THE CHALLENGE

Most AI Projects Never Reach Production

The gap between a promising AI experiment and a deployed system costs organizations months of rework and ground they can’t recover.

80%

of AI projects fail 1

Most AI initiatives stall before delivering value, not because the models are wrong, but because the path to production is broken.

1. RAND Corporation, August 2024. 

9+ months

from pilot to launch 2

Most AI projects take the better part of a year to reach production, compounding delays that erode business value before it’s ever realized.

2. MIT NANDA Initiative, July 2025.

95%

of GenAI pilots fall short 3

Most generative AI pilot programs fail to deliver meaningful results, leaving organizations with sunk costs and no path forward.

3. MIT NANDA Initiative, July 2025.

THE SOLUTION

Close the Gap With Trusted AI Workflows

Every package, model, and dependency is signed, verified, and consistent at execution so what works in development runs reliably in production.

Reproducible environments

Consistent packages and dependencies across every stage, from local experimentation to production.

Consistent deployment anywhere

Deploy across cloud, self-hosted, on-premises, and edge with environments that behave the same wherever they run.

Governed model delivery

AI and Software Bill of Materials (AIBOM and SBOMs) travel with every deployment. Governance enforced at every target.

Multi-model support

Build compound AI systems with LLMs, vision models, and embedding models on one platform.

Consistency in production

What your team approves is what runs in production, secured, verified, and tamper-proof from deployment to inference.