Run suitable AI models on your infrastructure, in your cloud account or on Intellicy-managed infrastructure. Intellicy connects them to the knowledge your team needs, with access and data boundaries defined for your deployment. Private configurations can process documents and prompts without sending them to an external model API.
Choose the environment that fits your business: your own servers, your cloud account or Intellicy-managed infrastructure. We can deliver the application in any of these environments.
We work through model suitability, capacity, access, cost and ongoing operation before recommending a configuration. The right choice depends on the work the system must do and the boundaries your organisation needs.
Ongoing operations and maintenance are available under an optional contract. Hosting, infrastructure costs and the responsibilities of each party are agreed for the engagement.
Policies, procedures, documents and operational information provide context that a model alone does not have. We help make approved information available to the system, with access boundaries appropriate to the people using it.
Your team gets the context it needs, and where the use case calls for it we show the sources and their limitations.
Private AI is a design choice across the system. We examine where documents, prompts, responses and logs are processed and stored, which services are involved and who can access the information.
For a deployment intended to keep business data within your environment, supporting processing and monitoring must respect that same boundary. Any external processing should be identified and agreed before it is used.
Running a model privately does not make every output correct. The system still needs testing, defined permissions and appropriate human review.
We help establish what the AI may do, which decisions remain with people and how the team should handle uncertain or unsuitable output.
We support customer-owned infrastructure, deployment in your cloud account and Intellicy-managed infrastructure. We agree the environment and its data-handling arrangements around your requirements.
Yes. We offer optional operations and maintenance contracts. The agreement defines the work included, each party's responsibilities and the service terms. You can also engage us to deliver the system into your own environment with an agreed handover.
Usually, the starting point is a suitable existing model that can run in your chosen environment. Connecting it to approved business knowledge and, where justified, adapting it for a specific task are different from training a new foundation model.
A fully private configuration can process your documents and prompts without calling an external model API. The complete architecture, including document processing, storage and logging, must be configured to meet that requirement. We define the data boundary as part of the solution design.
Yes. Model and deployment choices should follow the task and your requirements. An external model service is a separate option with its own data-handling terms; it should not be described as equivalent to a fully private deployment.
Not necessarily. The base model is subject to its licence. The deployment, client-specific work, operating responsibilities and any rights to custom adaptations need to be made clear in the engagement terms.
Integration depends on the systems, interfaces and access available. We assess those dependencies when defining the use case and deployment approach.
Tell us about the work you want to improve, the information involved and the requirements your organisation needs to meet. We can discuss whether a private deployment is appropriate.
Business design. AI systems. Customer control.
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