Task boundary
Define the narrow decision or transformation the model assists and the cost of an incorrect result.
Applied AI systems
AI Integration Engineering.
A production AI feature is a measured workflow around a model, not a prompt attached to a text box.
How we use it
We integrate AI where probabilistic interpretation improves a defined task. Inputs, context, tools, policies, evaluation, review, and monitoring are designed as one system so model changes do not silently change product behavior.
Architecture decisions
The questions that shape the implementation
Context
Control retrieval, permissions, freshness, source attribution, and context size.
Evaluation
Use representative cases and quality thresholds instead of relying on demonstration prompts.
Action safety
Separate suggestions from consequential actions and require validation or approval where appropriate.
Engineering practice
What disciplined use looks like
Provider abstraction where useful
Structured output validation
Representative evaluation sets
Retrieval permission checks
Cost and latency budgets
Human escalation and traceable decisions
Failure modes
What we actively design against
Confident unsupported output
Sensitive context crossing access boundaries
Silent behavior changes after model updates
Unbounded agent tools
No baseline for judging improvement
Appropriate use cases
Knowledge retrieval
Document extraction
Request classification
Draft assistance
Semantic search
Reviewer copilots
Technical questions
Can AI integration be model-independent?
The application can reduce lock-in through stable internal contracts and evaluations, though provider capabilities and behavior will never be perfectly interchangeable.
How is AI quality measured?
We define task-specific cases, expected properties, failure categories, reviewer criteria, and production feedback rather than using one generic accuracy score.
Related expertise