An internal knowledge application
An AI-assisted interface to approved company information, with source retrieval and user access controls.
Sustainablemindz services
Software Development
AI applications and integrations designed for your compute environment.
Illustrative infrastructure imagery
The scope
What's included
We build AI applications, internal platforms and integrations on top of your compute.
- Applications
- LLM workflows and internal knowledge tools
- Model serving
- Inference APIs and deployment pipelines
- Integration
- Data sources, identity and existing systems
- Validation
- Functional tests and model evaluation criteria
- Handover
- Source, documentation and agreed maintenance scope
AI and LLM application development
Build applications around defined user needs, with evaluation and safeguards appropriate to the use case.
Model deployment and inference APIs
Make models available to your applications through agreed serving interfaces and deployment workflows.
Internal developer platforms
Create tools for teams to access compute, deploy services and manage development workflows.
Data pipelines
Connect ingestion, preparation and processing steps with validation and operational visibility.
Integrations with your existing systems
Connect applications to available interfaces, identity services and business systems.
Ongoing support
Agree a maintenance scope covering updates, issue handling and improvements after delivery.
From requirement to delivery
How it works
- 01
Define the outcome
Agree users, workflows, data access, system interfaces and acceptance criteria.
- 02
Design and build
Develop working increments around the agreed application and integration architecture.
- 03
Test and deploy
Validate functionality, model behaviour and access controls in the target environment.
- 04
Hand over and support
Deliver documentation and agree ongoing maintenance responsibilities and scope.
Example scopes, not customer case studies
Typical projects
A private model-serving API
A model deployment connected to internal applications through a defined inference interface.
A compute operations platform
An internal interface for deployment workflows, job visibility and integration with existing tools.
Documented delivery
What you receive
The final deliverables are defined in your agreed scope.
- Agreed scope, milestones and acceptance criteria before work starts
- Source code in your repository, with documentation
- Deployment scripts for your GPU environment
- Test results and performance benchmarks on your hardware
- Handover session and agreed support period
After handover
Support and assurance
Your code, your IP
Code and documentation are delivered to you; nothing is locked to our systems.
Support period
Bug fixes and adjustments during an agreed period after launch, with optional ongoing maintenance.
Security and privacy
Data handling, access control and logging are designed in from the start, not added later.
Before you start
Frequently asked questions
Buyer guide · 5 min read
Buying vs renting GPUs: when each makes sense
Build your next AI environment.
Share your workload, location and timeline. Start with a configuration quote or an engineering conversation.