DeepResearch generates comprehensive reports by combining multi-step processing, structured data aggregation, and customizable output formatting. Instead of stopping at a single answer, the system breaks down complex queries into subtasks, gathers and cross-references data from multiple sources, and organizes the results into a coherent narrative. This approach mirrors how a developer might build a pipeline: first extracting raw data, then transforming it through validation and analysis, and finally loading it into a structured format tailored to user needs.
The process begins with query decomposition. When a user submits a request, the system identifies key components (e.g., technical concepts, timeframes, or comparison criteria) and uses specialized modules to address each part. For example, a query about “machine learning deployment challenges” might trigger separate searches for scalability issues, tooling limitations, and team skill gaps. These modules then pull data from APIs, databases, and indexed research papers, filtering out low-quality sources using predefined reliability metrics. Conflicting information is resolved through weighted voting—for instance, prioritizing peer-reviewed studies over forum discussions unless recency is a user-specified factor.
Finally, the system synthesizes results using templates and rulesets that determine report structure. A technical audience might receive sections like “Architectural Tradeoffs” or “Performance Benchmarks,” complete with code snippets or diagrams generated from raw data. Developers can customize outputs by adjusting parameters like depth (e.g., including implementation details for Kubernetes vs. high-level cloud strategies) or attaching supplemental materials like dataset samples. This modular design allows DeepResearch to scale from brief summaries to 50-page technical documents without rewriting core logic, similar to how a CI/CD pipeline automates testing and deployment stages.