Private AI & enterprise LLM solutions
Assess and build private AI deployments with identity controls, approved hosting, model evaluation and a clear view of infrastructure and operating costs.
THE BUSINESS CONTEXT
Some organisations need more control over where an AI application runs and how information is processed. The right design may be a managed enterprise service, a dedicated cloud environment or a self-hosted model, depending on the actual requirements.
Private deployment is a technical and operational choice, not a blanket guarantee of privacy or compliance. We compare feasible options, evaluate task quality and confirm the people and resources needed to operate the selected environment.
YOUR FIRST ENGAGEMENT
Architecture Assessment First
Compare deployment options and run one approved knowledge task in a controlled pilot environment.
Discuss This PilotData-handling requirements, sample tasks, IT ownership, hosting constraints and a realistic operating budget.
Measures are agreed for your project. Results depend on the data, workflow and evaluation; they are not guaranteed improvements.
WHAT WE DO
Identify data categories, permitted regions, connectivity, identity systems and retention expectations. Review provider terms and infrastructure dependencies during discovery. Clarify whether the requirement concerns application hosting, model processing or both, since a private interface may still call an external model service.
Evaluate candidate models on representative tasks, latency and total operating cost. Assess licensing, hardware needs and maintenance responsibilities. Self-hosting can provide control but also introduces capacity, patching and availability work. The choice follows evidence rather than an assumption that one deployment model is always best.
Integrate approved sign-in, role controls and source-aware retrieval. Keep credentials outside browser code and isolate customer or department information where needed. Logging is designed to support operations without unnecessarily retaining sensitive prompts and responses. Test access boundaries through the complete retrieval path.
Prepare deployment documentation, update procedures and recovery options. Load-test the agreed task volume and identify scaling limits. Fine-tuning is considered only when there is a suitable licensed dataset and a demonstrated need beyond retrieval or configuration. Final residency and compliance decisions remain with the customer’s responsible advisers.
A DEFINED ENGAGEMENT
Your proposal defines the exact scope, responsibilities, milestones, and exclusions. Depending on the engagement, the work can include:
WHERE IT FITS
Assess controlled access to sensitive internal material.
Plan tenant-aware AI features and model access.
Evaluate deployment options against internal requirements.
WHO THIS CAN HELP
Explore example workflows and the customer groups these services are designed to support.
A PRACTICAL FIRST STEP
Choose one useful task, agree how the result will be checked, and use the evidence to decide what should happen next.
QUESTIONS, ANSWERED
Only a specifically designed and verified architecture can meet that requirement. We document every external dependency and model-processing path before making that claim.
Potentially. Licensing, task quality, hardware and operating capacity are assessed during discovery.
No. Retrieval, better source data or workflow changes may solve the problem more simply. Fine-tuning requires a clear purpose and a suitable evaluation dataset.
CONNECTED CAPABILITIES
Prepare documents and business data for AI with reliable ingestion, data-quality checks, access controls and repeatable update pipelines..
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Evaluate AI answer quality, permissions, costs and failure cases, and define practical ownership, review and change-control processes..
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Maintain AI knowledge, evaluate model changes, monitor usage costs and improve workflows through a clearly scoped ongoing support engagement..
Explore This SpecialtyA CONVERSATION IS A GOOD PLACE TO START