Composabl Multi-Agent Industrial AI Pilot Offer - Quick Start
by Composabl
2-3 month multi-agent industrial AI pilot
Transform complex industrial operations with an end-to-end SaaS platform built to design, orchestrate, train, benchmark, and deploy multi-agent AI systems tailored for industrial environments.
Built by engineers for engineers
Rapidly deploy intelligent agents that drive real-world automation, improve efficiency, and preserve expert knowledge. Built by engineers for engineers, Composabl is modular, explainable, and designed to scale industrial operations through AI-powered decision-making. With our powerful Orchestration Studio, you can seamlessly build and operationalize multi-agent AI systems that integrate directly into your existing operational landscape. Whether you’re working with mathematical algorithms, RPA tools, or diverse data systems, enhance your stack by embedding decision intelligence at every layer. Unlock the full potential of advanced AI technologies—including deep reinforcement learning (DRL), machine learning (ML), and our machine teaching methodology—to drive real-time decision-making and intelligent automation.
WHAT THIS OFFER ENTAILS
- Two Composabl Platform seats (includes no-code UI & SDK)
- One agent deployment
- Selecting a use case
- Creating a simulation
- Building and training agents
- Deploying the top-performing agent into production
Industry-Proven Results
We have the experience. CEO & Founder, Kence Anderson, and his team have been building AI systems for Fortune 500 industrials since 2017
- Glass Bottle Manufacturing Automated a process previously untouched by AI, creating an agent that mirrors expert operator logic and delivers recommendations through the HMI
- Logistics Scheduling A Fortune 50 logistics company replaced fragile, human-dependent scheduling systems with a reliable multi-agent solution that maintains throughput even during staffing gaps
- Oil & Gas A Fortune 10 energy giant chose Composabl to develop autonomous agents for a Virtual Operator Program spanning hundreds of real-world use cases
- Aerospace A global aircraft manufacturer leveraged Composabl’s orchestration tools to simulate passenger population dynamics—something previous internal efforts failed to achieve
Key Platform Capabilities
1. No-Code Agent Orchestration Studio Composabl’s studio environment enables teams to build, train, benchmark, and deploy agents using a no-code interface. Engineers without programming backgrounds can easily create and orchestrate agent systems that replicate high-value industrial skills.
2. SDK for Advanced Integrations For advanced users, Composabl offers a robust Python SDK that integrates any model - LLMs, reinforcement learning agents, proprietary algorithms - into orchestrated agentic systems. This provides unmatched flexibility for AI R&D and enterprise-scale deployment.
3. Real-Time Physical Deployment Unlike traditional AI that’s limited to dashboards, language based only functionality or offline models, Composabl is designed for real-time operation in the physical world. Its agents connect to control systems, factory floor equipment, and interfaces like SCADA, MES, HMI, and ERP.
4. Simulation-Based Machine Teaching Before going live, agents are trained in realistic simulations to test and refine performance. Using Machine Teaching, expert operators can “teach” the AI by embedding their experience into agent behaviour, ensuring safe, efficient, and expert-aligned outcomes.
Most industrial AI solutions are either narrow (e.g., IIoT dashboards, predictive analytics) or too rigid (monolithic models that can’t adapt). Composabl breaks the mould by enabling real-time autonomous decision-making through multi-agent systems that are:
- Modular Like a machine, built from interchangeable, upgradable parts
- Adaptable Agents learn and evolve based on changing operational conditions
- Explainable Every decision is traceable to a designed agent and behaviour model
- Human-Aligned Preserves and scales human expertise through intuitive interfaces
Common Use Cases
At a glance
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