Enterprise Secure AI
We deploy open-source language models on your dedicated hardware, behind your firewall. No cloud, no third parties.
Secure, private, and enterprise-grade AI deployed on your own infrastructure, fully controlled by you. Our solution eliminates dependency on external providers while giving you access to powerful open-source models configured for your use case.
The Limitations of Cloud-Based AI for Enterprises
Modern AI is powerful, but relying on public APIs or cloud-based LLMs exposes companies to real risks. When sensitive data and intellectual property leave your boundary, privacy, compliance, cost, and control all become vulnerabilities.
Data leaving the company boundary, risk of leaks, compliance violations, or regulatory exposure.
Intellectual property or trade secrets at risk when relying on external APIs or cloud-based services.
Unpredictable operating costs due to pay-per-use or per-token pricing models, especially as usage scales.
Operational dependency on third-party providers, creating exposure to outages, service disruptions, or sudden policy/price changes.
What Makes Us Different
Our AI deployment operates fully on your hardware, giving you complete privacy, predictable pricing, and customizable performance. By integrating your knowledge base via RAG (Retrieval Augmented Generation), your self-hosted models become deeply informed about your company’s context, boosting relevance and responsiveness while eliminating reliance on third-party APIs.
True data privacy: No data leaves your organization
Custom intelligence: Models trained/augmented with your proprietary knowledge
Predictable costs: Fixed packages, no per-token surprises
Reliable uptime: No dependence on external API availability
Supported Models
We support a range of high-demand open-source AI models that can be configured for your needs, from general NLP and reasoning tasks to coding assistance and domain-specific workflows. These models run on your dedicated hardware with the right precision and quantization for performance and cost balance.
GPT-OSS (e.g., 120B / 20B)
Open-weight models from OpenAI that you can run locally, offering transparency, control, and performance comparable to some proprietary models.
GLM 4.6 / GLM Variants
Flexible models with efficient reasoning and coding support, often chosen for balanced, reliable performance
Kimi K2 / Kimi K2 Thinking
Models tuned for creative content and advanced reasoning, well-suited for complex prompts and iterative thinking tasks.
Developer Tools & IDE Integration
If your team wants to connect self-hosted models to developer environments like VS Code, or replace costly AI IDE tools (e.g., Cursor), we can support that as an optional integration. These workflows — such as real-time code suggestions or advanced agent tooling — often require larger models and more powerful hardware, and will be priced separately to reflect the added infrastructure and engineering effort.
VS Code and IDE-centric coding AI support with Real-time suggestions & inline assistance
Team-wide, secure development workflows
Transparent Pricing
Invest in your own AI infrastructure and reduce long-term costs compared with ongoing cloud API spend. Our pricing is straightforward and designed for teams of varied sizes, giving you stability and cost efficiency over time.
Plus
$35,000 +
$1,250/mo maintenance*
Payback vs OpenAI corporate workspace in ~28 months**
What's included?
Everything in Basic
More capable hardware
IDE support & integration***
up to 100 users
Enterprise
Architected Together
With Your Teams
Bespoke AI infrastructure for mission-critical environments
What's included?
Everything in Plus
Custom solutions
Custom integrations
Multiple models running together
Adjusted for any scale
* Prices indicative — final depends on hardware & service details.
** Compared to maximum users in corporate OpenAI workspace.
***AI coding may require more sophisticated hardware depending on the needs. Reach out to us for details
Not only private, cheaper in the long run for large teams. Build and own your AI platform with predictable costs and no usage caps. Use it as much as you wish without per-token pricing surprises.
Ready to Secure Your AI?
Talk to our team to design your secure, private AI deployment. Get a tailored quote, hardware guidance, and deployment plan that fits your organization’s needs.
FAQ
What does RAG — Retrieval-Augmented Generation mean?
RAG combines an LLM with a retrieval system that fetches relevant internal documents at query time. This ensures the AI answers using grounded, up-to-date information from your own knowledge base rather than only its training data.
Why is RAG important for enterprise AI?
RAG enables the model to reference your company’s real data — policies, manuals, databases, etc. — making responses more accurate and relevant while reducing hallucinations common in plain LLM outputs.
What is a Large Language Model (LLM)?
A Large Language Model (LLM) is a type of advanced AI trained on massive text datasets to understand and generate human-like language. LLMs power modern AI workflows, enabling tasks like summarization, conversation, reasoning, and automated insights.
How is your self-hosted AI different from cloud AI services?
Our AI runs on your own hardware entirely behind your firewall — so no data leaves your environment, ensuring privacy, compliance, and total control. There’s also predictable fixed pricing rather than per-token costs charged by cloud APIs.
How long does it take to build, run, and integrate an AI system?
Depending on your specific needs, integration depth, and data complexity, deployments typically take between 3–5 months from planning to production. This includes hardware setup, model tuning, RAG pipeline configuration, and integration with internal tools.
Can the AI understand our company’s internal data?
Yes. Using RAG, your LLM is connected to your company’s internal data sources — such as documents and knowledge bases — so answers are tailored to your own information and workflows. However, everything is kept private inside the hardware.
Where are our chat logs and interactions stored?
All chats and interactions are stored inside your server setup — on your hardware or private cloud — never on external third-party servers. You control data retention, access policies, and backup procedures.
What does “customized to our data” really mean?
It means your model is enhanced with your own business knowledge via RAG indexing and retrieval systems, so the AI answers questions based on your company’s context, not just generic information.
Do you support IDE integration like VS Code?
Yes — we can connect your self-hosted models to developer environments (e.g., VS Code) and replace costly AI IDE tools like Cursor. This integration typically uses larger models and more powerful hardware and will be priced separately due to the added resources required.
Is this secure for sensitive industries like legal, finance, and healthcare?
Absolutely. Our self-hosted setup ensures data never leaves your controlled environment, meeting strict privacy, compliance, and security standards required in highly regulated industries.
What is a vector database, and do I need one?
A vector database stores numerical representations of text (embeddings) so your RAG system can quickly find relevant documents. Most enterprise RAG setups use one to ensure fast and accurate retrieval during queries.