Safer Workflows for AI-Assisted Coding

AURA is an AI-augmented research platform designed to help innovation teams move from broad research synthesis to clear, testable hypotheses. Instead of producing a static literature review, AURA generates evidence-backed “innovation bets” that can be compared, ranked, refined, and used to guide early-stage decision-making.

The system adapts ideas from AI co-scientist research into a domain-aware workflow for emerging technology strategy. Users begin with a research topic, then AURA asks clarifying questions, generates targeted search queries, gathers evidence, and produces structured hypotheses grounded in technical, regulatory, market, and operational signals.

Each hypothesis is evaluated across multiple dimensions, including impact, feasibility, cost, risk, time-to-market, profitability, and customer desirability. A tournament-style ranking process compares hypotheses against one another, while an evolution step combines or improves the strongest ideas into more specific and actionable bets.

AURA was tested through mobility-focused use cases, including eVTOL and robotaxi research. These demonstrations showed how the same research question can produce different strategic outputs depending on the domain profile, scoring weights, evidence sources, and decision criteria applied to the topic.

The project highlights a shift in AI-assisted research: from summarizing what is already known to helping teams decide what is worth exploring next. By combining evidence gathering, hypothesis generation, domain-aware evaluation, and iterative refinement, AURA offers a framework for turning complex research landscapes into clearer paths for innovation.