How One Research Firm Cut Trend Reports to 35 Min With AI

The platform traditionally relied on senior analysts responding reactively to client questions. There was no scalable mechanism to proactively identify emerging signals — internet trends, consumer sentiment, behavioral shifts — that typically precede measurable sales changes.

Fractional AI built a modular research-agent system that mimics experienced analyst workflows in two stages: topic generation, where agents scan public web data to surface emerging trends for specific brands or categories, and structured evaluation, where affirmative and negative agents debate a trend's importance using sales data and external evidence while a third agent simulating a brand executive renders the final decision.

The system uses composable agent loops (topic generation, proposition evaluation, debate orchestration, stakeholder simulation) built on the OpenAI Agent SDK with the o4-mini model. Streamlit supported prototyping and FastAPI runs production, with Braintrust for evaluation. Research was deliberately separated from judgment to reduce bias — research agents gather evidence while the judge reviews briefs without independent tool access.

Best fit for market-intelligence or research businesses that want to shift from reactive analyst work to proactive, low-cost, scalable trend discovery.