Private AI Server for Your Business, Without Giving Away Your Data
We design and support a private AI server for your business, deployed on-premises or in an environment you control. For local inference, prompts and documents can stay within that deployment boundary; any external services or data flows are agreed with you in advance.
Talk to Our Team See How It WorksFree AI tools come with a hidden cost
When your staff types sensitive company information into a free AI chatbot, that data can leave your control and be processed outside your own infrastructure.
Private data can leak out
Customer records, contracts, source code or financial numbers can be stored on someone else’s server, outside your control.
No control, no visibility
You cannot see what free tools do with what your staff types in, or how long it is kept.
Compliance risk
Sensitive or regulated data leaving your business can break client contracts and data protection rules.
Did you know?
When your staff uses free AI tools, they may be exposing your company’s private data to outside servers you do not control — without even realising it.
Private AI, without the operational guesswork
We help organisations deploy secure AI systems that keep sensitive business data, internal documents and proprietary knowledge within an environment they control.
Built for privacy-sensitive teams
Ideal for healthcare, finance, regulated operations, legal, engineering and knowledge-heavy teams that cannot expose confidential information to public AI platforms.
Governed by your rules
From role-based access to runtime controls, we help align the stack around your data handling, security and compliance requirements.
RAG, fine-tuning and model choice
We support document grounding, retrieval workflows, and LoRA / QLoRA fine-tuning where a domain-specific model or workflow requires it.
Typical deployment stack
Ollama, vLLM, Open WebUI, Qdrant and pgvector for larger knowledge deployments. We can also support Slack, Microsoft Teams, CRM integrations and REST API connectivity for business workflows.
How private AI answers questions about your documents
Retrieval-augmented generation (RAG) lets an AI assistant look up relevant information in approved business documents before drafting an answer.
Connect approved sources
Choose the documents or knowledge sources for the project, and agree access permissions and update responsibilities.
Find relevant passages
The retrieval layer searches the indexed content for passages related to the employee’s question.
Generate a grounded response
The model uses the retrieved passages as context. Source links or citations can be included when supported by the selected design.
RAG or fine-tuning? RAG is usually the starting point when an assistant needs to find current information in documents. Fine-tuning may help adapt response style or task behaviour. The right approach depends on the use case, data, evaluation results and operating requirements; the two can also be combined.
Clear starting points for private AI
Choose the setup that best matches your team’s size, workflow and security requirements.
Best for small teams
- From $142/month
- RTX 4090 (24GB) for fast local inference
- 1–2 open-weight models
- 8/5 support included
Best for departments
- From $569/month
- RTX 6000 Ada (48GB)
- RAG + multiple model options
- 24/7 response with 4-hour target
Best for enterprise
- Custom from $5,130/month
- H100 (80GB), multi-GPU architecture
- LoRA / QLoRA and large-scale deployment
- 24/7 support with dedicated engineer
Support is bundled into each tier, rather than presented as a separate line item.
Structured for the way businesses actually deploy AI
Every tier is scoped with our team, the type of workload, and the level of support you need. Final sizing remains dependent on model choice, usage, concurrency and integration scope.
Team
Department
Enterprise
User counts describe the intended team size, not a guaranteed number of simultaneous requests. Actual throughput depends on model choice, context length, request mix, latency targets and integrations. We validate capacity against the agreed workload during discovery or a pilot.
Per-seat API bills vs. one infrastructure you own
Compare what you’re really paying for over time. Private AI cost efficiency depends on your workloads, hardware and usage, so we size each setup around your team’s needs.
Pay-per-use API
Private Server (Mobit)
A fair cost comparison should include your current API or per-seat spend alongside hardware or hosting, power, storage, implementation, ongoing support and expected usage. Savings and break-even timing vary by workload, so we assess them from your actual usage rather than promise a universal percentage.
Your own private AI server, on your own premises
We design, set up and support a private LLM server in an environment selected around your needs.
