Microsoft Azure
Infrastructure, networking, identity and integration. The foundation for applications, model access and connections to company systems.
Data and context · AI environment
AI on the company’s terms. From models to infrastructure.
Azure is the starting point. The most sensitive data can be processed by local models on company servers. We select other clouds and fully on-premise environments together with IT.
Technology
Proven technology. Used in our own work.
We build on technology we know through everyday use. Azure and Databricks support the data layer we build for clients and the environment we develop at WeAreFuture.
Lakehouse, data preparation and shared definitions. Reliable context for reports, assistants and agents, with access control and data lineage.
As Microsoft and Databricks partners, we combine these technologies with the AI Engine methodology. The deployment scope reflects the company’s existing environment.
Explore the data foundationDeployment
An environment matched to your data and work.
Azure is the starting point. The most sensitive data can be processed by local models on company servers. We select other clouds and fully on-premise environments together with IT.
Azure as the starting point
We start with Azure. Together with IT, we agree the data scope, access and connections to company systems.
Local models for sensitive data
The most sensitive information can stay on company servers and be processed by local models. We agree the data boundaries and controls with IT.
Other clouds and on-premise
We select other clouds and fully on-premise environments together with IT, according to the data requirements and existing infrastructure.
Architecture
Choose where it runs. Then choose the models.
One environment can use different models. We select them for the quality the task needs, data sensitivity, response time and operating cost.
AI EngineCompany data and context
People, assistants and agents
The task and relevant context reach AI Engine. Every request has an identified user or agent and a defined access scope.
Rules and controls
Agreed rules determine which data can reach each model. A shared gateway provides visibility into usage and cost.
A model for the task
We use approved local, private or commercial frontier models. The choice is evaluated on real examples of the work.
Network isolation and access rulesHistory, monitoring and resource limits
Client story
A financial company. Local AI for sensitive documents.
The company needs to process financial statements and contractor data within its own infrastructure. We are preparing the environment and the selected workflows with its IT team.
The scope includes GPU server selection, local models, a shared model gateway and connections to applications. Test and production environments are separated.
The requirement
Sensitive information must remain within the agreed company environment. The IT team needs control over access and a clear operating model.
The setup
Local models and a model gateway support the selected workflows. We define the network, permissions, updates and service access supervised by the company’s IT team.
The intended result
Less manual document work and the ability to use AI under the company’s data constraints. The effect will be assessed after the workflows are launched.
Time saved
The planned workflows aim to reduce manual document reading and data entry; the effect will be measured after launch.
New possibilities
Local processing enables AI use on information that cannot leave the agreed company environment.
First step
Start with one task and its data boundaries.
The first trial should answer whether the environment fits the actual way of working. It provides evidence for a decision about a wider setup.
Agree the scope
A sample task, data classification, current infrastructure and the people responsible for business and IT.
Test the options
We compare quality, response time and operating conditions on agreed examples and candidate configurations.
Decide on deployment
The result, responsibilities, resource budget and acceptance criteria define the next stage.
Let’s talk about the work you want to improve.
Tell us how a process works today. Together, we will explore where AI Engine can help and which module to start with.
Book a callOr write to: hello@wearefuture.ai
