deep-research

Protocol-driven business research orchestration enforces a serial, stage-ordered workflow between an LLM decision-maker and a runtime enforcement layer, eliminating freeform deviations to ensure auditable, repeatable results.

Start a session by selecting the entrypoint and loading shared contracts and a domain pack to orchestrate end-to-end business research sessions, executing explicit queries across root-cause, demand, and value layers.

Yes, you need shared contracts and a bridge runtime to bind the local Python runtime, maintaining semantic alignment and safety while executing explicit queries across multiple data contexts.

The best way to enforce a stage-ordered workflow for root-cause analysis is applying a serial protocol spanning Intent Recognition, Environment Discovery, Planning, Execution, Evaluation, Finalization, and Data Visualization.

Yes, you can adapt business research workflows for different data contexts by applying domain packs, which maintain disciplined, auditable insights across multiple data contexts without freeform deviations.

A runtime enforcement layer governs LLM orchestration by eliminating freeform deviations, ensuring the LLM decision-maker follows a serial, stage-ordered protocol to produce auditable, repeatable results.