Kandid
Kandid is an AI-powered photo delivery platform designed to instantly share event photos with attendees using facial recognition. After an event, photographers or organizers upload photos to Kandid, and attendees who have registered their face (typically through a quick selfie during event check-in or via a pre-event link) automatically receive all photos in which they appear. The system processes images in the cloud, detecting and matching faces within seconds, and delivers personalized galleries directly to each attendee's device through a dedicated app or web interface. The platform emphasizes privacy by only showing photos to the individuals in them, and it provides organizers with analytics on photo engagement and delivery metrics. Kandid uses advanced deep learning models for face detection and recognition, ensuring high accuracy even in crowded scenes and varying lighting conditions. It supports batch uploads, real-time processing, and integrations with event registration systems to streamline attendee onboarding. The tool is accessible through a web dashboard for event managers, and attendees can view and download their photos via a branded mobile app or web portal. Additional features include customizable photo watermarks, automatic cropping, and the ability for attendees to opt out or delete their facial data after the event. Designed for scalability, Kandid can handle events ranging from small gatherings to large conferences with thousands of participants. It reduces the manual effort of sorting and distributing photos, allowing photographers to focus on capturing moments while the AI handles the logistics. Organizers appreciate the instant gratification effect, as guests receive notifications right after the event, boosting social sharing and attendee satisfaction. The platform also incorporates privacy safeguards: faces are processed ephemerally, with encrypted data, and users must explicitly consent to face enrollment. No raw facial images are stored l
- Category
- AI/ML
- Pricing
- Free
Problem solved
At events, hundreds of photos are taken, but attendees often waste time scrolling through massive galleries to find pictures of themselves, while organizers struggle to manually sort and distribute photos. Kandid solves this by using AI facial recognition to instantly match photos to each attendee and deliver only their relevant images, eliminating the need for manual tagging and ensuring guests quickly receive and share their best moments.
Use cases
- Wedding receptions where guests instantly receive all candid photos they appear in
- Corporate conferences and trade shows for attendee photo delivery and brand engagement
- Birthday parties and private celebrations with personalized photo sharing
- Sporting events and marathons where participants get race photos matched by face
- School proms and graduation ceremonies for easy student photo distribution
- Music festivals providing real-time photo galleries to wristband-registered attendees
Audience
- Business Users
- Content Creators
- Enterprise
- Hobbyists
Pros
- High-accuracy facial recognition even in crowd scenarios
- Instant, automated photo delivery to attendees without manual sorting
- Privacy-centric design with opt-in consent and ephemeral face processing
- Scalable for events of any size, from small parties to mega conferences
- User-friendly interface for both organizers and attendees
Cons
- Performance can degrade in extremely low lighting or with heavy obstructions
- Requires attendee pre-registration or on-site face enrollment, which may create friction
- Limited customization of matching sensitivity for organizers
Official website: https://kandid.nexorium.io/
Canonical LambdaBase page: https://www.lambdabase.com/tools/kandid