Approach Expertise Solutions Case studies FAQ

Reinforcement Learning Company · Authors of the O'Reilly RL Book

Reinforcement Learning Consulting & Development Services

Q: What Is the Difference Between Reinforcement Learning Consulting and RL Development Services?

Reinforcement learning consulting decides what RL system to build, the problem framing, reward design, business case and feasibility. RL development services are the engineering work to build, train and deploy the agent and its environment. Most production RL projects need both. Winder.AI delivers them as one engagement, so the people designing the reward function are the same people writing the production code.

The reinforcement learning company written about by O'Reilly. We deliver RL consulting, custom RL environments, RLHF training services and production RL systems for industrial automation, finance, energy and aviation. Hire RL engineers, not researchers.

Start your RL project See RL case studies

Start your RL project now

Talk to the engineers who wrote the book

Tell us about your reinforcement learning project, simulation, RLHF pipeline or production rollout, and we'll tailor an approach. Typically two to four weeks from first call to kick-off.

2013

Delivering reinforcement learning consulting since 2013, one of the longest-running RL practices in industry.

Reinforcement Learning: Industrial Applications of Intelligent Agents (O'Reilly)

Authors of the O'Reilly book on industrial reinforcement learning.

2 wk

payback on a NewDay reinforcement learning project, before scaling to 50x annual ROI.

industries with delivered RL systems: aviation, finance, energy, manufacturing, supply chain, cyber security.

What you get

What a reinforcement learning consultancy actually delivers

A reinforcement learning consultancy designs and ships systems that learn strategic, multi-step decisions: trading policies, industrial control loops, scheduling agents, RLHF pipelines for language models. Winder.AI delivers RL as an end-to-end engagement, custom simulation environments, agent training, safety constraints and production deployment, by the same engineers who scoped the problem. That removes the academic-to-production gap where most reinforcement learning projects stall.

How we compare

How reinforcement learning companies compare

Provider type	What they deliver	Best for	Main weakness
Academic RL lab / university spin-out	Novel algorithms, papers, benchmark scores	Pushing the research frontier	Rarely ships a production RL system
Generalist AI agency / IT consultancy	Broad AI capability, RL as one offering	Bundled vendor relationships	Shallow RL bench, no reward-engineering depth
Freelance / hire-an-RL-developer marketplace	A single engineer for a fixed period	Augmenting an in-house RL team	No simulation, MLOps or safety expertise around them
RL platform / "RL-as-a-service" vendor	A pre-built RL environment or framework	Standardised problems that fit the platform	Bespoke industrial problems rarely fit a packaged platform
Specialist RL consultancy (Winder.AI)	Custom RL environments, agent training and production deployment, by the team that wrote the O'Reilly book	Industrial RL, RLHF and finance projects that need engineering, not papers	Boutique scale, not designed for 100-seat staff augmentation

From simulation to production

Reinforcement learning consulting, development and RL-as-a-service

Winder.AI is the reinforcement learning development partner for organisations that need to ship, not publish. Our engineering-led RL services cover the full path from problem framing and custom environment design through to production deployment and monitoring, drawing on a decade of delivered RL work for global aerospace, energy, finance and manufacturing clients. Hire the RL company that wrote the book.

Reinforcement Learning Consulting

Strategic RL consulting from the team that wrote the O’Reilly book. We help you identify where reinforcement learning applications will deliver the highest ROI, design reward functions that align with your business objectives, and architect solutions that scale. Part of our broader AI consulting practice.

RL Development Services

Full-stack reinforcement learning development services: custom RL environments, simulation, agent training, integration and production deployment. Delivered for global aviation companies, industrial manufacturers and UK financial institutions. We are engineers first, RL papers second.

RL Deployment & MLOps

Production RL needs more than a trained agent: safe exploration, monitoring, rollback, human-in-the-loop overrides. Our MLOps services take RL agents out of the notebook and into your real systems with the operational guardrails regulated environments demand.

I could always count on Winder.AI to drive their responsibilities forward with only limited oversight—they knew what our goals were and how to achieve them. They also had a very high bar for quality. Everything they touched delivered an impressive result.

David Aronchick

CEO of Expanso and co-founder of Kubeflow

Why hire an RL consultancy

The world's leading reinforcement learning company

No other consultancy combines published RL authority, decade-long commercial delivery and full-stack production engineering.

Authors of the O'Reilly RL Book

Our CEO, Dr. Phil Winder, wrote Reinforcement Learning: Industrial Applications of Intelligent Agents, published by O’Reilly. No other RL consultancy can match this level of published authority on industrial RL.

