Automate econometric & policy figure creation with AI-driven workflows

Executive Summary

Policy researchers, government analysts, think tanks, and consultancies spend disproportionate time turning econometric outputs into publication- and policy-ready figures, often duplicating manual steps across Stata/R/Python and producing outputs that are hard to audit or reproduce; this problem affects an estimated 80,000 target organizations whose aggregate willingness-to-pay implies a $2.4B market (80,000 × $30K ACV). The consequence is slow turnaround, inconsistent visuals for counterfactual analyses, and growing noncompliance risk as funders and journals increasingly require code and data provenance. A practical product would be an AI-driven workflow that ingests model outputs, applies domain-specific templates, generates annotated code and publication-quality visuals, and emits reproducible notebooks and auditable metadata. Core features would include connectors for Stata/R/Python, LLM-assisted translation of model output into annotated plotting code, versioned pipelines for reproducibility, and export formats tailored to journals and policy briefs; in many workflows this can potentially cut manual figure production time by half or more. This market looks attractive now because reproducibility requirements, the rise of AI-assisted coding, and expanding demand for policy-focused analytics are converging—your market score of 92/100 and revenue potential rating of 86/100 reflect that timing, and competition is currently low. To stand out you must combine robust statistical validation and human-in-the-loop guardrails with clear audit trails, domain-tuned language models, and strong integrations with common econometric tools; challenges include handling heterogeneous model formats, building trust in AI-generated code, and the initial effort to curate training examples and validation rules.