Inference Engine | CloudFactory

Training Engine

How models should be trained for high-stakes AI

CloudFactory’s Training Engine helps organizations with structured feedback, fine-tuning, and robust validation, so AI performs as expected in the real world.

Prompt engineering

Supervised fine tuning

Red teaming

Reinforcement learning from human feedback (RLHF)

AI Trust Gap

"Thriving AI projects depend on trust, alignment, and adaptivity."*

67%

of CEOs report low trust in AI deployment—even with high expectations for its performance.

(PWC, 2024)

80%

or more of AI projects fail—twice the rate of failure for traditional IT projects.

(Rand report, 2024)

Poorly trained models: lead to hallucinations, bias, and failure in production.
Inconsistent prompts: result in unreliable outputs from unstructured data.
Lack of human feedback loops: causes models to drift and degrade over time.
Unvalidated models: risk unsafe, non-compliant behavior.
Generic models: underperform in high-stakes, domain-specific use cases.

*Forrester, 2024

Common AI needs

What we’re hearing from
clients

“I need to train my model on proprietary, expert-labeled datasets for better domain-specific performance, so I can increase reliability in regulated or complex environments.”

“I want to gain insight from an unstructured data feed that I have within my organization and deliver it as a report to my users.”

“I need to ensure my model won't produce unsafe or biased outputs before we go live, so I can reduce reputational, ethical, and compliance risks.”

“I need to align my model's responses with user preferences and social norms, so I can increase trust and satisfaction with our AI.”

“I need a structured process to gather human feedback and use it to continuously improve my model, so I can keep pace with changing needs and use cases.”

Meeting you where you are

An enterprise-ready AI platform service

Designed to fine-tune, align, and secure AI through human-guided learning, prompt precision, and stress-tested reliability

Human feedback
Rankings, preferences, and corrections

Ethical alignment
Adherence to moral/social guidelines

Specialized learning
Model adaptation

Dynamic prompting
Optimal model responses

Bias audits
Stress testing

Hallucination analysis
Correcting false information

Training Engine