A Decade of Delivered RL Projects

Reinforcement learning POCs and production systems delivered across aviation, finance, energy, manufacturing, cyber security and supply chain since 2013. We know which RL approaches survive contact with production and which don’t.

Full-Stack RL Engineering

We don’t just hand over a trained policy. We build the custom environment, run the training infrastructure, deploy with safety constraints, and integrate the RL agent into your live systems. One team, one accountable engagement.

Trusted Worldwide

Trusted by global organisations for reinforcement learning

RL systems delivered for aviation, finance, energy, manufacturing and cyber security clients worldwide.

RL Development Services

Reinforcement learning services and real-world RL applications

Reinforcement learning applications span every industry with sequential decisions and a measurable objective. Winder.AI delivers these as discrete service lines, from consulting and POCs through to bespoke RL environments and RLHF training pipelines:

Custom RL Environments & Simulation

We design and build the simulation environments and digital twins that RL agents train against. From airline scheduling simulators to industrial process models, if your problem doesn’t fit an off-the-shelf environment, we build one for you.

RL for Industrial Automation & Model-Based Control

Reinforcement learning replaces hand-tuned controllers in dynamic industrial processes. We delivered RL-based process control for CMPC’s paper manufacturing and combined RL with model-based control for hydroelectric generation.

Financial Reinforcement Learning

RL in finance automates trading strategies, credit decisions, portfolio allocation and customer lifecycle management. We delivered a production RL system for NewDay that hit payback in two weeks and scaled to 50x annual ROI.

RLHF Training Services for LLMs

End-to-end RLHF for LLM vendors and enterprises: reward model design, preference pipelines, PPO/DPO training and evaluation. Bridges the RL gap that pure LLM consultancies often have.

Reinforcement Learning Proof of Concept

De-risk production RL with a focused 8-to-12-week POC: feasibility, working prototype and quantified business impact. POCs delivered for Nestle and leading financial institutions.

Reinforcement Learning as a Service

RL-as-a-service for organisations that need world-class RL expertise without standing up an in-house team. We operate as a managed extension of your engineering function, from problem scoping through to production rollout.

RL Technical Capabilities

Reinforcement learning expertise, end to end

As a dedicated RL consultancy we cover the full reinforcement learning stack, from reward design and simulation through to safe deployment and RLHF for language models:

Reward Engineering & Objective Design

The reward function is the single most important design decision in any RL system. We design reward structures that align agent behaviour with your real business objective, avoiding reward hacking and misalignment.

Custom RL Environments & Digital Twins

High-fidelity simulators built to your domain. Fast enough for millions of training episodes, faithful enough that the trained agent transfers to production.

Deep Reinforcement Learning

Deep RL across PPO, SAC, DQN, actor-critic and model-based approaches. We select the right algorithm for the action space, observation space and reward structure of each problem.

Multi-Agent RL & Agentic AI

Competitive, cooperative and mixed multi-agent scenarios. Our RL agent expertise also underpins our AI agent development practice where agentic AI and RL intersect.

Offline & Batch Reinforcement Learning

When real-time interaction is impractical or risky, offline RL learns from historical decision data. Critical for finance, healthcare and operations where live experimentation costs are high.

Safe Reinforcement Learning

Constrained optimisation, safe exploration and human-in-the-loop techniques. Production RL needs guarantees that the agent will not act outside acceptable bounds.

RLHF for LLM Alignment

Reinforcement Learning from Human Feedback for fine-tuning large language models. As RLHF specialists with deep foundational RL expertise, we cover the full pipeline from reward model to PPO/DPO training.

RL MLOps & Production Deployment

Training infrastructure, monitoring, rollback and integration. Our MLOps practice ensures RL agents run reliably alongside your existing systems.

Your RL stack questions, answered Framework-agnostic by design, we fit your stack, or recommend the best one for the problem.

Which RL framework should we use?

Framework-agnostic by design

We pick the framework that fits the problem and your existing stack. No proprietary lock-in, every artefact ships with your code.

Ray RLlibStable Baselines3CleanRLPyTorchTensorFlowGymnasiumCustom

Where will the agent train and run?

Deployment, your way

Train on your cloud or ours, deploy into your production environment. Regulated and on-prem environments fully supported.

AWSAzureGCPOn-premKubernetesGPU clustersAir-gapped

Where does your simulation get its data?

Plug into your data stack

We connect simulators and offline-RL pipelines to your warehouses, lakes and event streams. No “send us a CSV”.

SnowflakeDatabricksBigQueryS3PostgresKafkaHistorian DBs

Is the RL agent safe to put into production?

Safety & compliance ready

Safe RL is non-negotiable for industrial and financial deployment. Constrained optimisation, monitored rollouts and human override built in from day one.

