I designed a professional and investor-ready pitch deck for LLMWare AI, an innovative artificial intelligence platform focused on running Small Language Models (SLMs) and AI agents directly on local machines. The presentation deck highlights LLMWare AI’s advanced capabilities in RAG (Retrieval-Augmented Generation), on-device AI deployment, and its library of 100+ quantized language models optimized for Windows environments.

The pitch deck clearly communicates how LLMWare AI enables secure, efficient, and scalable AI workflows without relying on cloud infrastructure. With a clean, modern design and structured storytelling, the AI presentation positions LLMWare AI as a powerful solution for enterprises, developers, and organizations seeking high-performance local AI execution.

Varying Sizes, Uses

Optimization, RAG, Agents

x86, ARM CPU, GPU, NPU

First-in-kind cross-platform (ARM, x86) optimization system support for CPU, GPU and NPU

AI Explainability, Model Safety, Compliance & Audit

AI Agent orchestration engine & RAG with Parser and Data Ingestion Pipeline

GitHub Accelerator (1 of 10 projects chosen for open-source AI)

Partnerships: Intel, Dell, Qualcomm, IBM, Capgemini, SAS

Featured by Intel at CES 2025 & World Economic Forum (Davos), Dell at Tech Forum/Press Tour, Microsoft AI Tour

Lead Generation/Funnel, Pioneer in Small Language Models, Grow community for increased brand awareness

Co-Sell with partners - Intel, Qualcomm, Dell, SAS, Channel partnerships in Europe and S. America, Heavy enterprise sales & marketing support/referrals by Intel and Qualcomm, Integration into OEM laptops

Initial focus on Regulated Industries: Financial Svcs, Insurance & Pharma, Lighthouse Customer Pipeline

No Enterprise-critical features like AI Explainabiliity, Model Safety and Security monitors, Audit & Compliance

Little to no support for local AI Agent development and the deployment

No end-to-end framework w/ proprietary models & data ingestion pipeline and cross-platform model optimization

Up to 30x FASTER: vs. running GGUF or Pytorch (Mistral 7B)

22 Billion vs. 3 Billion: Max model size using LLMWare (vs. >3B Crashes/Unusably slow)

Small Language Models and Framework for Enterprise RAG and AI Agents