LLM Integration.

Choose a feature with a clear result

This service suits an application that already has users and a specific task AI could assist. A summary that helps staff review a long record is different from an agent that changes records by itself. We define the input, expected output and the point at which a person must review the result.

Document search belongs to the RAG service when the feature needs answers grounded in a maintained knowledge base. A multi-step tool-using workflow belongs to agentic automation. Keeping these scopes separate makes the proposed integration easier to assess.

Set data and acceptance boundaries

We identify which fields may leave your application, who may trigger the feature and what provider configuration is appropriate. Retention, regional processing and contractual requirements need checking against your chosen provider and account; a model API is not automatically a compliance solution.

A representative evaluation set includes short, long, ambiguous and invalid inputs. For extraction, we agree required fields and how missing values should be represented instead of allowing the model to invent a value.

Integrate on the server, with useful states

Model credentials stay on the server. I connect the agreed provider, structure requests and validate results before the application uses them. Structured output can help match a schema, but factual accuracy still requires task-specific checks.

The interface shows progress, allows a user to review the result and explains when a request fails. Timeouts, bounded retries and usage limits prevent a temporary provider problem from becoming an uncontrolled loop.

Evaluate quality, latency and cost together

We compare candidate configurations against the agreed examples and record failures alongside successful outputs. A smaller model may be sufficient for one task while another requires a different approach. No model is selected solely from a headline benchmark.

I review latency and expected request size, and document the provider costs that remain outside the build fee. Logging is limited to information needed to diagnose the feature without retaining unnecessary personal or confidential text.

The integration you receive

Deliverables include the agreed feature, server integration code, prompt and schema configuration, evaluation examples, failure handling and operating notes. You retain a clear description of what the feature can do and what still requires human judgement.

An illustrative use is turning a support conversation into a draft internal summary that a staff member can edit before saving. This is a possible implementation pattern, not a claim of measured results for a past client.

What I need to scope the work

Share the application stack, the task you want to improve and anonymised examples of the input and desired output. Include confidentiality constraints and approximate expected usage. That is enough to start assessing whether an LLM integration is appropriate and what a bounded first release should include.