10,000 reviews.
One clear signal.
Star ratings don't tell you why customers love or hate your product. We pull every review across Amazon, Flipkart, Myntra, and Walmart, classify sentiment, extract recurring themes, and turn raw text into decisions your product and brand teams can act on.
From review text
to product decisions
Clean review dataset
Full text, rating, verified status, timestamp, reviewer metadata, variant, helpful votes — structured per SKU.
Sentiment classification
Positive / neutral / negative at review and aspect level. Handles English, Hindi-English code-mix, and regional variants.
Theme & aspect extraction
"Battery life", "fit issues", "delivery damage", "smell" — the recurring concepts across thousands of reviews.
Complaint clustering
Grouped complaints with sample reviews, severity scores, and week-on-week trend.
Competitor comparison
Your review profile side-by-side with named competitors. What do customers love about them that they don't love about you?
Fake / incentivised review signals
Patterns consistent with inauthentic review activity, called out so your analysis isn't poisoned by noise.
Where it earns its keep
Product teams
What should the next version of the product change? Reviews hold the answer if you can read them at scale.
Brand & insights
Brand health tracking grounded in customer voice, not survey panels.
QC & category
Catch quality issues in the field long before they surface in returns or warranty data.
PE & market research
Review-derived product health metrics for diligence and category reports.