legal_ops · services · workflow
Harvey integrates OpenAI Deep Research in under 12 hours using modular AI architecture
Harvey needed to rapidly integrate OpenAI's newly released Deep Research API, facing challenges including the API being too new for base LLM knowledge, complex streaming with many dynamic output types, and the need for code compatibility with Harvey's internal Workflow Engine and thinking states.
How it works
Common implementation structure
How this type of workflow is generally built, generalized across documented cases — not tied to any one vendor's stack. Click any stage to read what happens there. Specific products that implement these stages appear in “Tools commonly seen” below.
Stage 1 · User engages Harvey agent
Users engage with Harvey agents through product surfaces designed for legal tasks.
Tools used
HarveyDeep ResearchWorkflow EngineStreamlit
Outcome
Harvey successfully integrated Deep Research within less than 12 hours of the API's release, incorporating URL citations into their existing citation system so every step of the workflow can be traced and verified.
Results
Time savedless than 12 hours
Grounding & classification
Source type: technical build writeup
22 fields verified against source quotes.
agentic workflowai agentknowledge searchknowledge basehuman review describedmetric backedtools describedvendor confirmedworkflow describedlegalsoftwarecycle time reductiontechnical build writeuplegal document reviewlegal opsagentic task executionhuman review queue