Non-coders: run multi-AI research workflows without writing code

Executive Summary

Many knowledge workers—product managers, market researchers, consultants, and academic teams—need to run multi-step research that chains multiple AI models, tools, and data sources but cannot write or maintain code. Today they rely on developers, spreadsheets, or expensive freelancers, producing slow, error-prone, and irreproducible workflows that waste time and limit adoption. You could build a no-code platform that lets non-coders assemble, run, and iterate multi-AI research workflows via a visual canvas with connectors to multiple LLMs and modalities, templated agents for literature review, hypothesis testing and data extraction, plus built-in provenance, evaluation, and exportable pipelines. Adding a model marketplace, pay-as-you-go inference, role-based collaboration, and one-click integrations to common data stores would make complex research accessible without engineers. The timing is favorable: LLM-as-a-service and cheaper access to specialized models make chaining and specialization practical, while no-code tooling accelerates adoption; this maps to a $120B addressable market (300M knowledge workers × $400/year) with a market score of 92/100 and revenue potential 88/100. Competition is medium, indicating clear opportunity but requiring disciplined execution. To stand out, prioritize rigorous provenance and evaluation so outputs are auditable, build high-quality research templates for initial verticals, and offer a hybrid deployment model that supports enterprise security and model choice. Be candid about challenges—UX complexity, managing model and compute costs, and slow enterprise procurement—and target early customers with high ARPU and measurable ROI to validate the thesis before scaling.