market-research

Automated competitive intelligence gathering combines Exa's semantic search APIs with a five-stage analytical workflow to collect competitor positioning, customer sentiment, and market trends, producing an evidence-based strategy document in minutes. This eliminates manual data collection and structuring.

The best way to conduct a moat analysis for investor due diligence is applying the Seven Powers framework to automatically collected competitor data. This generates specific investor risk assessments and stress tests, validating foundational assumptions about market entry and competitive advantages.

Yes, you can generate a full market entry analysis without manual research by running target industries and competitor domains through automated parallel data collection. This outputs a comprehensive strategy document covering customer pain points, competitor differentiators, and market gaps in 2-5 minutes.

Yes, this market research workflow requires the exa-py API dependency for semantic search and parallel data collection, alongside pydantic for structuring the collected data into validated analytical models required for the five-stage strategy generation workflow.

To perform due diligence on specific competitor domains, input the exact domain sets alongside the industry category into the automated research workflow. The semantic search APIs will target those domains directly to extract foundational assumptions, positioning, and unspoken market insights.

Limitations of using automated semantic search for competitive strategy planning include reliance on the availability and indexing of public competitor data, meaning highly stealth startups or proprietary internal metrics may not be fully captured in the generated investor risk assessments and moat analysis.