customer_support · education · workflow
Brainly builds Snap to Solve with Google Vision AI, achieving 70% satisfaction and 6x photo query engagement
Students found typing queries on smartphones cumbersome, and Brainly needed a multilingual AI solution supporting users across 35 countries — alternatives they evaluated focused mainly on English and a few additional languages.
How it works
Common implementation structure
How this type of workflow is generally built, generalized across documented cases — not tied to any one vendor's stack. Click any stage to read what happens there. Specific products that implement these stages appear in “Tools commonly seen” below.
Stage 1 · Student snaps photo of question
Users snap a photo of their question with the Brainly app to initiate a Snap to Solve query.
Tools used
Vision AIOCRTensorFlowML KitAndroid
Outcome
Snap to Solve achieved 70% user satisfaction, drove 6x more engagement than typed queries, and boosted paid subscription numbers; improving machine learning algorithms with TensorFlow yielded 10x more instant answers.
What failed first
Other AI solutions evaluated by Brainly focused mainly on English and a few additional languages, failing to meet Brainly's multilingual requirements across 35 countries.
Results
Volume70%
Grounding & classification
Source type: vendor customer story
28 fields verified against source quotes.
computer visionknowledge searchocrspeech to textknowledge basefailure mode describedmetric backednamed customerproduction runtime claimedtools describedvendor confirmedworkflow describededucationcustomer satisfactionrevenue increasethroughput increasevendor customer storycustomer supportautonomous resolutionextract classify route