What it means for your business
AI Integration Engineering connects AI models to applications, enterprise data and operational tools. It covers APIs, user identity, knowledge retrieval, permitted actions, error handling and quality monitoring. The goal is a controlled AI capability inside an existing system.
When it makes sense
When a language model demo works but is disconnected from company data, users and operational rules. The integration layer turns an AI capability into an application feature that respects permissions, handles failures and produces results that can be evaluated.
Example workflows
Illustrative workflows. The actual scope and system access are agreed for each project.
Enterprise knowledge retrieval
Question → permission check → retrieval of accessible documents → response with source references. RAG adds retrieved context to the model request; quality depends on both retrieval and the accuracy of source material.
AI inside a SaaS product
A user starts a feature in a familiar interface. The backend verifies access, prepares a request, validates the result and returns it to the application. Data belonging to different customers must remain isolated.
Actions through business APIs
The model proposes a tool call and the server validates parameters and permissions. Looking up an order status uses a dedicated API; changing the order needs additional checks.
Choosing an approach
| Approach | How it works | Best suited to |
|---|---|---|
| Direct model call | The model receives the bounded request context. | Classification, drafting and field extraction. |
| RAG | Relevant source information is retrieved before answering. | Working with changing knowledge bases and documents. |
| Tool calling | The application executes an authorized API request. | Retrieving current state or performing an action. |
Controls and reliability
Access controls must apply to retrieved documents and each tool, not only the login screen. Secrets remain on the server. We validate output schemas, limit request size and handle provider timeouts and rate limits. Write operations need a separate retry strategy. Logs should retain enough information for diagnosis without unnecessarily exposing sensitive content.
The first pilot
We define contracts between the application, model and data sources. We decide where retrieval is needed, where an API call is appropriate and where deterministic code should remain in control. Response formats, parameter validation, secret management and failure handling are agreed before testing in a staging environment.
Select one feature and one data source. Review API coverage or document quality, and prepare reference questions and difficult examples. For RAG, evaluate retrieval separately from the answer: was the right source found, and was it used correctly? Then monitor latency, failures and cost. A model change requires re-evaluation; a shared interface does not make different models behave identically.
Scope of work
- Model integration through APIs
- RAG and enterprise knowledge retrieval
- Agent tools and system connectors
- Reliability, testing and observability
What you receive
Integration services and connectors, API documentation, contract checks, access configuration and observability. RAG implementations also need content preparation, index refresh rules and retrieval evaluations. Infrastructure choices reflect your requirements and existing stack.
What we measure
- Retrieval quality
- Whether the right material is found and inaccessible documents are excluded.
- Answer quality
- Grounding in sources, completeness and correctness of structured fields.
- Integration behavior
- API errors, timeouts, latency and behavior when a dependency is unavailable.
- Feature cost
- Model, retrieval, storage and retry costs per useful result.
Questions and answers
How is AI integration different from agent development?
Integration connects the model to data, an application and tools. An agent uses those connections to execute a workflow. An AI feature, such as document classification, does not necessarily require an agent.
Does every project need RAG?
No. RAG is useful when answers should rely on external or changing knowledge. Formal operations may be better served by APIs and code; a small context can sometimes fit directly into a model request.
Can we keep our existing .NET backend?
Yes. An AI integration can live within the current backend or in a separate service. Architecture, workload and operational requirements guide the choice.
How are company documents protected?
Permissions are checked during retrieval and context delivery. Allowed data, model providers, logging and hosting requirements are agreed before connection. The specific controls depend on the project.
Can we switch model providers?
An integration layer can reduce coupling to one API. Switching still requires evaluation of quality, tool formats, latency and cost.
Prepare for the first conversation
Describe the workflow, current systems and expected outcome. The first discussion identifies discovery questions and the boundaries of a possible pilot.
Open the project brief →