Engineering Production AI Systems

Behelitai designs and builds enterprise AI agents, knowledge systems and workflow automation. Published implementations cover structured interviews, contract review, support triage, operations reporting and controlled content generation.

Retrieval-augmented generation (RAG) supplies relevant documents to a model. An agent coordinates workflow steps and tools. Behelitai’s interview system combines role-knowledge retrieval with explicit assessment state, follow-up questions and reporting.

Controls include restricted tool permissions, approved retrieval sources, output validation and human review. Behelitai’s published architectures describe these safeguards; the controls needed for a particular deployment depend on its data and permitted actions.

Not every project needs fine-tuning. First evaluate whether instructions, retrieval quality and workflow constraints address the observed failures. Compare options against the task’s reliability, latency, governance and cost requirements.

Engagements isolate client data and restrict retrieval to approved sources; generated outputs are validated and sensitive actions require human approval. Safeguards include access control, transmission protection and permission isolation. Specific data handling terms are described in the privacy policy.

Describe the workflow, its users, available data, existing systems and the outcome you want to measure. Include access restrictions and actions requiring human approval so the initial discussion can focus on scope and evaluation criteria.