Market research projects produce mountains of data - survey results, industry reports, competitor analyses, customer interviews - that need to be synthesized into actionable insights. The synthesis step is where most projects stall.

The Problem

A typical market research effort involves reading 10-20 industry reports, analyzing survey data, and conducting interviews. The raw material is valuable, but turning it into a concise brief with clear implications takes days of analyst time. Meanwhile, decision-makers need answers now.

The AI Approach

An LLM can read multiple research inputs and synthesize them into a structured brief that identifies common themes, contradictions between sources, key statistics, and strategic implications. It does in minutes what an analyst takes days to produce as a first draft.

Three-Step Build

Step 1 - Source compilation. Gather all research inputs: report excerpts, survey results, interview transcripts, and competitive data. Structure each source with metadata (source type, date, credibility rating).

Step 2 - AI synthesis. Feed the compiled sources to an LLM with instructions to identify the top five findings, note where sources agree and disagree, extract key statistics, and articulate strategic implications.

Step 3 - Structured output. Generate a research brief with sections: executive summary, key findings with supporting evidence, market sizing data, competitive landscape, and recommended actions.

Where It Breaks

The model may over-weight sources with more text regardless of credibility. Proprietary data and paywalled reports may not be available for input. Statistical analysis beyond simple aggregation requires dedicated analytical tools, not an LLM.

The Production Path

Build a research template that standardizes output format across projects. Add source credibility weighting to improve synthesis quality. Create a research library that accumulates insights over time, making each subsequent project faster as baseline knowledge grows.