TECHNOLOGY
How we design systems meant to be used for years
This page describes PRESIRE's technical approach and design principles. It describes how we work, not the current state of any specific product — for that, see the maturity stated on each product page.
ARCHITECTURE
System architecture
We decompose systems into units with clear boundaries that can be understood and tested independently. The practical test of an architecture is whether someone taking it over in three years can change it safely without reading the entire codebase first.
- Explicit module boundaries and interface definitions
- Data model before page implementation
- Core business logic decoupled from external services
- Model abstraction layer, not bound to a single AI provider
- Observability designed into the system rather than bolted on afterwards
AI GOVERNANCE
AI governance
Once AI enters operations, the real question is not how accurate it is. It is whether the organization can detect, trace and correct it when it is wrong. We design that into the system rather than writing it into a policy document.
- The scope AI may decide autonomously is written down
- Judgement-critical steps retain human review
- Every decision path leaves a traceable record
- Retrieval and answers must be able to cite their basis
- When no reliable basis exists, the system says so rather than speculating
- The scope of any third-party AI provider use is explicitly disclosed
DEPLOYMENT
Deployment
Deployment shape is not a technical preference. It follows from the data boundary and compliance requirements. We confirm the constraints during design, then choose the shape.
- On-premise, private cloud or hybrid assessed against data classification
- Environment configuration and secrets managed separately
- Repeatable deployment pipeline, not dependent on specific individuals
- Version control with a rollback path
- Monitoring and alerting included in delivery scope
Frequently asked questions
- What deployment approaches does PRESIRE support?
- We assess on-premise, private cloud and hybrid deployment against an organization's data boundary and compliance requirements. Which shape is viable depends on the specific constraints and resources of each case.
- What are PRESIRE's AI governance principles?
- Four core points: the scope AI may decide autonomously is written down; judgement-critical steps retain human review; every decision path leaves a traceable record; and when no reliable basis exists the system says so rather than speculating.
- Is PRESIRE tied to a specific AI provider?
- No. Model abstraction is one of our design principles, so systems can substitute providers rather than depending on a single one.