iDrive Ventures Into Voice Search With Human-Powered OOLOO App
Known primarily for its cloud backup solutions, iDrive is making an unexpected pivot into voice-assisted search with its new mobile application OOLOO. This innovative platform replaces algorithmic responses with real human answers, creating what could be described as a “crowdsourced Siri.”
The Human-Powered Search Landscape
While the concept isn’t entirely novel, OOLOO enters a challenging market space:
Historical Precedents:
- Mahalo (2007) initially offered human-curated search results before pivoting
- Jelly, founded by Twitter’s Biz Stone, attempted a similar Q&A model with mixed success
Current Challenges:
- Google’s algorithmic dominance has shaped user search behavior
- Niche platforms like Quora thrive on quality content but struggle with mainstream adoption
How OOLOO Works
The app features a straightforward interface:
- Users voice-record their queries
- Questions are routed to human responders
- Answers arrive via push notification, typically within 1-2 minutes
- Responses often include supporting web links for further reading
“We believed existing voice assistants like Siri could be significantly improved,” explains Matthew Harvey, iDrive’s Business Development lead.
Early Adoption and Growing Pains
Since its recent launch, OOLOO has shown promising traction:
- 10,000+ queries processed
- Users in 50+ countries
- Currently limited to U.S. users due to overwhelming demand
The app warns of potential delays during peak usage periods, hinting at scalability challenges common to human-powered platforms.
The Future of Human-Assisted Search
While OOLOO’s long-term viability remains uncertain, its early adoption suggests there may be appetite for:
- Local business recommendations
- Quick translations
- Personalized directions
- Product inquiries
The app is currently available as a free download on iTunes and Google Play.
Caption: OOLOO’s clean interface emphasizes voice queries and human responses.
As the digital assistant market evolves, OOLOO represents an interesting experiment in blending human expertise with mobile convenience. Whether this model can compete with algorithmic efficiency will depend on its ability to maintain response quality and speed at scale.
