Invisible automated processes like invoice reconciliation, W9 processing, claim approval letters, and compliance support, resulting in significant cost and time savings.

Invisible's team stepped in to review partially completed conversations between different models. We analyzed the search results, reference material, and model responses.

Invisible's team worked within the client's platform to review model conversations from the research team and assess the level of accuracy of the model responses.

Invisible improved Cohere’s data quality and scalability, enhancing multilingual, coding, and reasoning capabilities to strengthen its enterprise-ready AI performance.

A top-tier AI research company urgently needed to evaluate potential data labeling partners through a high-stakes pilot. The challenge? They had less than 24 hours to spin up a structured annotation workflow involving code-based tasks, 50+ trainers, and an auditable backend—all while avoiding the pitfalls of legacy tools like spreadsheets.

Invisible responded by building and deploying a fully functional annotation interface and backend task management system in just 14 hours. The team delivered the pilot within 4 days, enabling the client to assess labeling quality with clarity, speed, and confidence. The result was a fast, flexible, and high-quality data labeling experience—without writing a single spec.