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 use human reviewers in production to verify and correct the output of their AI pipelines - which quickly becomes an unsustainable bottleneck. 

Our solution dynamically learns from our clients' unique domain specific human feedback data; provides recommendations whether to send the output of a LLM pipeline to a human for review; monitors the insights obtained from human reviewers at scale, while also flagging suspicious reviews to ensure consistent final outputs; and integrates easily with our clients' existing pipelines via a small number of simple API calls.
Scale human-in-the-loop for AI workflows in regulated industries
Title: Redouble AI: Scale human-in-the-loop for AI workflows in regulated industries | Y Combinator

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### Scale human-in-the-loop for AI workflows in regulated industries

Redouble AI is the solution to scale human-in-the-loop for AI workflows in regulated industries. Instead of having to rely on slow and expensive human reviewers, Redouble’s clients save 80% of staff costs and significantly improve the accuracy and quality of their outputs by integrating our tool into their workflows. Addressing an $11BN market, the company is already generating revenue with its first set of clients. The founders are two serial entrepreneurs (who have built successful companies in AI and health-tech respectively) and a data engineering veteran who has written more than 120 enterprise-grade software applications.



Redouble AI

Founded:2024

Team Size:3

Location:San Francisco

### Active Founders

### Martin Bittner, Founder/CEO

Martin Bittner is the Co-Founder & CEO of Redouble AI. Before that, he was the CEO of Arctoris, a pharma workflow automation company which he co-founded and led from inception to profitability. In terms of background, Martin is a physician-scientist, having obtained his MD from the University of Freiburg in Germany, and his DPhil in Oncology as a Rhodes scholar at the University of Oxford.



### Haotian Li, Founder

Haotian received his training in physics and computational chemistry from Zhejiang University and New York University. In 2017 Haotian cofounded the AI drug discovery start-up Redesign Science as CTO, designed and built the internal computational pipeline that increased the high through put screening efficiency by 100X.



### Andrey Santrosyan, Founder

Specializing in devising and executing holistic data and software strategies for drug discovery. Acumen in capturing, storing, and securing research data, constructing analysis platforms to facilitate swift decision-making and rapid optimization cycles. Led the development of over 120 scientific applications, contributing to the successful market introduction of seven pharmaceutical products.



### Company Launches

[### Redouble AI: Quality control for AI in regulated industries](

**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](mailto: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.
