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Volga Partners

Use Cases/Category 06

Customer Feedback and Product Intelligence

01Turning Customer Reviews into Product Insight 02AI Feedback Triage Quality Layer
Use case categories
01

Turning Customer Reviews into Product Insight

Customer Story 01 · Customer Feedback and Product Intelligence

The Challenge:

A worldwide e-commerce marketplace gets hundreds of thousands of product reviews every day in more than 50 languages. Customers describe the same problem in different ways, so it was hard to see what was actually going wrong with a product. Sorting them by hand would have taken hundreds of people and thousands of hours.

Effective Solution:

Volga's 500+ native-speaking experts read each review in its original language and broke it into clear parts, such as quality, delivery, or fit. Each part was labeled by what the customer really meant. Clear guidelines and layered checks kept every language consistent, and the experts' corrections helped the client's system learn.

Results:

The client can now hear its customers clearly in every language, and see exactly what they are unhappy about.

50+ languages, each read by native speakers
500+ language experts working as one team
2,000+ product listings and 10,000+ reviews broken into clearly labeled concerns
Recurring product problems spotted early
Complaint trends visible across every market
02

AI Feedback Triage Quality Layer

Customer Story 02 · Customer Feedback and Product Intelligence

The Challenge:

A software powerhouse gets thousands of user comments every week, most of them short, vague, and messy. It had built an AI to sort them, but needed people to check its work, fix its mistakes, and show where it could be trusted.

Effective Solution:

Volga built the human layer behind the AI. Reviewers checked every routing decision, corrected wrong tags, and removed spam and duplicates. Volga set up clear roles: triagers, auditors, product-area specialists, and a team to get approved issues to engineers quickly. Volga tracked correct calls, wrong calls, and manual fixes separately, so success meant accuracy, not just volume.

Results:

Engineers stopped sifting through noise and started receiving issues that were already checked and in the right place.

5,000-7,000 comments reviewed every week
Clear gains in the AI's accuracy across key product areas
Fewer comments landing with the wrong team
Less checking left for the client's own engineers
Faster path from a user's comment to a fix-ready issue
Solid data on where the AI can be trusted, ready for the next product areas
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