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Plan AI deployment around your data and operating constraints.
Assess managed, private-cloud and self-hosted options for your AI workflow. Deployment choices should reflect data access, infrastructure capability, model licensing and ongoing operating costs.
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Overview
Private AI Deployment, explained
Some organisations cannot send data to a public AI service because of contracts, regulation or internal policy. Private AI deployment runs language models inside infrastructure you control, so prompts, documents and outputs stay within your environment.
We help select an open model that fits your quality needs, size the infrastructure, deploy it securely and build the applications on top: internal assistants, document search and automation. We also set up monitoring, access control and an update process so the system stays maintainable.
Before you build
Decisions worth settling first
Settling these points early keeps scope realistic and makes the estimate meaningful.
01
Data restrictions
Identify which data cannot leave your environment and why.
02
Hosting option
Choose between your cloud account, a private cloud or on-premises hardware.
03
Quality target
Test candidate models on your tasks to confirm they are good enough.
04
Capacity
Estimate users, requests and response time needs to size GPUs and scaling.
05
Access control
Integrate with your identity provider and define who can use which features.
06
Maintenance
Plan model updates, security patches and monitoring responsibilities.
Services
What our private AI deployment services include
Model selection and testing
Benchmarking open models on your own tasks for quality, speed and cost.
Secure deployment
Deployment in your VPC or data centre with network isolation and encryption.
Inference optimisation
Quantisation, batching and serving frameworks to reduce hardware cost.
Private RAG
Document search and question answering that never leaves your environment.
Internal assistants
Chat and drafting tools for staff connected to approved internal data.
Operations and monitoring
Logging, usage tracking, alerting and an update plan for models and infrastructure.
How it is built
The layers behind a dependable result
Every layer has an owner, tests and documentation, so the system can be supported after launch.
01
Infrastructure
GPU servers or managed instances, networking, storage and security controls.
02
Model serving
Inference servers with authentication, scaling and observability.
03
Application services
Retrieval, prompts, business logic and integrations.
04
Governance
Access policies, audit logs and data retention settings.
Use cases
Where it helps
Regulated industries
Use AI on sensitive records without third-party processing.
Legal and professional services
Search and summarise confidential client documents.
Manufacturing and engineering
Query technical documentation and IP-sensitive designs.
Public sector
Meet data residency rules for internal assistants.
Enterprises with strict vendor policies
Avoid adding new external data processors.
Cost control at scale
Reduce per-request cost for high-volume workloads.
Illustrative use case
A team with restricted documents can assess whether a controlled cloud environment meets its access and retention requirements.
Why PoliteTech
Why teams choose PoliteTech
Scope before commitment
Priorities, assumptions and acceptance criteria are agreed in writing before a delivery plan is fixed.
Engineers who build
Recommendations come from the people who design, code and support the software, so they reflect real implementation effort.
Visible progress
A shared backlog, regular demos and recorded decisions keep you informed without chasing updates.
Practical AI
Evaluation sets, review steps, cost limits and monitoring are part of every AI feature we ship.
Flexible engagement
Start with a scoped project, a dedicated team or support, and change the model as your roadmap evolves.
Clear ownership
Repositories and accounts can live in your organisation, with IP and confidentiality terms agreed in the contract.
Industries
Where this work fits
Retail and Ecommerce
Build web and mobile journeys around product discovery, ordering and customer service.
Financial Software
Support reporting, document processing and internal operations through defined application workflows.
Logistics Software
Connect planning, task assignment and operational updates in one workflow.
Education and Elearning
Create platforms for content, activities and progress tracking.
Fitness and Wellness Apps
Support tracking, coaching content and user engagement around a clear product goal.
Manufacturing Software
Replace fragmented tracking with workflows for quotations, work status and supporting documents.
Real Estate Applications
Create listing, enquiry and internal coordination tools around your business process.
Business Operations
Build task, approval and reporting tools around the way your team works.
Process
A practical path from requirements to release.
Each stage ends with something you can review: a scope, a prototype, a working increment or a release.
01
Understand
Discuss your users, business needs, existing systems and delivery constraints.
02
Define
Agree scope, technical approach, responsibilities and a delivery plan.
03
Design and build
Develop in manageable increments with visible progress and review points.
04
Test and release
Validate agreed workflows, resolve release blockers and prepare deployment.
05
Support and improve
Document the handover, review production feedback and plan the next priorities.
Technology
Tools and platforms we work with
LlamaMistralQwenHugging FacevLLMOllamaText Generation InferenceKubernetesDockerNVIDIA GPUsAWSAzureGoogle CloudKeycloakPostgreSQLpgvector
Cost
What shapes the cost of your project?
There is no useful price without scope. These are the factors we review when preparing an estimate, and the estimate lists every assumption so you can see what would change it.
01
Number of workflows
Each workflow needs its own prompts, tools, evaluation set and review design.
02
Data preparation
Cleaning, labelling, chunking and access rules often take more effort than the model itself.
03
Integrations
Connections to CRMs, ERPs, document stores and identity providers add design and testing work.
04
Evaluation and review
Building test sets, review queues and monitoring is essential for reliable results.
05
Model and hosting
Hosted APIs, open models and private deployment have very different setup and running costs.
06
Security and compliance
Sensitive data, audit needs and data residency add controls and documentation.
Engagement models
Choose the engagement that fits your next step.
Dedicated Team
For a changing roadmap or ongoing development. Agree roles and working process, then manage priorities through a shared backlog.
Best for team extension and long-term product work.
Scoped Project
For a defined product or milestone. Align on requirements, deliverables and acceptance criteria, with changes reviewed before they affect the plan.
Best for MVPs, integrations and focused releases.
Maintenance and Improvement
For software that already exists. Start with a technical review, then agree support routines and planned improvements.
Best for live applications and modernisation.
FAQ
Questions about private AI deployment
Are open models good enough?
For many business tasks, yes, especially with retrieval and good prompts. We test on your examples before recommending a model.
What hardware is needed?
It depends on model size and traffic. Smaller quantised models can run on modest GPUs; larger models need more capacity. We size this during planning.
Can it run on-premises?
Yes, if you have suitable hardware or plan to buy it. Cloud deployment in your own account is another common option.
Is private AI more expensive?
Upfront setup costs more than using an API, but at high volume self-hosting can lower the cost per request. We model both.
How do you keep the model up to date?
We define an update process to evaluate new model versions and roll them out safely.
Which models do you deploy?
Common choices include Llama, Mistral and Qwen families, selected by licence, quality and size.
Can we begin with a focused scope?
Yes. We can review your current situation and agree a bounded first engagement before a wider build.
How are pricing and timelines determined?
We review requirements, technical complexity, team needs and delivery constraints before proposing an engagement. Estimates list assumptions and what may change the plan.
Can we discuss a project under an NDA?
Yes. Let us know before sharing confidential materials so we can agree confidentiality terms first.
Discuss your private AI deployment needs
Share your goals, current situation and the help you need. We will review your message and suggest a practical next step.