Overview
AI integration engineering is the design and implementation of the connections around a model: application requests, identity, business data, permitted tools and monitoring. It makes an AI feature usable inside a product or internal system while keeping business rules in the application layer.
A prototype may use a single document and a developer’s account. A production feature must handle different users, changing records, unavailable dependencies and inconsistent answers. Our integration work focuses on these boundaries so your team can understand how information moves, which system is authoritative and where a failure must be handled.
Where this can help
Illustrative applications, not claims about completed client projects.
Knowledge inside the product
Bring answers from approved manuals, policies or product documentation into an existing interface. The user receives references to the source material and the product applies access rules before content is used.
Business context on demand
Expose selected CRM, ERP or service-management operations through documented tools. The integration retrieves current facts from the responsible system rather than asking the model to infer a live status.
AI features for SaaS teams
Add assistance to an existing application with request validation, tenant boundaries and feature-level usage tracking. Product teams keep control of the user journey and release policy.
What we can deliver
- Interface specification
- Request and response contracts, authority of each data source and error semantics.
- Connectors and retrieval
- Approved system adapters and, when needed, a pipeline for searchable company knowledge.
- Access and validation
- User-aware authorization, output validation and a defined handling path for unacceptable responses.
- Operating visibility
- Traces and measurements that connect a user-facing failure to the model, tool or source involved.
Engineering considerations
We distinguish retrieval from execution. Retrieval supplies relevant information; a business tool accesses live state or changes it. Both require access control. Retrieval-augmented generation (RAG) also needs an update and deletion path when the underlying content changes. Structured model output is checked by application code before downstream use. Rate limits, timeouts and retry policies are defined per dependency rather than treated as a generic reconnect loop.
Working with EU and US teams
A cross-border implementation needs an explicit data path. We document the model service, storage, search index and logging destinations proposed for the feature. Your stakeholders can then review provider terms, residency expectations and allowed categories of information. For international SaaS products, tenant separation and region-specific configuration should be considered in the architecture. These are project requirements to validate, not blanket claims of regulatory compliance.
A practical first engagement
Select one feature that already has a product owner and an expected place in the interface. Agree its input contract, reference scenarios and fallback behavior. Test with inaccessible content, missing data and provider failures as well as normal requests. For knowledge retrieval, inspect the evidence returned by the search step separately from the final answer. This helps establish whether the issue is source coverage, retrieval or model behavior.
Evidence for the next decision
- Feature usefulness
- Does the response help the intended user complete the product task?
- Boundary correctness
- Are permissions, tenant separation and source references respected?
- Service behavior
- What are the latency, error frequency and cost of the complete feature?
Questions about this service
Can you integrate AI without replacing our application?
That is often the objective. We identify a suitable boundary in the existing application and assess whether the integration belongs inside it or in a separate service.
Is a vector database required?
Not always. Search requirements may be met by keyword search, vector retrieval, a combined approach or an existing knowledge service. The choice follows the content and evaluation results.
Will the model be trained on our company data?
Connecting documents for retrieval is different from training a model. Provider data-use terms, retention and configuration must be reviewed for the selected service before company information is used.
Can the integration run in our infrastructure?
Deployment options depend on the model, connected services and your infrastructure standards. We can scope customer-controlled deployment where the selected components support it; this does not automatically mean every dependency is local.
Who maintains the integration after launch?
The delivery scope should assign ownership for API changes, content refreshes, evaluations and incident handling. We document those responsibilities and agree any continuing engineering support separately.
Before the statement of work
A buyer’s guide to the decisions that shape an enterprise AI engagement: ownership, data, acceptance and operating responsibility.
Read the planning guide →