Lamini

Lamini - Enterprise LLM Platform is an enterprise-grade platform designed to build high-accuracy LLM-powered agents and tools. It focuses on reducing hallucinations, enabling memory-based RAG, and providing scalable classification and function-calling capabilities to connect with external tools and APIs. Lamini targets production-grade AI applications with strong emphasis on factual accuracy, latency, and cost efficiency. It also offers documentation, tutorials, and a rich set of guides for implementation and integration.

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How to Use Lamini

1. Choose a product or toolkit (Memory RAG, Classifier Agent Toolkit, or Text-to-SQL).
2. Configure your data sources and targets (enterprise data, documents, or external APIs).
3. Build mini-agents or pipelines by composing memory, retrieval, and tool-calling components.
4. Deploy on-premise, air-gapped, or VPC to keep data private; leverage embed-time compute for faster retrieval.
5. Tune and monitor accuracy, latency, and cost; iterate with provided demos, docs, and support.

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Core Features

• Memory RAG: high-accuracy retrieval-augmented generation with embed-time compute
• Memory-based mini-agents: deploy many high-accuracy agents in parallel for workflow composition
• Text-to-SQL: build highly accurate text-to-SQL agents for business analysis
• Classifier Agent Toolkit: scalable, accurate LLM-based data classification with configurable categories
• Function Calling: connect to external tools and APIs to extend capabilities
• High-accuracy tuning: memory tuning and optional fine-tuning to reduce hallucinations
• Enterprise-friendly deployment: on-premise, air-gapped, or VPC deployments
• 100% accuracy and large time savings claims backed by case studies and benchmarks

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Use Cases

• Factual reasoning and enterprise chatbots with high accuracy
• Automated data classification and routing for customer support and internal workflows
• Text-to-SQL for business analytics and ad-hoc querying
• Smart assistants that call external tools and APIs to perform actions
• Building scalable, accurate LLM-powered agents for complex enterprise tasks

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How It Works

• Provide data sources (documents, databases, APIs) and define targets for your agents
• Memory RAG layers integrate embeddings and fast retrieval to supply accurate context
• Function Calling enables agents to execute actions against external tools or APIs
• Agents can be deployed in parallel and composed into complex workflows
• Lamini emphasizes privacy and security with deployment options that keep data in private environments

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Safety and Best Practices

• Aim for production-grade accuracy with memory RAG and tuning options
• Use on-premise or private deployments where data sensitivity is high
• Validate outputs in controlled environments before public release

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Pricing & Resources

• Free credits for getting started
• Documentation, guides, video tutorials, and blogs for ongoing learning
• Support channels for bug reports, feature requests, and feedback

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Core Features

• Memory RAG: high-accuracy mini-agents with embed-time compute to improve retrieval quality
• Classifier Agent Toolkit: scalable, accurate LLM-based classification across many categories
• Text-to-SQL: bridge natural language queries to precise SQL
• Function Calling: seamless integration with external tools and APIs
• Deployment options: on-premise, air-gapped, or VPC for data privacy
• High-accuracy tuning: reduces hallucinations and improves latency-cost balance