On-premises deployment keeps infrastructure in your facility and gives your team direct operational control. A private cloud can offer managed infrastructure within a tenancy you control, while a hybrid setup can route only approved workloads to external services. Each option has different cost, control and maintenance trade-offs; we review data sensitivity and dependencies with you before recommending an approach.
| Approach | Where inference runs | Operational considerations | May suit |
|---|---|---|---|
| On-premises | Infrastructure in your facility | You control the environment; hardware, power, physical security and maintenance need planning. | Workloads with strict local-control or connectivity requirements. |
| Private cloud | In a cloud tenancy selected for the project | Review region, provider terms, access, network boundaries and ongoing cloud costs. | Teams seeking managed infrastructure without hosting equipment on-site. |
| Hybrid | Across private and approved external services | Define which data and tasks may use each environment, with routing and access controls agreed in advance. | Teams balancing sensitive workloads with selected external capabilities. |
| Hosted AI API | Provider’s service environment | Check provider retention, processing terms, region, security controls, usage pricing and model availability. | Lower-volume or exploratory workloads where the provider terms meet requirements. |
Data Control Within Your Boundary
With local inference, prompts and documents can be processed within the environment you control. External services, if needed, are identified and agreed as part of the design.
Full Control, On-Premises
The server sits inside your own building or your own private cloud. You decide who has access, and how it is used.
Built to Scale
Start small and grow. We size the setup to your team today, and expand it as your AI use grows.
Support That Fits Your Plan
Monitoring, updates and engineering assistance are agreed as part of the service plan, so responsibilities and support hours are clear from the outset.
Hardware ownership and hosting are agreed for each project: options may include client purchase, lease or managed hosting. The proposal should confirm who supplies the hardware and who is responsible for maintenance and operations.
AI that helps people work smarter, faster and more securely
Empowering Your Team With Private AI
AI That Makes Your Employees More Productive
Artificial Intelligence is not here to replace your employees — it is here to make them more productive. A private AI server acts as a secure digital assistant, helping your team find information faster, automate repetitive tasks, analyse data, draft content, and support decision-making while keeping sensitive business information protected.
Private AI Infrastructure for Secure Business Workflows
With the right private AI infrastructure and AI governance policies, your organisation can use AI more efficiently while maintaining control over its data, models, and internal workflows.
Reduce Repetitive Work and Improve Productivity
By reducing the time spent on routine and repetitive work, your staff can focus on higher-value activities such as customer service, strategic planning, innovation, and business growth. The result is greater efficiency, improved productivity, and better use of your team’s expertise.
Enhance Security and Operational Efficiency
With a private AI infrastructure, your organisation gains the benefits of advanced AI technology without exposing confidential data to external providers. Every query, document, and workflow remains under your control, helping strengthen cybersecurity, support compliance requirements, and reduce the risk of data leakage.
Unlock the Full Potential of Your Workforce With Private AI
Our mission is simple: help organisations leverage the power of AI safely and responsibly, enabling teams to work smarter, operate more securely, and focus on what truly matters — growing the business and delivering value to customers.
Private AI. Enhanced Security. Smarter Teams. Better Business Outcomes.
Architecture reviewed by a CISSP-certified security advisor
We design deployments around the principles that regulated teams are already held to.
Private AI for regulatory compliance starts with understanding the rules that apply to your organisation. For teams researching CCPA compliance AI or PCI DSS AI, we can discuss the relevant privacy and payment-data requirements, then configure controls to fit your environment. A deployment can support compliance efforts, but does not by itself certify an organisation or make it compliant.
Frameworks listed reflect the architecture principles our deployments are designed around. Formal certification status is confirmed per engagement — ask us for the current roadmap.
Is private AI the right fit for your team?
The best choice depends on data sensitivity, expected usage, model needs, budget and the level of operational control you want.
Consider private AI when
You need greater control over where sensitive prompts and documents are processed, have recurring workloads, or need an environment tailored to your governance requirements.
A hosted API may fit when
You are testing an idea, have lower or variable usage, want minimal infrastructure management, and the provider’s terms and controls meet your requirements.
Consider a hybrid when
Some tasks need tighter data boundaries while other workloads benefit from approved external models. Set data-handling rules before routing work between environments.
A discovery session or scoped pilot can compare response quality, performance, integration effort, security controls and total operating costs before a wider rollout.
Different sectors, the same non-negotiable: data stays put
Our initial focus areas are the UK, US and UAE, with flexible delivery for broader regional rollouts.
Contract & case review
Summarise privileged documents without them touching a third party.
Clinical documentation
Private AI for healthcare can help draft and structure patient notes inside your network boundary, subject to your clinical review and privacy controls.
Reporting & analysis
Private AI for finance / banking can support confidential reporting. Private AI fraud detection workflows can help analysts review transaction patterns while keeping sensitive records within approved infrastructure.
Public sector operations
Meet data-residency requirements with fully on-premise deployment.
