A useful AI capability should fit the product around it. inAi helps software companies and internal product teams integrate AI into existing applications without treating the model connection as the whole project.
The work can be an embedded assistant, structured extraction, a drafting tool, classification, search or a bounded workflow across existing services.
Fit the existing system
We begin with the current user journey, application architecture, authentication and permissions. The integration should understand which workspace a user belongs to, which records they may access and what the application expects as a valid result.
We define the boundary between your software and the AI service: API contracts, data sent to model providers, response formats, timeouts, error handling, usage limits and responsibility for changes.
Choose the smallest useful intervention
Sometimes the right first release is a new backend endpoint and one review screen. Sometimes it needs background jobs, document ingestion or a separate retrieval index. We choose the architecture around the workflow rather than require an agent or a vector database in every project.
For example, an existing request-management application could gain a source-linked draft response. The user keeps the same request record, reviews the draft, changes it where necessary and saves an approved version. The integration does not need permission to send anything externally unless that is separately included.
Deliverables that make integration maintainable
An engagement can include an architecture review, API and event contracts, implementation, data mapping, integration tests, model evaluation, user-interface changes, usage controls and deployment documentation.
We agree how versions are managed, how output formats are validated and what happens when a downstream service is unavailable. Where appropriate, the AI capability can be disabled without preventing the surrounding application from functioning.
Release progressively
Define an initial user group and test set. Verify both AI output and ordinary software behaviour, including access control and invalid inputs. Introduce the capability behind the agreed release mechanism, monitor it and establish rollback conditions.
A model or prompt change is a product change when it affects user outcomes. It should pass the relevant regression checks before reaching the whole user base.
What your team supplies
Useful starting material includes API documentation, a description of the data model, test access, sample records and the intended user journey. Your technical owner should help define the boundary with existing infrastructure and review changes that affect the product.
We can work in your repository and release process where agreed, or deliver a bounded service with an explicit interface and handover. The proposal identifies which team owns monitoring and incident response after launch.
Common questions
Do we need to rebuild the product? Not automatically. The initial review identifies the smallest credible integration and any existing constraints that must be addressed.
Can the model provider change later? We can design a provider boundary and evaluate alternatives. Switching is not assumed to be costless: behaviour, features and operating constraints may differ.
Can our users review outputs first? Yes. A review-and-approve workflow can be part of the product design rather than an afterthought.