend line

The result is review analysis that compounds instead of resetting every quarter. Feature complaints surface before they become a support crisis. Competitor comparisons update automatically instead of waiting for the next round of manual reading, and панели, которые вы можете настраивать и белой этикеткой track sentiment, feature mentions, and rating trends over time, so this month’s reviews are measured against last month’s, not read in isolation. The same engine that reads a batch of five-star reviews scales to far larger volumes: one legal intelligence firm ran the identical analysis pipeline across 5,100+ hours of case material and saved $700K+, proof the approach holds at real enterprise scale. Ask across your entire review history from Claude, ChatGPT, or Cursor through the MCP server, the same way you would query any other knowledge base.