Amplifying is an intelligence and optimization platform designed for developer tool companies to understand and influence the choices AI coding agents make. Created to address the shift in software distribution, Amplifying provides systematic research and data-driven insights into how agents like Claude Code, OpenAI Codex, and Cursor interact with codebases. By running these agents against real-world repositories, the platform identifies which tools are recommended by default, why they are chosen, and how these patterns evolve over time. This data is critical for vendors looking to defend their market position and for investors tracking the future of the developer tool stack.

Functionality involves a continuous benchmarking process that executes coding agents on diverse project types and stacks. The platform captures primary recommendations, alternative tool suggestions, dependency installation decisions, and the agent's internal reasoning. This information is synthesized into a private intelligence dashboard, enabling companies to track their category rank, monitor competitor positioning, and analyze the impact of new model releases on their own adoption rates.

Some of the key features are:

• Benchmark dataset: Structured runs of leading coding agents on real-world codebases to capture primary tool picks and reasoning.
• Intelligence dashboard: A private platform for visualizing pick rates, competitor maps, and per-model breakdowns.
• Continuous re-runs: Automatic suite refreshes within 24 to 48 hours after major model updates to ensure data accuracy.
• Strategic playbooks: Evidence-based recommendations for positioning, SDK improvements, and partnership opportunities.
• Vendor analytics: Detailed signal tracking on prompt triggers and stack heatmaps that influence agent decisions.

Operationally, Amplifying maintains a rigorous research methodology that treats AI agents as distribution channels rather than simple chat interfaces. By using actual CLIs and real project environments, the platform avoids the biases often found in synthetic testing. Vendor clients receive access to these findings to see their category through the eyes of the AI. The system provides actionable data on why specific tools are adopted or ignored, allowing teams to adjust their product surface, CLI tools, or SDKs to align better with what agents find most useful.

Some common use cases include:

• Competitive Benchmarking: Devtool vendors can measure their market share in the agent channel compared to their top 5 competitors.
• Positioning Defense: Companies can identify when and where an agent shifts its preference away from their tool to a DIY or competitive alternative.
• Product Optimization: Product teams can use agent verbatim reasoning to identify usability gaps in their CLI or SDK that prevent agents from recommending them.
• Market Analysis: Investors can use Amplifying's longitudinal data to forecast adoption trends in categories like vector databases, infrastructure, and observability long before they appear in public earnings reports.