Private AI
AI that runs inside your own environment, on your data, under your access rules.
Why It Matters
Some data cannot leave the environment where it is held. Customer records, patient files and unpublished research are often in that group, and a public AI service is not an option.
A private deployment has its own risks. A model on the wrong hardware is slow and costly. An index that ignores permissions can expose a document, and an upgrade can quietly change answers. The work fails when the boundary is unclear, when permissions are added after the model, or when nobody owns quality after launch.
What We Do
Models on Your Premises
Models that run in your buildings, a private cloud or a network with no outside connection.
Frontier and Open-Weight Models
We compare open-weight and hosted models on your own questions and recommend the smallest one that meets the quality you need.
Tuning for Your Language and Domain
We tune a model on your vocabulary, documents and formats, and test it on real questions before release.
Permission-Aware Knowledge Platforms
Search and answers that apply your access rules before a passage reaches the model, so each reader sees only what they may see.
What Changes for You
Data Inside Your Boundary
Models and search run in your data centre or private cloud, with no document leaving the environment you set.
Answers Within the Reader's Rights
Search applies your access rules before the model sees a passage, so a person reads only what they may read.
A System You Control
You choose the models, the hardware and the update window, and you can replace a model without rebuilding the product.
How the Work Runs
A private deployment is sized and tested before it is trusted. Your security team approves the boundary, and your team owns the system at handover.
Set the Boundary
We agree the users, the tasks and the data the model may read. Your security team names the network, the hardware and the rules the system must meet.
Prepare the Sources
We bring documents into a searchable store and map your access rules to each item. You confirm the owners of each source and the groups that may read it.
Choose and Tune the Model
We test candidate models on your own questions for quality, speed and cost. You see the comparison and decide which model runs where.
Deploy Inside
We install the model, the index and the monitoring in your environment. Your team runs the acceptance test, and we hand over the runbook with it.
Measure and Update
We track answer quality, refusals and cost, and review flagged answers with your team. You decide when a model or index update is applied.
Hand Over
We train your staff to read the quality report, add documents and apply an update. Your team runs the system with the written guide we leave.
Platforms and Technologies
The work runs on the hardware and platforms your security team already approves. NVIDIA GPUs are the processors that run large models. Google Cloud provides a private cloud tenancy, and Red Hat Enterprise Linux is the operating system many private deployments run on. Meta Llama, Mistral, Qwen and DeepSeek are open-weight models you can run yourself. Hugging Face is a library and model catalogue. vLLM runs a model in production and keeps responses fast when many people use it. Databricks, where you already use it, prepares the data before it reaches the model.
- AI and machine learning
- Meta Llama
- Mistral
- Qwen
- DeepSeek
- Hugging Face
- vLLM
- Data and analytics
- Databricks
- Methods We Use
- Private GPU hosting
- Search that follows access rules
- Networks with no outside connection
- Small language models
Naming a product does not mean a partnership with its vendor.
NVIDIA
We size and build GPU estates on NVIDIA for training and inference, then prove the stack under a real workload.
Google Cloud
We build data, analytics and AI systems on Google Cloud, with BigQuery, Vertex AI and GKE under one control model.
Red Hat
We build OpenShift platforms on Red Hat, move applications into containers and keep operations planned.
Industries
Banking and Financial Services
Banking, payments, lending, wealth, insurance and digital wallets: the platforms they run on and the connections between them.
Education and Workforce Upskilling
EdTech platforms, serious-training simulations and national workforce upskilling academies, built accessible and in the learner's language.
Public Sector
Citizen portals, case management, payments and integrations with government registries, designed to recognised accessibility guidelines.
Questions
What buyers ask before they put a model inside their own environment.
Can the model run without any document leaving our environment?
Yes. The model, the index and the logs stay in the environment you name, which can be your data centre or a private cloud tenancy. We test that boundary before users arrive.
How do you stop an answer showing a document to the wrong person?
Your access rules are applied during search, before any passage reaches the model. The answer is built only from what the reader may see, using the identity and groups you already manage.
Which models can we run?
Open-weight models such as Llama, Mistral, Qwen and DeepSeek, and hosted models where your policy allows them. We compare them on your questions and recommend the smallest model that meets the quality you need.
What hardware do we need, and what does it cost?
That depends on the model, the number of users and the response time you need. We size the GPUs and memory from a measured test, show the cost of each option, and you approve the capacity before we allocate it.
Who owns the system and decides when it changes?
You own the application, the tuned model and the documentation we build for you. We test an update on your question set, show the difference in quality and cost, and apply it in a window you choose.
Tell Us What You Need to Run, Build or Secure.
Describe the service, the systems and the pressure you are under. The right team takes it from there.