Run AI models inside your own network so sensitive data never leaves your control.
Stack we work in
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Many organizations want the benefits of large language models but cannot send customer, patient or financial data to a public AI service.
We deploy models where your data already lives: on your own servers, in your private cloud network, or through private endpoints on Azure OpenAI, Amazon Bedrock and Google Vertex AI. For open models we set up serving with vLLM, SGLang, Ollama or Hugging Face TGI, with single sign-on, access logs, usage limits and monitoring, so your teams get a working AI service and your security team gets the controls it needs.
Use cases stall because data cannot be sent to a public model API.
People paste company data into consumer chat tools because there is no approved option.
Customers and regulators ask where data is processed, and nobody has a clear answer.
Separate API keys across teams make spend and usage impossible to see.
Applications are built against one vendor's API, so switching models means rework.
Outcomes
Deployment design, model choices and a GPU and API cost comparison for your expected load.
Production inference on vLLM, SGLang or Ollama, or private endpoints, defined in Terraform.
One API for all models with SSO, quotas, logging, masking and cost reporting.
A web chat for staff, connected to approved models and internal documents.
Data flow diagrams, controls list and evidence for security and data protection reviews.
Dashboards, alerts and procedures for upgrades, scaling and incidents.
Summarize notes and answer staff questions without patient data leaving your environment.
Learn moreAnalyze documents and support staff with models that meet audit and data location rules.
Learn moreSearch and draft from confidential client files inside your own network.
Query manuals, specs and maintenance records on-site, even with limited connectivity.
Learn moreAir-gapped or sovereign deployments where data must stay in a set location.
Add AI features to your product while keeping customer data in your own cloud.
Pick the level of control your data and budget need.
Built with
Fast, reliable inference on the right hardware.
Built with
Control who uses which model and what they send.
Built with
Keep data where regulators and customers expect it.
Give people something useful to use on day one.
Built with
GPU sizing and a cost comparison across on-premise, cloud GPUs and private endpoints come first, with a written recommendation you can take to finance.
Single sign-on, audit trails, encryption with your keys and documentation for security reviews and data protection impact assessments are part of the delivery.
vLLM, SGLang, TGI or Ollama behind one LiteLLM gateway means you can swap models without rewriting applications.
20+ of our engineers hold cloud and data platform certifications, and every environment is built with Terraform so it can be rebuilt or audited.
You get runbooks and handover documentation, and can keep us on for monitoring and upgrades or run the platform yourself.
Featured case study
Every business already owns the sensor network it needs. CCTV records everything and tells you nothing — until someone reviews the footage, usually days after the loss.
Read the case study
We map data sensitivity, users, use cases, latency needs and the regulations that apply.
You receive Requirements and risk summary
We choose on-premise, private cloud or private endpoints, select models and size GPUs against expected load.
You receive Architecture and cost comparison
A working private model with SSO and a chat interface for a small group of users.
You receive Running pilot
We add the gateway, logging, masking, quotas, monitoring and backups, and run a security review.
You receive Production-ready platform
Teams and applications are onboarded in stages with usage guides and support.
You receive Rollout and runbooks
We patch, upgrade models, tune performance and report on usage and cost.
You receive Managed service or handover
Tech stack
AI and machine learning
Languages
Cloud and DevOps
Other tools
Engagement
A working private model with SSO and chat for one team, in a few weeks.
Full private AI platform with gateway, security controls, RAG and monitoring.
We operate, patch and upgrade the platform and report on usage and cost.
It depends on the deployment model, the number of users and requests, the size of the models, high-availability needs and the security controls required. GPU hardware or cloud GPU rental is separate and billed to you; we compare it with hosted API costs during sizing. As a guide, a private LLM pilot usually costs $15,000 to $35,000, and a production platform with gateway, SSO, monitoring and RAG $40,000 to $150,000 or more (USD).
For many business tasks such as summarizing, drafting, extraction and answering from your documents, current open models like Llama, Qwen, Mistral and gpt-oss perform well, especially with RAG or fine-tuning. For the hardest reasoning tasks the top hosted models still lead, so we often route those through private endpoints while keeping sensitive data on self-hosted models.
No. You can rent GPUs in your own cloud account, use private endpoints to hosted models, or buy servers if usage is high and steady. We model the cost of each option for your expected load before you commit.
Self-hosted means the model runs on servers you control. Private endpoints use a cloud provider's hosted model (Azure OpenAI, Bedrock, Vertex AI) over a private network connection, with contractual commitments that your data is not used for training. Both keep traffic off the public internet.
Usually vLLM or SGLang for production GPU serving, Ollama or llama.cpp for small installs and laptops, and NVIDIA NIM or Triton where a client has an NVIDIA stack. We choose based on hardware, model and throughput needs.
Yes. Models, vector databases, logs and backups are deployed in the region you choose, and we document data flows for your data protection team.
Users sign in with your identity provider. A gateway enforces which teams and applications can use which models, applies quotas and filters, and logs every request for audit.
Yes. Open models, serving software and supporting tools can be installed without internet access, with an agreed process for bringing in updates.
We can run it as a managed service, with patching, model upgrades and monitoring, or hand it to your team with runbooks and training.
A single-model deployment in your cloud account with a gateway and SSO typically takes 3 to 6 weeks. On-premise or air-gapped installs depend on hardware lead times and your security review, usually 6 to 12 weeks.
Book a call, and we talk through the goal, the systems involved and any constraints under NDA. You then get a short written proposal with scope, timeline and estimate. We need your expected users and request volume, data residency rules, identity provider details and, for on-premise, the GPU hardware you have or plan to buy.
You do. The infrastructure runs in your accounts or data center, the Terraform code and runbooks are handed over, and open models are used under their own licences. No usage fees are paid to us.
Book a call. We will review your data, users and security needs and outline the right private deployment.
