Hamming vs. DoCoreAI

Experience automated voice testing and monitoring like never before. Quickly evaluate your AI voice agent with thousands of simulated users in just minutes, simplifying a process that typically requires extensive effort. Achieving optimal performance from AI voice agents can be challenging, as even minor adjustments to prompts, function calls, or model providers can significantly impact results. Our platform stands out by supporting you throughout the entire journey, from development to production. Hamming empowers you to store, manage, and synchronize your prompts with your voice infrastructure provider, achieving speeds that are 1000 times faster than conventional voice agent testing methods. Utilize our prompt playground to assess LLM outputs against a comprehensive dataset of inputs, where our system evaluates the quality of generated responses. By automating this process, you can reduce manual prompt engineering efforts by up to 80%. Additionally, our monitoring capabilities offer multiple ways to keep an eye on your application’s performance, as we continuously track, score, and flag important cases that require your attention. Furthermore, you can transform calls and traces into actionable test cases, integrating them seamlessly into your golden dataset for ongoing refinement.

DoCoreAI is a dedicated platform that enhances the optimization of AI prompts and telemetry, specifically designed for product teams, SaaS companies, and developers working with large language models (LLMs) like those offered by OpenAI and Groq (Infra). With a local-first Python client and a secure telemetry engine, DoCoreAI enables teams to collect valuable metrics on their LLM interactions while protecting the integrity of original prompts to maintain data privacy. Key Features Include: - Prompt Enhancement → Improve the efficacy and reliability of LLM prompts. - Monitoring LLM Usage → Track token consumption, response times, and performance patterns. - Expense Analysis → Review and refine costs associated with LLM usage across different teams. - Developer Productivity Metrics → Identify time efficiencies and recognize potential usage hurdles. - AI Telemetry Solutions → Compile detailed insights while ensuring user privacy remains a priority. By leveraging DoCoreAI, organizations can decrease token costs, enhance AI model efficiency, and offer developers a unified platform to scrutinize prompt performance in real-time, thereby cultivating a more streamlined workflow. This comprehensive framework not only enhances productivity but also encourages data-driven decision-making, ultimately leading to improved outcomes in AI deployment. Furthermore, the ability to monitor and analyze usage patterns helps teams stay ahead in the rapidly evolving landscape of AI technology.