Turn reviews
into prompts that work.
Speak AI turns every product review into structured prompts you can run in one click: sentiment, feature feedback, and competitor comparisons, extracted and ready to act on. We build it with you.
The wins teams ship.
Time to a live product, hours saved per file, and dollars saved. Same platform, very different applications.
Legal tech company builds a white-label deposition platform, 8 months faster.
Global research agency launches a white-label qualitative research platform.
Legal intelligence firm processes 5,100+ hours of carrier calls, 95% faster.
Healthcare consulting firm cut session processing from 8 hours to 0.3.
E-commerce manufacturer centralizes call review and cuts it by 85%.
Recruiting firm cuts candidate report time from 5 hours to 10 minutes.
Bring real reviews. Leave with prompts.
A working session, not a sales pitch. No obligation.
You bring real reviews
An Amazon export, a Shopify feed, a folder of one-star complaints. Whatever your team reads by hand today.
We map your review taxonomy
Your feature list, your competitor set, your escalation thresholds. Your words, your weights. Not a template.
You see them prompted, live
Your own reviews, summarized and scored on your own criteria, with a rollout plan for the whole team.
Product review prompts for every kind of team.
The same engine, pointed at the reviews your team actually reads.
Marketplace review triage
Amazon, Shopify, and Trustpilot reviews summarized and sentiment-scored, so merchandising teams see what is breaking before returns spike.
Escalation flagging
Reviews mentioning unresolved complaints or repeat issues surfaced automatically, so support reaches frustrated customers before they churn.
Feature request mining
Every feature mention extracted and counted across thousands of reviews, turning scattered feedback into a ranked roadmap input.
Testimonial & UGC sourcing
The most quotable, specific customer lines pulled out and ready to drop into a landing page or case study.
Competitive benchmarking
Your reviews compared against named competitors on the same criteria, so positioning decisions rest on evidence, not opinion.
Client review reporting
Run review analysis for every client account on a branded workspace, with exports and the API.
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 dashboards you can customize and white-label 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.
Engineered with you, accurate from day one.
A generic AI tool starts from zero. We shape the fields, sentiment scoring, and prompts around how your team actually reads reviews: your feature taxonomy, your escalation thresholds, your competitor set. Then we prime the application on your existing review history so it is useful on the first import. You get structured data back, not just a word cloud.
- We design the context, fields, and scoring around your review taxonomy, not a generic template.
- Your historical reviews and CSV exports prime the knowledge base before go-live.
- Structured data on every review, queryable from Claude, ChatGPT, and Cursor through the MCP server.
Bring your applications into Claude, ChatGPT, and Cursor.
No terminal. No npm. No config. Speak AI's MCP server gives any assistant 100+ tools to search, analyze, and act on your knowledge base in about 60 seconds. It is the same layer your applications run on, wired into the hundreds of apps in your stack through an integrations layer and a full developer API.
One system of record for everything your team says.
In-person and virtual, in one place. No stitching together a meeting tool, a voice recorder, and three other apps. Speak AI captures it all into one searchable knowledge base your applications are built on.
Teams build on Speak AI.
Real feedback from teams using Speak AI for research, transcription, meetings, and client work.
Questions we get
Your first scorecard runs on a real recording during the consult. Team rollout takes days, not months, because we build it with you and prime it on your existing recordings.
Pooled usage, not per-seat, with no volume minimums. Pilots are credited in full. We scope pricing for your exact workflow on the call.
Speak AI handles 100+ languages, including conversations that switch language mid-sentence, and can translate in and out.
Yes. White-label deployments run on your own domain with your logo, including client platforms agencies resell, plus branded iOS and Android apps.
There is no single best model for every prompt. Claude, ChatGPT, and Gemini each handle nuance and instruction-following differently, and the right one depends on the task. Speak AI runs all three so you can compare outputs on the same batch of reviews instead of committing to one vendor.
Most teams learn prompt engineering faster by testing structured prompts against real data than from a single book. The shortcut is a clear ask, the reviews as context, and the exact output format you want back. Speak AI’s prompt library gives you working examples to start from on your own reviews.
A prompt reviewer checks whether a prompt actually produces the output you need before it runs at scale, catching vague instructions or missing context. In Speak AI, you can test a review-analysis prompt on a handful of files first, refine it, then run it across your full review history once it is dialed in.
The frameworks that hold up share the same shape: state the role, give the reviews as context, name the exact output you want (sentiment, features, comparisons), and specify the format. That is the structure behind every prompt in Speak AI’s product review library, and the one we help you customize on the call.
Enterprise builds support BAAs, custom data processing agreements, SSO, and data residency options. We share security documentation on request and scope each build to your requirements.
From a pile of reviews to prompts that work.
Book a free consult, bring a batch of real reviews, and watch them summarized, scored, and turned into ready-to-run prompts before the meeting ends. Consults include early access to new features, an extended trial, and implementation credits.