AI engineering and LLM integration

This service suits teams moving from an idea or prototype into a product, or improving an integration whose quality or cost is difficult to understand. It is useful when the demo works but the team needs evidence about real inputs, failure cases and the consequences of model or workflow changes.

We begin with the task the feature should perform, the data it can use and the expectations of its users. I review the current approach and help define the behavior that needs to be evaluated before choosing an integration or agent design.

The scope can include LLM integration, agent and pipeline engineering, an evaluation harness and model or vendor assessment. Quality, usage cost and runtime constraints are considered together. The goal is to give the team a working implementation and a repeatable way to examine its strengths and limitations.

Possible deliverables

• llm integrations
• agent systems
• evaluation harness
• model and vendor read

Bring the use case, representative inputs, expected outputs and any existing prototype. Explain where the current behavior succeeds or fails, which data is available and what cost or response-time constraints matter. Examples from the intended workflow are more useful than a demonstration built only around ideal inputs.