Lamini is an Enterprise AI platform that enables organizations to develop their own Expert AI models using large-scale language model (LLM) fine-tuning and memory tuning techniques. This technology reduces hallucinations to 95% accuracy and enhances data retrieval from enterprise sources, addressing the need for reliable and efficient AI-driven insights.
Funding
$24.8M raised to dateRaised to date based on public sources. This may differ from the amount the company actually raised and is based only on what is publicly available on the internet.
Founders
Product
Problem
General-purpose large language models (LLMs) often lack the specific knowledge and accuracy required for enterprise applications, leading to unreliable outputs and hallucinations when dealing with proprietary data. Fine-tuning LLMs for specific tasks can be complex, time-consuming, and may not guarantee the desired level of accuracy or security.
Solution
Lamini offers an enterprise AI platform that enables organizations to develop custom, high-accuracy AI models tailored to their specific data and use cases. The platform leverages large-scale language model (LLM) fine-tuning and memory tuning techniques to significantly reduce hallucinations and improve data retrieval from enterprise sources. By training open-source models on proprietary data, businesses can build smaller, faster, and more accurate agents that deliver reliable insights. Lamini's platform is designed for developers, automating and scaling MLOps processes with familiar development patterns, and can be deployed securely in various environments, including VPC, on-premise, and air-gapped setups.
Target Audience
Lamini targets Fortune 500 companies and leading startups seeking to build custom AI models with high accuracy and security for specific enterprise use cases, such as text-to-SQL, classification, and document reasoning.
Features
- LLM fine-tuning and memory tuning for enhanced accuracy and reduced hallucinations
- Support for building text-to-SQL agents with accuracy exceeding 95%
- Classifier Agent Toolkit for large-scale classification tasks
- Memory RAG for improved document reasoning and factually accurate responses
- Capability to fine-tune small language models (SLMs) for efficient function calling
- Secure deployment options, including VPC, on-premise, and air-gapped environments
- Developer-friendly SDK for intuitive and automated MLOps