Deep Research

AI agents autonomously perform multi-step research and generate detailed, cited reports efficiently.

Business impact

• Research productivity — Reduces manual effort and time spent on gathering and synthesizing information
• Time to insight — Shortens duration from question to actionable, well-supported answers and reports
• Operational efficiency — Automates repetitive research workflows, freeing resources for higher-value tasks
• Report accuracy — Improves factuality and citation quality through multi-source verification and reasoning
• Compliance adherence — Supports governance by integrating secure data handling and audit trails

Data requirements

• Open web data (Text) — Used for broad, real-time information gathering and context enrichment
• Proprietary enterprise databases (Structured) — Integrates internal knowledge and confidential data for comprehensive analysis
• Financial market data (Numeric) — Feeds domain-specific insights for finance-related research workflows
• Documents and reports (PDF, Word, Sheets) (Text) — Supports multi-format input for richer, contextual research synthesis
• Multimodal inputs (images, charts) (Image) — Enables generation and inclusion of visual analytics in research reports

AI methods and techniques

• Agentic AI — Autonomously plans and executes multi-step research workflows with iterative refinement
• Predictive AI — Forecasts relevant information needs and guides search strategies dynamically
• Generative AI — Synthesizes comprehensive, human-readable reports with citations and visuals

AI models and model families

Gemini 3.1 Pro, OpenAI o3 reasoning model, Claude 3.7 Sonnet, GPT-4o, smolagents

Industries

Real-world evidence

6 documented case studies on record.

Companies using this: Alibaba, BNY, FactSet, Google, OMNY Health, Opera.