Move beyond passive analytics. Deploy Autonomous Decision Systems that optimize complex trade-offs in real-time—reducing energy costs by 25% and unplanned downtime by 50%.
For the last decade, enterprise AI has focused on forecasting—telling you demand will spike or equipment might fail. But knowing what will happen isn't enough. You need to know what to do about it. Prism Infoways bridges the gap between data and action. We engineer Reinforcement Learning (RL) agents that move beyond static rules, learning optimal strategies through millions of simulated interactions to solve your most complex, non-linear business problems.
We build high-fidelity environments (Gymnasium, AnyLogic, Omniverse) to safely train agents before they ever touch your live infrastructure.
Deploy multi-agent systems that dynamically adjust inventory, route logistics, and mitigate the "Bullwhip Effect" in real-time.
Algorithms that learn thermal inertia and machine dynamics to slash HVAC costs and optimize manufacturing dispatch.
Algo-trading and portfolio management bots that optimize for risk-adjusted returns (Sharpe Ratio) in volatile markets.
Leverage your historical logs to train agents safely using Conservative Q-Learning (CQL), ensuring zero "cold start" risks.
Complete deployment architecture using Ray and Kubernetes to scale simulation and inference seamlessly.
Traditional heuristics hit a ceiling. RL agents continually discover novel strategies, delivering 12%+ reductions in logistics costs and >100% performance gains in financial execution.
Static rules fail when the world changes. Our agents adapt to "regime changes"—from supply shocks to market volatility—without manual reprogramming.
Balance competing goals effortlessly. Optimize for profit and safety, or speed and sustainability. We engineer reward functions that align AI behavior with complex business KPIs.
Validated impact includes 30-50% reductions in unplanned downtime and 25% energy savings in smart environments.
We mathematically define your problem (State, Action, Reward) and audit your data to ensure feasibility before writing a single line of code.
We mathematically define your problem (State, Action, Reward) and audit your data to ensure feasibility before writing a single line of code.
We engineer the "Gym" environment and use Domain Randomization to train agents that are robust enough to transfer from simulation to the real world.
We engineer the "Gym" environment and use Domain Randomization to train agents that are robust enough to transfer from simulation to the real world.
The agent is deployed to production but "hand-cuffed." It makes decisions in the background, allowing us to compare its performance against your existing systems without risk.
The agent is deployed to production but "hand-cuffed." It makes decisions in the background, allowing us to compare its performance against your existing systems without risk.
Once live, the system utilizes active learning pipelines to refine its policy based on fresh real-world feedback, getting smarter every day.
Once live, the system utilizes active learning pipelines to refine its policy based on fresh real-world feedback, getting smarter every day.
Disrupt your market with "Agentic AI." Whether you are building autonomous drones or next-gen fintech apps, we provide the Ray/RLlib architecture to get you from whitepaper to MVP.
Unlock the hidden capacity in your assets. We focus on "Brownfield" integration, deploying non-intrusive agents that optimize your existing HVAC, logistics, and manufacturing grids.
Yes, because we never train on live systems. We use Offline RL to learn from historical data and Digital Twins for simulation. We only deploy agents that pass rigorous "Shadow Mode" evaluation.
Not always. While RL is data-hungry, high-fidelity Simulation can generate the billions of data points needed for training. We can build these simulators based on your known business logic.
Standard ML (Supervised Learning) predicts what will happen. RL decides what to do. It is active, not passive, optimizing for long-term rewards rather than immediate accuracy.
A Proof of Concept (POC) typically takes 8-12 weeks, moving from Problem Formulation to a trained agent in a simulated environment.
We rely on industry standards: Python, Ray (RLlib), and PyTorch. This ensures your solution is scalable, non-proprietary, and compatible with modern cloud infrastructure.
Stop reacting to the market. Start shaping it. Engineer the decision systems that optimize your enterprise 24/7.
