Kliq provides an image‑recognition AI platform that turns crowdsourced smartphone photos into structured, machine‑readable data about the physical world. By mobilizing a global network of users to capture street‑level, indoor, and agricultural imagery, the service eliminates costly expert visits and delivers up‑to‑date visual intelligence for industries such as logistics, insurance, and agritech. The end‑to‑end workflow—from capture to data extraction—enables scalable, real‑time digitization of environments that are otherwise blind spots for AI.
Funding
Funding not disclosed
Founders
Product
Problem
The physical world lacks systematic, digitized data at street level, inside buildings, and on farms, making it difficult for AI-driven operations to obtain up‑to‑date visual information. Traditional expert visits are expensive, slow, and cover only a small fraction of locations, causing data to become stale quickly.
Solution
Kliq offers a field data infrastructure that converts smartphone photos into structured, AI‑ready data. Users capture images with any smartphone, and the platform’s computer‑vision pipeline extracts objects, locations, counts, labels, and metadata automatically. The extracted information is validated and delivered as clean, labeled datasets that can be integrated directly into dashboards, model‑training pipelines, or APIs. By mobilizing a distributed network of contributors, Kliq provides real‑time visual intelligence at scale, reducing reliance on costly expert visits and enabling continuous, street‑level monitoring for operational decision‑making.
Target Audience
Primary customers are enterprises and organizations that require up‑to‑date visual data for operational efficiency, such as logistics firms, agricultural managers, property inspection services, and infrastructure monitoring companies.
Features
- Smartphone‑based photo capture workflow that activates contributors on demand across any geographic area
- Automated computer‑vision extraction of objects, counts, locations, and metadata from images
- Built‑in validation to ensure accuracy of the structured data before delivery
- Seamless integration options including dashboards, API endpoints, and data pipelines for model training
- Scalable network management that supports any volume of data collection from a distributed user base