Loop AI Group
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About
Loop AI Group provides an enterprise AI control center designed to help organizations move beyond generic, commodity LLMs. The platform revolves around two main components: Loop Q, a cognitive platform for training custom Small Language Models (SLMs) on proprietary data, and Loop AI Agents Orchestra, a visual management tool for the full agent lifecycle. By focusing on SLMs, the tool aims to deliver higher accuracy and lower latency for specific business tasks compared to larger, more expensive general-purpose models. The system is designed to turn proprietary company knowledge into autonomous agents that can perform real jobs within an organization's existing structure. The platform features a low-code visual interface that allows teams to build and orchestrate AI agents without requiring a large team of data scientists. Users can leverage a template library of AI agent blocks to quickly prototype and deploy workflows. A key functional advantage is the ability to benchmark model performance against cost in real-time, allowing organizations to hot-swap between custom SLMs and commercial LLMs as fallbacks. Crucially, these agents are capable of integrating with legacy enterprise systems through either standard APIs or the same user interfaces human employees use, which allows for automation in environments where modern integrations are not available. This solution is primarily designed for large enterprises in high-stakes industries such as banking, insurance, healthcare, and telecommunications. Because the platform supports on-premises and edge deployment using NVIDIA GPUs, it is particularly suited for organizations with strict security and compliance requirements who must maintain absolute sovereignty over their data. It caters to IT and operations teams who need to scale AI across the workforce while ensuring that their models are trained on specific, relevant historical data rather than generic public datasets. What sets Loop AI apart is its extensive history in the field, having pioneered unsupervised deep learning long before the current mainstream AI boom. Unlike many competitors that rely on RAG (Retrieval-Augmented Generation) or generic model wrappers, Loop focuses on creating independent, client-trained SLMs that can run locally. Their recent expansion into quantum-first principles through Loop Quantum AI Labs further distinguishes them, as they aim to solve complex modeling challenges that exceed the capabilities of classical neural networks. This combination of established enterprise reliability and forward-looking quantum research makes it a unique player in the agentic AI space.
Pros & Cons
Supports on-premises and edge deployment for complete data sovereignty within the corporate firewall.
Enables training of custom Small Language Models without requiring dedicated data science expertise.
Visual low-code interface allows teams to manage the full agent lifecycle from prototyping to deployment.
Provides zero vendor lock-in through the ability to hot-swap between different AI providers instantly.
Proven performance at scale with a history of processing millions of tasks for Fortune 100 companies.
Pricing information is not publicly available and requires a custom demo request for quotes.
On-premises training and low-latency inference require specific NVIDIA GPU hardware to function.
Effectiveness is highly dependent on the availability of high-quality proprietary historical data for training.
Use Cases
Banking operations managers can deploy autonomous agents to handle complex knowledge-based tasks while keeping sensitive financial data strictly on-premises.
Healthcare providers can create specialized Small Language Models trained on clinical data to improve accuracy in patient care without using external cloud APIs.
Insurance claims adjusters can use AI agents to navigate legacy software systems and process documentation using the same interfaces as human workers.
IT directors in telecommunications can benchmark various LLMs against custom models to optimize the cost-to-performance ratio for automated support agents.
Platform
Task
Features
• on-premises deployment
• multi-llm support
• legacy system integration
• real-time performance benchmarking
• edge computing inference
• low-code visual tools
• loop ai agents orchestra
• custom slm training
FAQs
Can I use my own data to train models?
Yes, Loop Q allows you to train custom Small Language Models on your proprietary data using unsupervised learning. This ensures the models understand your specific business context without the need for manual data labeling.
Where is the data processed?
The platform supports deployment on-premises or at the edge using NVIDIA GPUs. This keeps all sensitive enterprise data within your own firewall, ensuring compliance and security for restricted industries.
Do I need data scientists to use this tool?
No, the platform is designed with low-code and no-code visual tools specifically for business teams. You can build, test, and deploy AI agents using a template library and drag-and-drop interfaces.
How does it handle vendor lock-in?
Loop AI offers zero vendor lock-in by allowing you to integrate any open-source or commercial LLM. You can use these as fallbacks or benchmark them against your custom models in real-time to optimize costs.
Can agents work with older legacy software?
Yes, the agents are built to integrate with legacy systems using either APIs or the existing graphical user interfaces used by your staff. This allows for automation without requiring a complete infrastructure upgrade.
Pricing Plans
Enterprise
Unknown Price• Custom SLM training
• Loop AI Agents Orchestra
• On-premises deployment
• Legacy system integration
• Multi-LLM support
• Real-time benchmarking
• Visual low-code tools
• Edge inference
Job Opportunities
Loop Quantum AI Labs | INTERNSHIP | On-Site USA
Deploy autonomous AI agents using custom Small Language Models trained on proprietary data to ensure enterprise-grade accuracy, security, and data sovereignty.
Other Requirements:
Exceptional abilities and the drive to make an impact
Responsibilities:
Build Quantum-Native AI to simulate complex systems
Tackle the intractability of complex chaos
Bridge causal gaps in Quantum Foundations: Adiabatic Annealing, Control Theory, & Error Correction
Develop Quantum-ML Hybrids and High-Performance Research Engineering
Research Neural Topology, Theoretical Neuroscience, and Neuromorphic Engineering
Show more details
Loop Quantum AI Labs | INTERNSHIP | On-Site Europe
Deploy autonomous AI agents using custom Small Language Models trained on proprietary data to ensure enterprise-grade accuracy, security, and data sovereignty.
Other Requirements:
Exceptional abilities and the drive to make an impact
Responsibilities:
Build Quantum-Native AI to simulate complex systems
Tackle the intractability of complex chaos
Bridge causal gaps in Quantum Foundations: Adiabatic Annealing, Control Theory, & Error Correction
Develop Quantum-ML Hybrids and High-Performance Research Engineering
Research Neural Topology, Theoretical Neuroscience, and Neuromorphic Engineering
Show more details
Loop Quantum AI Labs | INTERNSHIP | REMOTE
Deploy autonomous AI agents using custom Small Language Models trained on proprietary data to ensure enterprise-grade accuracy, security, and data sovereignty.
Other Requirements:
Exceptional abilities and the drive to make an impact
Responsibilities:
Build Quantum-Native AI to simulate complex systems
Tackle the intractability of complex chaos
Bridge causal gaps in Quantum Foundations: Adiabatic Annealing, Control Theory, & Error Correction
Develop Quantum-ML Hybrids and High-Performance Research Engineering
Research Neural Topology, Theoretical Neuroscience, and Neuromorphic Engineering
Show more details
Ratings & Reviews
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