Train Custom Small Language Models on Your Data
Loop Q is the enterprise platform for training custom Small Language Models (SLMs) on your proprietary data. Integrated with Loop AI Agents Orchestra, it delivers local inference on-premises or at the edge using NVIDIA GPUs—maximizing precision while minimizing costs and hallucinations for agentic use cases. Any enterprise engineer can train SLMs without deep expertise in model architecture, training infrastructure, or data pipelines.
Designed using principles of neuroscience and cognitive science, Loop Q has human-capacity abilities to autonomously connect content and context, unlocking the transformative power of your enterprise data. These insights power cognitive robotic process automation, cognitive software robots, and other cognitively-enabled applications.
Full Enterprise Sovereignty Over AI
While generic public LLMs are ubiquitous, they're often too slow and risky for real enterprise agents. Loop Q empowers clients to train and run multimodal Small Language Models on their own servers, delivering the high and consistent accuracy required for enterprise use—exemplified by an AI agent that has autonomously processed millions of dollars in financial transactions for a Fortune 100 bank over several years with no issues.
Key Differentiators
TRAIN YOUR OWN SLMs. NO DATA SCIENTISTS REQUIRED. ON-PREMISES OR AT THE EDGE.
Train custom Small Language Models specific to your agentic use cases—most vendors just fine-tune or resell generic models. Loop Q lets you build proprietary models focused on your AI agent workflows.
UNSUPERVISED LEARNING
UNSUPERVISED LEARNING
Loop Q learns in any language from unlabeled data without human guidance
Our proprietary algorithm uses deep learning and classical symbolic reasoning to allow Loop Q to learn in any human or machine language without requiring expensive and time-intensive, human-generated labels, dictionaries or grammar.
HIGH ACCURACY
ANY INDUSTRY
ANY LANGUAGE
HIGH ACCURACY
ANY INDUSTRY
ANY LANGUAGE
High accuracy: Custom SLMs are trained from scratch on your proprietary data in any language, creating an exclusive model containing only the language and context of your specific use cases—delivering precision that generic LLMs cannot match.
NATURAL LANGUAGE PROCESSING
NATURAL LANGUAGE PROCESSING
Loop Q learns any language in a matter of minutes
Already fluent in 15+ languages, Loop Q learns human language directly from data, just like native human speakers. An immediate advantage of the dictionary-free technology is its intrinsic understanding of slang, industry jargon, and abbreviations. Even typos and grammar mistakes make it smarter.
CONTINUOUS + AUTOMATIC LEARNING
CONTINUOUS + AUTOMATIC LEARNING
Loop Q achieves deep understanding of data across any specific data domain
As new data becomes available, the model is automatically updated without human supervision to provide the highest level of accuracy.
LOW-LATENCY EDGE INFERENCE
Run SLMs on-premises or at the edge with NVIDIA GPUs. Local inference delivers maximum precision with minimal latency, keeping your data within your firewall while reducing costs compared to cloud-based LLM API calls.
ENTERPRISE-GRADE ACCURACY
SLMs deliver the consistent accuracy and lower hallucination rates enterprises actually need. Custom-trained SLMs power agentic applications with the reliability required for mission-critical operations—avoiding the hallucinations and inconsistencies common with generic large language models.
How It Works
Loop Q enables enterprises to train custom Small Language Models directly on their source data. Combined with Loop AI Agents Orchestra, you can train SLMs, hot-swap them instantly, or complement them with any LLM of your choice when necessary—maintaining full control with zero vendor lock-in.
Your enterprise has produced data for years, but only a small percent of it (the "structured" part) could be understood by computers without human guidance. Now, Loop Q can take you beyond that tip of the iceberg and understand 100% of your data.
Beyond the Black Box Hype
General-purpose Large Language Models, despite reaching commodity status, still incur high costs when applied to real-world agentic applications. Loop Q moves beyond generic models with custom-trained SLMs that give enterprises full sovereignty over AI agents integrated into their organizational structures. By decoupling the AI model from the application logic, Loop empowers organizations to navigate continuous changes in AI model performance with confidence.