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AI Development covers AI agents, business chatbots, retrieval-augmented generation, LLM integration and custom model work, natural language processing, and evaluation with production support. The starting point is the task your users need to complete and the data, tools, and review steps that task requires.
These approaches address different needs and can be combined. RAG retrieves relevant information for a model to use. Agents call tools within a workflow. Model customization can address behavior or task requirements. We assess data quality, evaluation results, and operating constraints before choosing an approach.
Agree on representative tasks and acceptance criteria before rollout. Evaluation can cover answer correctness, retrieval quality, task completion, inappropriate actions, latency, and cost. Review difficult cases with the people who perform the work, then use the same criteria to check changes after deployment.
We map what information the application needs, who may access it, and which tools it may call. Deployment design addresses data handling, permissions, and logging requirements. Actions such as sending messages or changing records can require approval, with review paths for uncertain or incomplete results.
Bring a workflow, sample inputs, and examples of a useful result. We can help define the application and how your team will evaluate it.