Loop Smarter’s AI optimization platform is built on LARA — a proprietary reinforcement learning framework developed by our research team at the intersection of Operations Research and Deep Learning. At its core is ARPPO, our Average Reward Proximal Policy Optimization algorithm, purpose-built for continuous operational decision-making in real business environments.
The Science: Why Average Reward Matters
Most AI optimization systems use discounted reward, which treats future results as less important than immediate gains. For real-world operations — where businesses run continuously without episode boundaries — this creates a systematic bias that misaligns the AI’s objective from your actual goal.
ARPPO optimizes for long-run average reward: the metric that actually corresponds to how operations managers measure success. The algorithm continuously estimates and tracks average operational performance, learning policies that maximize steady-state throughput — not just short-term wins. The theoretical foundation is described in Schneckenreither & Moser (2025), Neural Computing and Applications.
What the Platform Optimizes
- Inventory & Order Release — Learns when and how much to order, balancing holding costs, stockout risk, and supplier lead times
- Dynamic Pricing — Adapts prices in real time to demand elasticity, competitor moves, and margin targets
- Demand Forecasting — LSTM-based probabilistic forecasting with uncertainty quantification (know the range, not just the point estimate)
- Production Scheduling — Multi-agent scheduling for shop floors, allocating jobs across machines to maximize throughput and minimize WIP
- Recommendation Systems — Continuously adapting item recommendations optimized for conversion or engagement
Integration Options
The platform is designed to integrate with your existing systems — no replacement of ERP, WMS, or other core infrastructure required.
| Integration Type | Description | Best For |
|---|---|---|
| REST API | HTTP endpoints for real-time decision requests. Query the AI for an order quantity, price recommendation, or scheduling decision in under 100ms. | ERP integrations, pricing engines, real-time systems |
| Batch API | Upload a CSV or JSON batch of scenarios, receive optimized decisions. Async processing for overnight planning runs. | Inventory planning, demand forecasting runs |
| Python SDK | Native Python client for data science teams. Direct model access for experimentation, custom environments, and pipeline integration. | Data science workflows, model evaluation |
| Direct DB Polling | Platform reads from and writes to your database directly (SQL or NoSQL). Zero-latency integration for high-frequency decisions. | High-throughput production scheduling |
Infrastructure & Deployment
The platform runs as a set of Docker containers, making it deployable on any infrastructure — on-premise servers, private cloud, or public cloud (AWS, Azure, GCP).
Minimum Requirements
- Docker + Docker Compose (all dependencies bundled — no manual library installation)
- CPU: 4+ cores (8+ recommended for multi-agent training)
- RAM: 8 GB minimum (16 GB+ recommended)
- GPU: Optional. An NVIDIA CUDA-compatible GPU (8 GB+ VRAM) reduces training time by 5–20× for large state spaces. CPU mode is fully supported for smaller deployments and development environments.
- Storage: 20 GB for model weights, training data, and logs
GPU vs. CPU Mode
For initial deployments and smaller problem sizes (state space < 50 dimensions, single agent), CPU-only mode works well — typical training completes in hours, not days. For larger environments (multi-agent, complex state representations, LSTM-based policies), GPU acceleration is recommended. The platform detects available hardware automatically and switches modes at startup.
The 4-Step Deployment Process
- Discovery (2–3 weeks): We analyze your operational problem, define the state/action/reward structure, and assess data availability. Output: a problem specification and feasibility assessment.
- Data Pipeline (2–4 weeks): Historical data ingestion, cleaning, and feature engineering. We build the training environment and validate it against your real-world outcomes. No data leaves your infrastructure if preferred — the platform can train fully on-premise.
- Pilot — Shadow Mode (2–4 weeks): The AI agent runs in parallel with your existing system, generating decisions that are logged but not acted upon. Performance is compared against your current approach. This gives your team confidence before live deployment.
- Go-Live & Continuous Improvement: The agent takes over decision-making in the agreed scope. It continues improving as it observes more of your operational data. A monitoring dashboard tracks KPIs and flags anomalies.
Who Is This For?
The platform is designed for operational teams at mid-to-large companies who face complex, repetitive optimization decisions:
- Operations Managers: Automate reordering and inventory levels without replacing your ERP — the AI integrates with SAP, Dynamics, and custom systems via REST API or database polling.
- Supply Chain Leads: Reduce WIP, stockouts, and excess inventory simultaneously. The average reward formulation means it optimizes for annual performance, not just this week.
- Pricing Teams: Deploy adaptive pricing without a dedicated ML engineering team. The platform handles model training, deployment, and monitoring.
- Plant Managers & Facility Directors: Multi-agent scheduling across machines and shifts, with real-time adaptation to demand changes and machine availability.
Technical Stack
Built in Haskell for maximum type-safety and performance, backed by the hasktorch deep learning framework (PyTorch C++ backend). The type-safe architecture means fewer runtime errors in production compared to Python-based alternatives, and the functional design makes the system auditable — every decision can be traced back to specific model inputs.
Interested in seeing ARPPO solve your optimization problem? Get in touch to schedule a technical discovery call — we will walk through your use case and provide a concrete estimate of achievable performance improvement.