Controlled Model Execution
This capability is for organizations that must leverage Large Language Models (LLMs) but cannot send proprietary data, customer information, or intellectual property to public APIs.
KryoNex integrates and manages private AI inference environments, allowing you to run powerful models entirely within your own operational boundary, complete with secure access to your internal databases.
Using public AI models for internal tasks often violates compliance requirements and leaks proprietary intellectual property.
Generic AI models lack the specific context required to be useful within complex, specialized enterprise workflows.
Hosting top-tier models (like Llama or Mistral) on dedicated hardware under your total control.
Building Retrieval-Augmented Generation systems that connect the AI to your internal data.
Creating secure, OpenAI-compatible endpoints so your internal software can talk to your private model.
Provisioning and optimizing the underlying cloud or on-premise compute required for fast inference.
Consulting on which models and hardware are required for your specific privacy and performance needs.
A dedicated engineering project to build the vector databases and data pipelines that feed the model.
Ongoing management, scaling and updating of your private AI infrastructure.
We deploy open-weights models onto dedicated, private compute infrastructure where no data is ever used for external training.
We architect Retrieval-Augmented Generation systems to securely connect the private model to your internal databases and documents.
We evaluate open-weights models against your specific task requirements and budget constraints.
We deploy the necessary GPU clusters within an isolated Virtual Private Cloud (VPC).
We build the secure data pipelines to feed your internal documents into the model's context window.
We deliver a secure, rate-limited internal API endpoint for your development team to consume.
They provide reasoning capabilities rivaling proprietary models, without the vendor lock-in or catastrophic privacy risks of sending data to third parties.
RAG prevents hallucinations by forcing the AI to read your actual, verified internal databases before formulating an answer.
Running your own hardware ensures predictable, low-latency response times for critical business applications.
We do not build AI toys or wrapper apps; we engineer the heavy data pipelines and infrastructure required to make AI actually work in production.
We architect systems where it is physically impossible for your data to be used to train external models.
We tune inference engines to squeeze maximum performance out of expensive GPU hardware, reducing your operational costs.
All inference infrastructure is deployed in restricted network segments or isolated private network environments with no public internet access.
We implement structured evaluation frameworks to validate AI responses against defined quality criteria before deployment.
We rigorously stress-test your private endpoints to guarantee they can handle peak internal demand.
You are handling sensitive PII, PHI, or highly valuable trade secrets that legally or strategically cannot leave your network.
You just need generic brainstorming tools, your data is already public and you have no strict regulatory compliance requirements.
We can deploy private inference on dedicated cloud GPU instances within your VPC or configure on-premise hardware for maximum security.
We support the deployment of leading open-weights models (such as Llama, Mistral, or specialized coding architectures) tailored to your performance needs.
We engineer secure, internal APIs that mimic industry standards (like the OpenAI API format), allowing your custom software to communicate seamlessly.
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.
Review your current tech stack and bounded contexts with a senior engineer.
Establish realistic milestones, engineering phases and capacity requirements.