AI Reliability: A Major Bottleneck for Enterprise Adoption
AI and large language models (LLMs) have a ton of useful applications, but for all their promise, they’re not very reliable.
Startups Find an Opportunity in Improving LLM-Powered Apps
No one knows when this problem will be solved, so it makes sense that we’re seeing startups finding an opportunity in helping enterprises make sure the LLM-powered apps they’re paying for work as intended.
Composo: A Headstart in Solving the Problem
London-based startup Composo feels it has a headstart in trying to solve that problem, thanks to its custom models that can help enterprises evaluate the accuracy and quality of apps that are powered by LLMs.
A Custom Approach
The company combines a reward model trained on the output a person would prefer to see from an AI app with a defined set of criteria that are specific to that app to create a system that essentially evaluates outputs from the app against those criteria.
Composo Align: A Public API for Evaluating LLM Applications
The company recently launched a public API for Composo Align, a model for evaluating LLM applications on any criteria.
Funding and Plans
Composo has recently raised $2 million in pre-seed funding, which will be used to expand its engineering team, acquire more clients, and bolster its R&D efforts.
Competitive Advantage
Composo believes it has a first-mover advantage and a competitive moat due to the R&D required to create its custom models and the data it has accrued over time.
Industry Agnosticism and Competitive Threats
Composo has chosen to be industry agnostic, but still have resonance in compliance, legal, healthcare, and security spaces.
Future Plans
Composo plans to focus on scaling its technology across its clients this year.
Conclusion
Composo is addressing a critical bottleneck in the adoption of enterprise AI, which is the reliability of LLM-powered apps. With its custom models and public API, it is well-positioned to help enterprises evaluate the accuracy and quality of these apps.
Frequently Asked Questions
Q: What is the main problem with LLM-powered apps?
A: The main problem with LLM-powered apps is their unreliability.
Q: How does Composo solve this problem?
A: Composo solves this problem by using custom models that evaluate the accuracy and quality of LLM-powered apps.
Q: What are the benefits of Composo’s approach?
A: The benefits of Composo’s approach include its ability to evaluate apps on any criteria and its industry agnosticism, making it a valuable solution for companies across various industries.
Q: What are Composo’s plans for the future?
A: Composo’s plans for the future include expanding its engineering team, acquiring more clients, and bolstering its R&D efforts.