Knowledge & process docs
Private AI for manufacturing can turn internal manuals and SOPs into a searchable, private assistant. Private AI predictive maintenance workflows can also analyse equipment records and maintenance history to help manufacturing teams identify potential maintenance needs.
From first call to a live private AI server
A guided process that starts with use cases, data sensitivity, AI governance policies and existing systems. We agree a suitable deployment approach, pilot goals and target-market requirements before moving into setup.
For many teams, a 2–4 week paid pilot is the most practical way to validate performance, integration needs and security requirements before a wider rollout. We can also advise on hardware ownership options — client purchase, lease or managed hosting — depending on your operating model.
Assess data sensitivity for AI
During the consultation, we review use cases, data sensitivity, user needs and the systems the solution may need to connect to.
Server Setup
We recommend a deployment and size the model and hardware around expected users, concurrency, storage and performance needs.
Staff Onboarding
We configure agreed integrations and access controls, then guide your team through the pilot and onboarding.
Ongoing Support
We agree monitoring, updates, backups and support responsibilities with you as part of the service plan.
The people behind your private AI setup
Umar Shafique
17+ years of experience in software engineering, IT consulting and AI infrastructure projects for clients worldwide.
View LinkedIn Profile →Asif Amjad
Cybersecurity professional (CISSP certified) helping shape how our private AI setups keep client data locked down.
View LinkedIn Profile →Before you talk to us, you probably want to know
Do we need our own IT team to run this?
No. We handle setup, monitoring, updates, and ongoing private AI deployment support. Your staff can use it like any other internal tool without requiring in-house machine learning expertise.
How is this different from ChatGPT Enterprise or Copilot?
Cloud-based services process data according to the provider’s terms. With a private AI server configured for local inference, prompts, documents and logs can remain within your chosen environment, giving your organisation greater data sovereignty and support for private AI for data privacy.
Can it run fully offline?
Yes. Our deployments can be completely air-gapped for facilities that require zero external network dependency, helping protect sensitive data and providing private AI intellectual property protection.
How to implement private AI: how long does deployment take?
As an initial estimate, Team and Department deployments may take around 2–4 weeks after discovery. The schedule depends on hardware availability, integrations, security reviews and agreed scope, and is confirmed during planning. Our experts follow private AI best practices and proven private AI deployment processes to configure your environment securely.
Do you offer a paid pilot or proof of concept?
Yes. For qualified deployments, we can scope a short paid pilot or proof-of-concept to validate model performance, team workflows, integrations and security controls before full rollout.
What happens if we need to change models later?
Models can be swapped, upgraded, or tailored without re-architecting the server. Our custom AI solutions give you flexibility as your requirements evolve, so you’re not locked into a single vendor’s roadmap.
What does ongoing support actually include?
Support can include monitoring, security patching, model updates and engineering assistance. The exact coverage, hours, backup responsibilities and response expectations are agreed in the service plan. Our architectures can support compliance efforts such as GDPR compliant AI and HIPAA compliant AI, depending on your deployment and governance requirements; they do not certify compliance on their own.
How do you choose the right model and server size?
We scope the model and hardware against your use cases, expected concurrent users, response-time needs, document volume, security requirements and budget. GPU memory (VRAM), system RAM and storage are considered during sizing; final specifications are confirmed after discovery and testing.
Can you connect the AI to our documents or business systems?
Where suitable, we can scope document search or other integrations as part of the project. The data sources, access permissions, retention, backups and any external connections are reviewed and agreed before implementation. Common options include Slack, Microsoft Teams, CRM tools and REST APIs.
Do you offer staff training and onboarding?
Yes. We can provide onboarding and training for teams that need guidance on model use, access controls, prompt governance and day-to-day operations as part of the rollout.
What is RAG and how does it work?
Retrieval-augmented generation (RAG) searches approved, indexed documents for information relevant to a question, then provides those passages as context to the AI model. Responses can include source links or citations when supported by the selected design. Access permissions and source updates are agreed as part of implementation.
How do you choose between RAG and fine-tuning?
RAG is generally used when an assistant needs to find current information in documents. Fine-tuning can adapt a model’s response style or task behaviour. The choice depends on the use case, data, evaluation results and operating requirements, and both approaches may be combined.
Who supplies and owns the hardware?
Hardware arrangements are agreed for each project and may include client purchase, lease or managed hosting. The proposal confirms who supplies the equipment and who is responsible for maintenance and operations.
Ready to keep your data in your own hands?
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