Lamini: Enterprise LLM Platform for Custom AI Models

Lamini gives enterprise software teams the tools to build and control their own large language models instead of relying on generic, off-the-shelf APIs. By baking in best practices for specializing LLMs on proprietary documents, Lamini helps teams dramatically improve model accuracy, reduce hallucinations, and add citations for verifiable outputs—all critical for high-stakes business applications. The platform supports flexible deployment, whether that's on-premise, in a private cloud, or fully air-gapped, making it a strong fit for security-conscious organizations. What sets Lamini apart is its infrastructure flexibility and depth of tooling: it's the only platform built to run LLMs on AMD GPUs at scale, alongside features like Memory RAG, a Classifier Agent Toolkit, and native text-to-SQL agent building. Teams can fine-tune models on massive datasets, wire in function calling to connect LLMs to external tools and APIs, and scale confidently to thousands of GPUs without sacrificing control over their data or model behavior. From automating manual classification work to powering factual reasoning chatbots, code assistants, and customer service agents, Lamini is built for engineering-heavy teams who need production-grade reliability rather than a quick prototype. It's positioned as infrastructure for organizations that view custom LLMs as a core product capability, not a side experiment.

1. Install Lamini on-premise, in your own cloud/VPC, or use it on-demand. 2. Feed the platform your proprietary documents and datasets to specialize an LLM for your domain. 3. Use the Lamini library and built-in best practices to fine-tune the model, improving accuracy and reducing hallucinations. 4. Add capabilities like Memory RAG, classifier agents, text-to-SQL, or function calling depending on your use case. 5. Deploy and scale the tuned model securely across your infrastructure, including AMD GPU clusters if desired.