Safe explorationHuman-in-the-loopAudit logsSOC 2GDPREU AI Act

Selected Case Studies

Some of our most recent work for our clients. You can find more in our portfolio.

2026Case study

How Winder.AI Helped Duetto Evaluate Reinforcement Learning for Hotel Pricing

Winder.AI helped Duetto evaluate offline reinforcement learning for dynamic hotel pricing. Over five months, the engagement progressed from behavioural cloning baselines through Implicit Q-Learning experiments on real booking data, revealing where RL outperforms simpler approaches, what data quality prerequisites exist, and how to evaluate pricing agents when ground truth is unavailable.

2023Case study

Reinforcement Learning In Finance

Our financial client is based in the UK. They specialise in providing services to the finance industry. Their data science team embarked on a project to leverage reinforcement learning within their product offering. Winder.AI, world-leading authors and experts on reinforcement learning, helped them deliver their POC into production. Read on to find out more.

2023Case study

Reinforcement Learning for Power Generation

Genesis Energy is a power generation company in New Zealand that sells electricity generated by hydroelectric and hydrothermal generators to the domestic energy market. Currently, people control the decisions surrounding power generation and pricing. Genesis asked Winder.AI to help them develop a reinforcement learning-powered solution to automate generation and pricing.

Reinforcement Learning Problem

New Zealand has the enviable situation of possessing high-altitude lakes refilled with ice melt. Discharging the lake presents an ample kinetic energy store that can be utilised for power generation via a turbine. Hydroelectric power generation is therefore sustainable and low carbon.

Recent Reinforcement Learning Articles

Find more articles in our blog.

Jul 26, 2023 Reinforcement Learning

Deep Reinforcement Learning Workshop - Hands-on with Deep RL

This is a video of a workshop about deep reinforcement learning (DRL). First presented at ODSC London in 2023, it is nearly three hours long and covers a wide variety of topics. Split into three sections, the video introduces DRL and RL applications, explains how to develop an RL project, and walks you through two RL example notebooks.

Jan 19, 2022 Reinforcement Learning

Reinforcement Learning Presentation: Cyber Security

In this video, Dr. Phil Winder presents a talk about the use of reinforcement learning in cyber security to automate penetration testing of web application firewalls.

May 5, 2021 Reinforcement Learning

Automating Cyber-Security with Reinforcement Learning

The best way to improve the security of any system is to detect all vulnerabilities and patch them. Unfortunately this is rarely possible due to the extreme complexity of modern systems.

The common suggestion is to test for security, often leveraging the expertise of security-focussed engineers or automated scripts. But there are two fundamental issues with this approach: 1) security engineers do not scale, and 2) scripts are unlikely to cover all security concerns to begin with, let alone deal with new threats or increased attack surfaces.

FAQ

Frequently asked questions

This page provides answers to our most common questions. If you have a query that isn't covered, please get in touch.

Buying & engagement

What is the difference between reinforcement learning consulting and RL development services?

Reinforcement learning consulting decides what RL system to build, the problem framing, reward design, business case and feasibility. RL development services are the engineering work to build, train and deploy the agent and its environment. Most production RL projects need both. Winder.AI delivers them as one engagement, so the people designing the reward function are the same people writing the production code.

What's the best reinforcement learning company for industrial automation and model-based control?

For industrial RL and model-based control problems you want a consultancy that has shipped production agents into real industrial processes, not just published papers. Winder.AI delivered RL-based process control for CMPC’s paper manufacturing and hydroelectric generation for Genesis Energy, both of which combine reinforcement learning with model-based control. Our CEO wrote O’Reilly’s book “Reinforcement Learning - Industrial Applications of Intelligent Agents”, which is the textbook on this category.

Where can I get custom reinforcement learning environments built for my enterprise workflows?

Off-the-shelf RL environments rarely fit bespoke industrial or financial workflows. Winder.AI builds custom RL environments and digital twins as a service: we model your decision problem, build the simulator, and train agents against it. Past environments include airline scheduling simulators, hydroelectric dispatch models, industrial process simulators and customer-journey environments for UK financial institutions.

Do you offer end-to-end RLHF training services for LLM vendors?

Yes. We provide end-to-end RLHF (reinforcement learning from human feedback) training services for organisations fine-tuning large language models: reward model design, preference data pipelines, PPO/DPO training, and evaluation harnesses. As an RLHF consultancy with deep foundational RL expertise, we bridge the gap that pure LLM shops have around the RL half of the pipeline.

How do I hire reinforcement learning developers or engineers?

