ENTERPRISE AI SOLUTIONS

ENTERPRISE AI SOLUTIONS

Secure private model execution, retrieval systems and autonomous agent runtimes.

We build private model inference environments that protect your intellectual property while automating complex operational workflows.
Complete Data Sovereignty
Automated Decision Making

Executive Summary

Using public AI APIs leaks proprietary corporate data and customer records to external vendors.

Off-the-shelf AI models hallucinate because they lack integration with actual internal company databases.

The Cost of Inaction

Data Leakage Risk

Employees copying sensitive internal codebase and operational data into public AI chat portals.

Disconnected Insights

No systematic way for AI to read runbooks, contracts, or transaction history safely.

The KryoNex Solution Architecture

We deploy open-weights models (Llama-3, Mistral) in secure, private containerized runtime environments.

Vector search architectures are built to connect your documentation to the models securely.

Target State

Private Model Perimeter

Open-weights models running on isolated GPUs owned and controlled by your organization.

Context-Aware RAG

Retrieval systems that safely index internal knowledge bases to deliver accurate answers.

Measurable Outcomes

Complete Data Sovereignty

Zero business data ever shared with third-party AI model providers.

Automated Decision Making

Reliable, context-aware AI assistants resolving user support and operations tickets.

Private Inference Control

Run large language models inside your own secure perimeter, protecting proprietary data.

Agentic Workflow Automation

Deploy persistent AI agents that execute complex back-office tasks continuously.

Who Benefits From This Solution?

Financial Services

Automating loan document reviews using private, secure AI models.

Healthcare

Summarizing physician notes while strictly preserving HIPAA compliance.

The Orchestration Stack

Required Platform Capabilities

  • Private AI & Inference (KryoNex AIM)
  • Agent Execution Environments

Required Engineering Services

  • AI & Machine Learning
  • Custom Software Engineering

Typical Implementation Roadmap

Phase 01

Model & Context Setup

Deploy open-weights models and set up vector retrieval databases.

Model server activeDocument ingestion pipeline complete
Phase 02

Integration & Testing

Build user frontends and integrate AI API endpoints into workflows.

Chat assistant activeLatency checks optimized

Representative Technologies

LangChain / LlamaIndexTensorFlow / PyTorchReact / Next.jsPostgreSQL

Frequently Asked Questions

What hardware is required for private AI?
We support cloud-based GPU instances (AWS/GCP) as well as hosting on your physical, on-premises servers.

Request Technical Consultation

Skip the generic sales calls. Speak directly with a KryoNex Solutions Architect to map your current architecture, identify engineering bottlenecks and design a scalable path forward.

  • Architecture Mapping

    Review your current tech stack and bounded contexts with a senior engineer.

  • Execution Timelines

    Establish realistic milestones, engineering phases and capacity requirements.

Project Context

Tell us about the engineering challenges you are facing.