The Problem

Enterprises are eager to integrate AI into their operations—but most current solutions fail to meet the performance, security, and reliability standards required for real-world deployment.

Despite advances in large language models (LLMs), organizations still face five critical barriers:

1

Inconsistent AI behavior

LLMs often produce variable outputs even with the same inputs, undermining confidence in workflows like claims processing or compliance reviews.

2

Security and data exposure

Many architectures route sensitive data directly through LLMs—risking leaks and violating HIPAA, GDPR, or SOC 2.

3

Lack of systemic reliability

Linear execution models create fragile pipelines with single points of failure.

4

No observability or recovery tooling

Most systems lack built-in monitoring, alerting, and automated recovery, leaving teams blind to failures or SLA breaches.

5

Poor integration with existing infrastructure

Connecting to EMRs, legacy APIs, or compliance workflows often requires extensive custom engineering.