Review and UGC Insights for Product Teams

• Head of Product or Category
• Head of Quality
• Head of Customer Experience
• D2C Brand Manager

Customers tell you what is wrong with your products every day, in marketplace reviews, Q&A, Instagram comments, support tickets and return reasons. The feedback is spread across channels, often in Hinglish or regional languages, and reaches the product team as an average star rating.

We build an insights pipeline that collects reviews, Q&A, social comments, support tickets and return reasons from official seller exports, APIs and your own systems. An LLM classifies each item against your product taxonomy, such as size and fit, quality, packaging, delivery, fragrance or skin reaction, at the aspect level and in English, Hindi, Hinglish and regional languages. Mentions are linked to SKU and, where possible, batch or lot. Spike detection flags themes rising faster than usual for a SKU. Product and quality teams get a weekly digest with counts and representative quotes, and safety-related mentions go to quality at once.

• Collects reviews, Q&A, comments, tickets and return reasons from official exports, APIs and internal systems
• Aspect-level classification against your taxonomy, in English, Hindi, Hinglish and regional languages
• Links feedback to SKU, variant and, where available, batch or lot
• Detects rising themes per SKU and alerts the owning team
• Routes mentions of skin reactions, injuries or contamination to the quality team immediately
• Weekly digest with counts, trends and representative quotes linked to the source

1. 01Collect feedbackReviews, Q&A, social comments, tickets and return reasons are pulled daily from approved sources.
2. 02Clean and linkPersonal details are stripped, and each item is linked to SKU, variant and batch where possible.
3. 03Classify by aspectThe LLM tags each item with aspects and sentiment from your taxonomy, with a confidence score.
4. 04Detect safety mentions and spikesSafety-related mentions and unusual rises in a theme are flagged.
5. 05Human reviewQuality assesses safety mentions, and analysts check low-confidence items and approve taxonomy changes.
6. 06Publish insightsDashboards and a weekly digest go to product, quality and CX teams.

1. Collection
   • Marketplace seller exports and APIs
   • Own-site reviews and Q&A
   • Helpdesk tickets and OMS return reasons
   • Social listening exports
2. Preparation
   • PII stripping
   • SKU, variant and batch linking
   • Language detection and transliteration handling
3. Classification
   • LLM aspect and sentiment classifier using your taxonomy
   • Safety mention detector
   • Spike detection per SKU and theme
4. Review
   • Analyst review queue for low-confidence items
   • Quality team alert inbox
5. Insights
   • Dashboard by SKU, theme and channel
   • Weekly digest with source quotes

The quality team assesses every safety-related mention, and analysts review low-confidence classifications and approve taxonomy changes. The system does not post public replies or contact reviewers.

• Marketplace seller portals and review exports
• Website review platform
• Helpdesk such as Freshdesk or Zendesk
• OMS for return reasons
• BI tools such as Power BI or Looker

• Data collected only through official exports, APIs or with platform permission
• Personal details stripped before analysis
• Every count links back to source quotes
• Regular sampling of classifications against analyst labels
• Safety mentions routed to people, never closed automatically

• Reviewer names and handles are personal data under the DPDP Act 2023 and are removed before analysis.
• Collection follows each platform's terms of service.

• Retail, D2C & Quick Commerce

  Catalogue Enrichment from Product Images

  Read product photos and back-of-pack images to fill category attributes, mandatory declarations and listing copy for each marketplace, with every value checked before publishing.

• Retail, D2C & Quick Commerce

  Abandoned Cart and Win-Back Automation

  Recover abandoned carts and bring back lapsed customers with WhatsApp and email journeys built around cart contents and purchase cycles, measured against a holdout group.

• Retail, D2C & Quick Commerce

  Guardrail Testing for AI Shopping Assistants

  Adversarial testing of AI shopping assistants for unauthorised discounts, wrong prices, order-data leakage and unsafe product advice, with validated findings, fixes and a retest.

• Hospitality & Travel

  Review Response and Reputation Automation

  Collect reviews from Google, OTAs and Tripadvisor, draft specific replies in your voice, route complaints to the GM and turn recurring themes into weekly actions for each department.