Tiliter: Computer Vision for Visual QA and Object Counting
Tiliter helps companies in logistics, manufacturing, and retail turn images into structured data. It is designed for teams that need to automate visual inspections or verify delivery quantities without relying on barcodes.
At a glance
- Category
- Software Development
- Best for
- Logistics and warehouse managers, Manufacturing quality leads, Retail operations managers, Facility maintenance supervisors, Industrial site managers
- Pricing
- A free trial is available. Pricing was not clearly available from the provided evidence. Buyers should confirm current pricing on the vendor website.
- Key use cases
- Proof of Delivery Verification, Facility Cleanliness Monitoring, Industrial Part Verification, Retail Return Verification, Surgical Tray Validation
- Integrations
- Zapier, Slack, Power Automate
- Official website
- tiliter.com
Tiliter is a computer vision platform designed to automate visual workflows across various operational environments. It uses AI agents to identify products, count objects, and assess cleanliness, which may help businesses reduce manual effort in visual inspections.
The tool is intended for organizations in retail, logistics, manufacturing, and healthcare. It operates as a cloud-first solution, though some components may be adapted for edge use.
It supports a variety of visual tasks, such as verifying surgical trays in hospitals or counting industrial parts on a delivery truck. By combining computer vision and large language models, the system can recognize items by appearance even when labels are missing.
Buyers should confirm if their specific operational environment requires edge processing or if a cloud-first API approach fits their technical infrastructure.
Key Features
Counts specific items, such as people or products, within an image to provide structured totals.
Identifies products and components by appearance, supporting workflows that do not use barcodes or RFID.
Evaluates visual cleanliness and hygiene standards in facilities, providing structured scoring.
Extracts text from packaging and supports the validation of expiry dates or serial numbers.
Identifies visible wear, cracks, or dents on objects and industrial equipment.
Detects missing or required objects, such as PPE, tools, or signage.
Use Cases
Using object counting to verify delivered quantities of industrial parts against delivery notes.
Automating the verification of cleaning standards in public transport or shared facilities.
Matching images of arrived parts against engineering drawings to help identify errors.
Verifying that returned footwear matches the product and packaging.
Performing visual checks on surgical trays for healthcare logistics.
Best For
Integrations
Pricing
A free trial is available. Pricing was not clearly available from the provided evidence. Buyers should confirm current pricing on the vendor website.
FAQ
It uses a combination of computer vision and large language models to identify items by their appearance and visual context.
The platform is designed for straightforward integration and can use snapshots from existing cameras or mobile devices.
It is a cloud-first solution that generally requires internet access, although some components may be adapted for edge use if required.
Source category: Software Development
Source subcategory: Computer Vision
