Practical AI, engineered for production.
Aminexus helps organisations define valuable AI opportunities and engineer them into production-ready systems. The work connects business goals, data, model behaviour, evaluation, governance and operating cost from the start.
Technology Partners
Capabilities
Connect the use case, system design and operating model.
Each engagement is scoped around a measurable problem rather than a model or tool chosen in advance.
AI opportunity and readiness
Prioritise use cases against business value, data readiness, delivery risk, operating cost and measurable success criteria.
Knowledge systems and RAG
Design retrieval, grounding and knowledge workflows around your information sources, access boundaries and quality requirements.
AI workflows and intelligent agents
Build task-oriented workflows with clear tools, permissions, human checkpoints and responsibility for consequential actions.
Evaluation, deployment and ongoing improvement
Define test sets, quality measures, observability and release practices so model, prompt and workflow changes can be assessed over time.
Production readiness
A useful demonstration is not yet a dependable system.
Production AI needs agreed quality measures, known failure paths, access controls, human escalation and visibility into cost and behaviour. Aminexus designs those controls into the delivery approach and supports risk-aware delivery without presenting technical controls as legal advice.
Delivery approach
Move from a defined problem to an operating system.
We work through the decisions that make an AI system useful, controllable and practical to improve.
1. Frame
Define the user, decision, workflow, value case, constraints and success measures.
2. Validate
Test data readiness and solution behaviour with representative inputs before committing to full delivery.
3. Engineer
Build the application, integrations, controls, evaluation and deployment path.
4. Improve
Measure real usage, investigate failure patterns and manage changes to models, prompts and workflows.
Outcomes
Evidence and controls for the next decision.
Defined value case
A scoped problem, intended user and measurable outcome that justify the work.
Representative evaluation
Test inputs and quality measures that reflect how the system will actually be used.
Deployable system
An engineered solution with integrations, controls and a clear release path.
Operating visibility
Signals for quality, failures, usage and cost that support ongoing improvement.
FAQ
AI services FAQ
Do we need to choose a model before engaging?
No. Model choice follows the use case, data, quality requirements, operating environment and commercial constraints.
When is RAG appropriate?
RAG can be useful when a system must work with controlled or changing knowledge sources. It is one design option, not the default answer to every AI requirement.
How do you approach AI governance?
We define practical controls around data access, evaluation, human review, change management and accountability. Regulatory or legal interpretation remains with the appropriate qualified advisers.
Start with the business problem, not the model.
Share the workflow, intended users and what success needs to look like. We will help shape a useful first step.
Discuss an AI opportunity