Acumen AI addresses complex AI integration barriers preventing organizations from accessing advanced conversational AI due to technical complexity and expensive infrastructure. Our solution democratizes AI through an intelligent platform combining Groq's high-performance inference, Llama-3.3-70b-versatile model, MCP agents and Streamlit interface deployed on Vultr’s cloud server. The 4-layer architecture features asynchronous processing, persistent memory, and advanced error handling, enabling natural language conversations with 10-step reasoning capabilities. Users interact through a responsive web interface, initializing AI agents, engaging in real-time conversations, and managing memory without technical expertise. The Total Addressable Market (TAM) for conversational AI reaches $32.6 billion globally, with our Serviceable Addressable Market (SAM) targeting $8.4 billion in enterprise AI assistants. Revenue streams include SaaS subscriptions, API usage fees, premium features, white-label licensing, and consulting services. Unlike competitors such as ChatGPT Enterprise or Microsoft Copilot, Acumen AI offers open-source flexibility, superior MCP agent reasoning, cost-effective Groq infrastructure, and specialized conversational AI focus rather than general-purpose tools. Future prospects include horizontal scaling, knowledge base integration, multi-modal capabilities, and vertical-specific solutions, positioning Acumen AI as a foundational platform for next-generation accessible conversational systems.

Our team built an agent-driven Healthcare Safety Platform designed to arrest James Regen’s “Swiss-cheese” iatrogenic cascades by unifying disparate hospital data into a Databricks Lakehouse and surfacing real-time risk insights. We began by defining the problem scope—10 percent of inpatients suffer preventable harm when latent system flaws align with active errors—then organized our work around four specialized personas. Agentic Maya Thompson led a strategic analysis of EHR admission/discharge records, incident and near-miss logs, and staffing schedules to prioritize the failure modes that most undermine patient safety and throughput. Carlos Reyes ingested data streams from EHRs, medical devices, wearables, and clinical protocols via Auto Loader into Bronze, Silver, and Gold Delta tables, codified transformation logic in Delta Live Tables, and enforced data governance with Unity Catalog to ensure compliance and lineage traceability. Dr. Priya Singh developed and rigorously validated predictive models—combining lab values, time-series vitals, protocol deviation flags, and staffing ratios—to flag patients at highest risk of cascading harm, audited model fairness across units, and registered top-performing versions in MLflow. Finally, Olivia Chen translated complex risk scores and incident trends into an intuitive dashboard using Databricks SQL and an embedded React interface, designing sliding-scale gauges, alert workflows tied to staff schedules, and drill-down incident timelines that guide timely, targeted interventions. Over multiple iterations, the team tagged each other on data-readiness checks, schema clarifications, feature requests, and prototype refinements in our integrated chat system, converging on a production-ready solution that continuously monitors care pathways, predicts misalignment in advance, and closes the “holes” in our clinical defenses—turning fragmented hospital data into life-saving insights.

Long-term Vision: The long-term vision for the Customer Review Improvement Agency includes the following: Scalability and Expansion: Continuously enhance the platform with advanced features such as AI-driven predictive analytics, advanced sentiment analysis, and multilingual support to cater to global businesses. Partnerships and Integrations: Form strategic partnerships with major review platforms, e-commerce sites, and social media networks to provide seamless integration and real-time data synchronization. Educational Resources: Develop educational resources and training programs for businesses to better understand the importance of customer reviews and how to manage them effectively. AI and Machine Learning Enhancements: Integrate machine learning algorithms to predict trends in customer feedback and provide actionable insights for businesses to preemptively address potential issues. Global Outreach: Expand our services to international markets, adapting our platform to cater to different cultural and linguistic needs, ensuring a global presence. Customer Loyalty Programs: Develop and manage loyalty programs that not only incentivize positive reviews but also foster long-term customer relationships through rewards and recognition. Comprehensive Analytics: Provide businesses with in-depth analytics and reports that offer a holistic view of their online reputation and customer feedback, enabling data-driven decision-making

🗓️ This will be a 10-day virtual hackathon from June 28 to July 7 💻 Access AI21 Labs' state-of-the-art language models to build innovative applications 💡 Meet and learn from AI21 Labs and Lablab experts 👥 Join the community and find your team 🏆 Prizes and awards of up to $9500 API credits + $3500 cash ⭐ Receive a certificate of completion for submission 🐱‍💻 Sign up now! It's free!

Build a flawless RAG based app w/o any hallucinations by using the newest Vectara’s HHEM model and win up to $1500! 🕒 24 hours to complete the challenge 💡 Implement RAG based app by utilizing HHEM model 🤝 Take part alone or form a team with other participants 🚀 3 finalists will share the Vectara’s credits worth of $4000

🌟 Explore new AI capabilities 🤝 Collaborate and build something extraordinary. 👥 Take part alone or form a team with other participants. 🎓 Receive guidance from expert mentors. 🏆 Compete for $20,000 in Prizes. 🧑🏻‍💻 Sign up before the Kick-Off Stream to secure your spot!