Relevant, permitted context
Specify which sources the feature can use and how access follows the application's user permissions.
Services / AI integration
AI integration for your existing software.
AI integration adds a defined capability, such as summarising records or extracting information, to software you already use. Advenno builds the application connections, permissions, evaluation and review flow around that capability.
01 / A capability inside your product
Connect the model to the real application.
AI integration can support tasks such as drafting a response, summarising a record, extracting structured information, or finding relevant material. The scope starts with the user's action and the expected result, including the cases where an answer should be withheld or reviewed.
The surrounding engineering matters: authentication, data access, application state, output validation, and recovery when a provider is unavailable. We define those boundaries before treating a successful prompt as a production feature.
Task-based evaluation
Review representative examples, failure cases, response time, and estimated usage costs against agreed criteria.
A controlled user action
Make review and correction possible. Define whether an output is a suggestion or can trigger a further action.
02 / Evidence before wider rollout
Make the release decision reviewable.
Bring the current architecture, a clear use case, representative inputs with appropriate access, and a person who can judge output quality. Data handling requirements and model-provider constraints need to be agreed early.
We use a narrow implementation to assess usefulness within the existing product. Acceptance criteria should cover both useful output and appropriate behaviour when context is missing, access is denied, or the model is wrong.
Handover should explain configuration, evaluation examples, provider dependencies, operating costs, and the process for reviewing future model changes. Release controls and ongoing monitoring are scoped with the product team.
Discuss an integration/ A FEW GOOD QUESTIONS
Before we build.
How is AI integration different from AI product development?
Integration adds a defined capability to software that already exists. Product development also needs the wider application: users, workflows, interfaces, data structures, and a useful first release.
How do we judge whether the feature is good enough?
Agree a set of representative tasks and failure cases before rollout. Review output quality alongside latency, cost, permission handling, and the consequences of an incorrect answer. The acceptance threshold depends on the use case.
Will an AI feature always produce the right answer?
No. The design needs to account for incorrect or incomplete output. Depending on the task, that can include validation, source references, human review, limited actions, and a clear way to report or correct a result.
/ NEXT, YOUR CHALLENGE
Let's make work
work better.
Bring the process, the bottleneck, or the idea.
We’ll work out a useful first step together.