Overview
Customer reviews are one of the richest sources of product intelligence available to any brand, yet most teams still rely on manual sampling, star averages, and gut feel to make R&D, pricing, and procurement decisions.
Prism is an end-to-end review intelligence platform that changes that. It ingests millions of reviews, automatically classifies products into granular categories, and applies a suite of analytical modules covering failure taxonomy extraction, price-tier benchmarking, quality drift detection, and product clustering to answer the questions that actually drive business decisions.
Built for e-commerce, consumer goods, and retail teams, Prism delivers operational signals. Every complaint is mapped to a specific fix. Every price tier comparison reveals where premium truly outperforms budget and where it doesn't. All of it is accessible through an interactive dashboard, filterable by segment and drillable by year, with insights ready in just hours.
Business Problem
Brands and marketplace sellers are sitting on millions of reviews and getting almost nothing actionable out of them.
Star ratings hide more than they reveal. NPS scores don't tell your R&D team which failure modes are rising. Spreadsheets can't scale across hundreds of SKUs. And by the time manual analysis surfaces a pattern, the damage to sales and brand perception is already done.
The result is that critical decisions are still being made on incomplete data, missed signals, and expensive guesswork.
Business Impact
Fix the right problems first
Prism maps every 1 to 3-star review to one of 12 structured failure modes, each linked to a specific corrective action. Teams know exactly whether they're dealing with a packaging issue, a formulation problem, or a sizing inconsistency and can track whether it's getting better or worse over time.
Compare value perception across price tiers
Prism helps teams understand how customer feedback differs between premium and budget products, supporting better pricing, portfolio, and product improvement decisions.
Cut time-to-insight from day to hour
What used to take 7 days of manual analysis now runs in under 3 hours, automatically, on a recurring schedule, with alerts when something goes wrong.
Techstack
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