Launching a Leading LLM 6 Months Faster

Helping a leading AI developer scale model capabilities and accelerate go-to-market by half a year.


Accelerating go-to-market for a world-class AI model

Invisible helped a leading AI developer scale model capabilities in record time by recruiting and mobilizing AI trainers, overcoming their previous challenges due to limited staffing resources and infrastructure.

Data tasks completed daily: 5,000+

Accuracy above benchmark: +10%

Go-to-market vs. expectations: -6 months

Problem: Scaling a manual process

A major AI development firm needed to create an AI chatbot to reliably answer user queries where previous contractors had failed to meet quality and volume targets.

Client researchers were overwhelmed in manual processes, spending valuable time on trying to scale adversarial natural language inference (NLI) and reinforcement learning from human feedback (RLHF) processes which were designed to reduce the risk of model hallucinations and improve quality.

This manual work was taking time away from strategizing and upgrading their model with technical expertise. The need for additional operators was mission-critical.

Solution: Strategic process partnership

To support this effort, Invisible rapidly hired, trained, and scaled coordinated teams of experts.

Invisible deployed two solutions to do so:

  1. A bespoke org structure tailored to coordinate people around the world in a dynamic, efficient manner.

  2. Comprehensive onboarding materials and training systems to deploy expert AI trainers at scale.

Operators were cross-trained for adaptability—teams were transferred to new processes within hours of reconciliation requests. Researchers had entire units of manual trainers to toggle at will.

Impact: Scale in every direction

Within 24 hours of being approached by our client, Invisible’s task force was processing over 500 data points. This scaled to 487 operators completing over 5,000 tasks per day in under two months.

Accuracy ratings of nearly 95% (10% above the benchmarking standard) enabled a public launch six months sooner.

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