structai

You can enforce structured LLM outputs by configuring a Python workflow that validates dict and list-style responses, applying exact-match or math verification to ensure model answers conform to your required schema.

Batch inference and PDF parsing can run in one consistent workflow by executing concurrent jobs that extract PDF text, clean it, and generate structured summaries simultaneously with controlled timeouts and concurrency.

LLM judging evaluates model answers by running evaluation loops that compare responses using exact-match, math verification, or an LLM-as-a-judge approach to measure answer quality within your application workflow.

Concurrent LLM batch jobs support controlled retries, time limits, and concurrency settings to manage multi-turn model interactions safely, ensuring reliable execution when handling large volumes of batch inference tasks.

Private network API access is managed through environment-based API configuration, ensuring safe handling of private endpoints when wiring together model calls, parsing outputs, and validating results.

Structured outputs are needed for LLM agent workflows to reduce the friction of wiring together multi-turn model interactions, allowing configurable prompts and multimodal inputs to produce validated, predictable results.