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.
Your team should not have to copy information into a separate chat window for every summary or draft. Advenno integrates defined AI capabilities into existing software, connecting permitted business data with the application, validation and human review the task needs.
01 / A capability inside your product
How do you add AI to existing software?
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.
AI inside the workflowA useful suggestion still needs a route to action.
Illustrative- Your applicationA user starts a defined task
- Permitted contextOnly the relevant records
- AI suggestionDraft, extract or summarise
- Review & applyCorrect it before using it
Access rules, output checks and a correction path are part of the application.
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/ QUESTIONS, ANSWERED
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.
Can you add AI to our existing software without rebuilding it?
Often a defined feature can be added through the existing application or its interfaces. We first review the architecture, permissions and available integration points. A limited rollout should keep the ordinary task usable if the model is unavailable or its output needs review. A rebuild is not assumed; any required changes should be explained in the scope.
Can an AI feature use our own documents and business data?
Yes, where the source systems and permissions allow it. Define which records are available to each user, how current the information must be and what sources an answer should reference. Access checks and data handling belong in the application design, not just the prompt.
Do we need to train our own AI model?
Not necessarily. An existing model combined with relevant, permitted context may be sufficient for the task. Compare a simpler integration with model customisation using representative examples, quality requirements and operating costs before committing to the more complex approach.
What does AI integration cost to build and run?
Build cost depends on the existing application, data connections, permissions, evaluation and review flow. Running costs can include model usage, retrieval storage, hosting and monitoring. Estimate those costs against expected usage and review them during the first rollout.
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.
Where will our data go when the feature calls an AI provider?
That depends on the provider and deployment selected. Identify the fields being sent, retention settings, processing locations and access arrangements before implementation. Review your requirements against the actual service configuration; do not assume every model or account has the same data terms.
/ NEXT, YOUR CHALLENGE
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