LLM application development

Model development focuses on training or adapting the model itself. LLM app development builds a product around a model. This service covers applications and deployment using existing models.

The product interface, prompt and model orchestration, a knowledge or retrieval layer where the use case needs one, integrations with your systems, permissions, evaluation and deployment. Scope is agreed per use case before build starts.

Yes. Existing APIs, databases and internal applications are connected through explicit schemas, identity scope and validated read and write boundaries rather than ad-hoc calls.

Model choice follows the problem. OpenAI, Claude, Gemini and other providers can be used, and the application is built so a model can be changed without rewriting the product around it.

That depends on the provider, product, model, deployment and contractual settings. Private documents can be connected through permission-aware retrieval, but that does not by itself prevent a provider from retaining submitted context. Before sending sensitive data, check the selected service terms, training controls, retention periods, deletion options and any feature-specific exceptions. Retention and use for training are separate questions; application logs and connected tools also need review. Tenant and document permissions should limit what each user can retrieve, with the agreed data-handling boundaries recorded before implementation.

Through repeatable evaluation scenarios, structured-output checks, grounding and citation checks, failure-mode review and production monitoring - tracked over time rather than judged from a single demo.

Use-case clarity, data readiness and access, integration count, permission and compliance requirements, evaluation depth and the scope of the interface shape delivery.