Lamini Platform
Build highly accurate mini-agents, reduce LLM hallucinations by 95%
Get started on the Lamini platform with $300 free credits.
Our products
Most accurate and efficient fine-tuning
Memory Tuning
Offers the highest level of accuracy while keeping inference latency and cost low.
10k
User-tuned
models
models
95%
Accuracy on tuned models
Build RAG mini-agents
Memory RAG
Tired of complex RAG systems that fail to deliver? Memory RAG boosts accuracy while keeping things simple.
Achieve 90-95% accuracy with Memory RAG compared to RAG on GPT4.
Leverage test-time compute during embedding to create more intelligent, validated data representations.
Automated high-quality inputs means faster, more cost-efficient retrieval.
Deploy many high-accuracy mini-agents in parallel that can be composed into agentic workflows.
High accuracy classification
Classifier Agent Toolkit
Replace manual data labeling with our highly scalable and accurate LLM-based classifier.
Classify large amounts of unstructured data with any number of categories.
Classify customer service requests based on intent and route to appropriate departments.
Triage code for legacy applications.
Use Cases
Unlock the highest value use cases
Running an LLM application in production can be risky. We help enterprises deliver high accuracy LLMs and agents to reduce risk.
Factual Reasoning
Turn documentation into intelligent chat bots for your customers and teams.
Classification
Make unstructured data work for you by automating manual classification tasks.
Text-to-SQL
Give your teams the tools to do their own business analysis.
Code Assistant
Give a niche programming language some love with its very own assistant.
Customer Service Agent
Scale your customer support and give reps time back to answer the tough calls.
Function Calling
Increase productivity and help your teams find the answers they need fast.
Trusted by Fortune 500 and leading startups
100%
Accuracy for content classification
1200+h
Of manual work saved annually
"Lamini's classifier SDK is easy to use... Once [the tuned LLM] was ready, we tested it, and it was so easy to deploy to production. It allowed us to move really rapidly.”
Chris Lu
CTO
94.7%
Accuracy for text-to-SQL
100+h
of engineering time saved
Unlike sklearn, finetuning doesn’t have a lot of docs or best practices. It's a lot of trial and error, so it takes weeks to finetune a model. With Lamini, I was shocked — it was 2 hours.
Engineering leader
CTO
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