Loop AI

Loop AI provides an AI Control Center that lets enterprises run custom small language models (SLMs) and integrate any LLM across on‑premise, edge, or hybrid infrastructure, keeping data behind the firewall and reducing latency and cost.

Updated 2 months ago

Funding

Funding not disclosed

Funding rounds are not available yet.

Founders

Founder details are not available yet.

Product

Problem

Enterprises rely on public large language models that are costly, slow, and expose sensitive data, making them unsuitable for mission‑critical AI agents that require consistent, low‑latency outputs and strict data sovereignty.

Solution

Loop AI offers an AI Control Center that enables organizations to train and run custom Small Language Models (SLMs) on their own infrastructure, including on‑premise servers and edge devices equipped with NVIDIA GPUs. The platform provides no‑code and low‑code visual tools with granular permissions, allowing teams to prototype, test, deploy, and monitor AI agents without dedicated data‑science expertise. By supporting any LLM as a fallback, Loop AI ensures flexibility while maintaining full control over model versions and data. Local inference reduces latency and cloud‑API costs, and the custom SLMs are trained on proprietary data to deliver higher accuracy and lower hallucination rates for enterprise use cases. The solution integrates a template library of AI agent blocks, enabling rapid development and scaling of cognitive automation across various industries.

Target Audience

Primary customers are large enterprises and organizations that need secure, high‑performance AI agents—such as financial institutions, healthcare providers, and manufacturers—who must keep data behind their firewall and minimize latency.

Features

No‑code and low‑code visual development environment with role‑based permission controls
Ability to train custom Small Language Models on proprietary data without requiring data scientists
On‑premise and edge deployment using NVIDIA GPUs for low‑latency, cost‑effective inference
Support for integrating any external LLM as a fallback, ensuring zero vendor lock‑in
Continuous, unsupervised learning from unlabeled data across any language or domain
Enterprise‑grade accuracy with reduced hallucination rates for mission‑critical agents
Template library of AI agent blocks for rapid prototyping, testing, and monitoring