TL;DR: Redouble is the solution to scale human-in-the-loop for AI workflows in regulated industries.

The Problem

Companies are increasingly adopting LLMs to automate their workflows, but sufficiently accurate and consistent outputs are hard to obtain, especially in regulated industries where mistakes are costly. As a result, AI companies are using human reviewers in production to verify and correct the output of their AI pipelines.

This human review step quickly becomes a critical bottleneck for exponential growth:

1. Even if the output is accurate 95% of the time, there's no way to tell which 5% is wrong, so the human has to review all outputs.
2. The cost of human review increases linearly with the pipeline throughput
3. Even the best reviewers make mistakes, especially due to fatigue, when having to identify similar mistakes repeatedly.
4. Domain-specific workflows require domain expert reviewers who are hard to find and expensive to hire.

Production-level quality control in regulated industries is an inherently different problem from model performance evaluation, since one cannot optimize the prompts to cover all the edge cases nor prevent all the unacceptable variations in LLM output.

Our Solution

Redouble AI is purpose-built to operate within these “high stakes, few data points” areas.

To address the problems above, our solution:

1. Dynamically learns from your unique domain-specific human feedback data.
2. Provides recommendations on whether to send the output of your LLM pipeline to a human for review.
3. Monitor the insights from your human reviewers at scale while also flagging suspicious reviews to ensure consistent final outputs.
4. Integrate easily with your existing pipeline with just a couple of simple API calls.

In addition, we also surface succinct, actionable, and granular insights that can be used to further optimize your AI workflow. So you can continually improve your pipeline’s performance as you scale.

We understand that human-in-the-loop quality control is critical if you are working with AI in the legal, healthcare, insurance, or finance sectors. We are on a mission to ensure that AI can be safely used in these mission-critical spaces without breaking the bank.

Our Ask

We are looking for companies that provide LLM-based services in regulated industries and use human reviewers. If you use human reviewers in your AI workflow, or if you know people who do, we would greatly appreciate any introductions and will offer any help that we can in return.

Schedule a demo on Redouble.ai or reach us through founders@redouble.ai

The Team

Martin is a serial entrepreneur, Rhodes scholar, and MD-PhD by training. In his last venture as co-founder and CEO, he built a profitable tech-enabled life sciences company and raised over $15M in venture funding.

Haotian is a serial entrepreneur, engineer, and physicist by training. In his last venture as co-founder and CTO, he built an AI-driven biotech company and raised over $17M in venture funding.

Andrey is a software and data engineer and mathematical statistician by training. During his 25 years at Novartis and multiple startups, he has built more than 100 applications across software and data infrastructure.