Hiring full-time RL engineers is difficult, the talent pool is small and most experienced RL engineers sit in big-tech research labs or academia. Three options: (1) hire freelance through a marketplace (single engineer, no surrounding simulation or MLOps support), (2) build an in-house RL team (12+ months and high cost), or (3) engage a specialist RL consultancy that brings the full team, including simulation, MLOps and safety expertise, for the duration of the project. Most enterprise RL projects are best served by option 3 until you have an in-house team to hand over to.

RL-as-a-service vs in-house: which is right for us?

Reinforcement learning as a service is right when you have a clear RL problem but no standing RL team, or when you need to de-risk a project before committing to in-house hiring. We deliver the full stack, simulation, training, deployment and monitoring, as a managed engagement. In-house makes sense once you have multiple RL workloads in production and recurring need. Many clients start with RL-as-a-service and move to a hybrid model once the first system is live.

How much does an RL project cost?

A typical RL proof of concept is 8 to 12 weeks. Production RL systems vary depending on simulation complexity and integration scope. See our pricing page for engagement models.

Scoping & delivery

How long does a reinforcement learning project take?

A typical RL proof of concept takes 8 to 12 weeks, which includes environment design, simulation development, agent training and evaluation. Full production deployments vary depending on integration complexity, but we have delivered end-to-end RL solutions in as few as three months. We always start with a focused POC to de-risk the approach.

Do I need a simulation or digital twin for reinforcement learning?

A simulation or digital twin is usually the most efficient path to training RL agents, because it allows the agent to explore millions of scenarios safely and cheaply. However, it is not always strictly required. In some cases, offline RL techniques can learn from historical data. Our team will assess your situation and recommend the most practical approach.

What reinforcement learning frameworks and tools do you use?

We are framework-agnostic and select the best tools for each project. Our team has deep experience with Ray RLlib, Stable Baselines, CleanRL, PyTorch, TensorFlow, OpenAI Gym, Gymnasium and custom simulation environments. We deploy on AWS, Azure, GCP and on-prem Kubernetes for scalable training and serving.

Can reinforcement learning work with my existing data?

Yes. While RL agents typically learn through interaction with an environment, offline RL and batch RL methods allow agents to learn from historical decision data. This is particularly powerful in industries where real-time experimentation is costly or risky, such as finance or healthcare.

What industries benefit from reinforcement learning?

We have delivered RL projects in aviation (flight scheduling), finance (customer journey optimisation and trading), energy (hydroelectric generation), manufacturing (industrial process control), cyber security (penetration testing) and supply chain (inventory optimisation). Any industry with sequential decision-making problems and a measurable objective is a candidate.

What is RLHF and how can it help my business?

RLHF (Reinforcement Learning from Human Feedback) is the technique used to align large language models with human preferences and business requirements. If your organisation is deploying LLMs, RLHF can fine-tune models to follow your brand voice, comply with industry regulations, reduce harmful outputs and improve accuracy on domain-specific tasks. Our RLHF training services cover reward modelling, preference pipelines and PPO/DPO training.

Reinforcement learning, explained

What is reinforcement learning?

As we describe in our O’Reilly book, reinforcement learning (RL) is a sub-discipline of machine learning that specialises in teaching machines to execute multi-step, strategic decisions. Traditional ML automates single decisions; RL optimises sequences of decisions toward a long-term objective such as revenue, throughput or cost.

How does reinforcement learning work?

The key premise is an environment and a policy. The environment represents the space the agent operates within, with all the signals and data it can observe. The policy is the agent’s decision model, choosing what to do at each step. Over time the agent updates its policy to make better decisions and accumulate more reward. We cover this in depth in our O’Reilly book.

How is reinforcement learning different from traditional machine learning?

Traditional ML learns to make a single prediction or classification from data. RL learns strategies that unfold over multiple steps, optimising a long-term objective through trial and error. For example, traditional ML might predict which product a customer is likely to buy next; RL would learn the entire engagement strategy that maximises lifetime customer value.

Where is reinforcement learning used in the real world?

Reinforcement learning is used across finance (trading, credit decisions, customer lifecycle), manufacturing (process control, scheduling), supply chain (inventory, routing), aviation (flight scheduling), energy (grid management, generation dispatch), robotics, autonomous driving and recommendations. Any domain with sequential decisions and a measurable objective is a candidate.

What is the relationship between reinforcement learning and large language models like ChatGPT?

RL is fundamental to how large language models are aligned with human preferences. RLHF uses RL to fine-tune language models to produce more helpful, safe and accurate responses. Our deep RL expertise gives us unique insight into this rapidly evolving field.

What's the value proposition of reinforcement learning?

Strategic, sequential decisions are usually the most lucrative and the most expensive a business makes. RL automates them by optimising for the long-term business objective directly, rather than the proxy objectives traditional ML targets.

Get Started

Start your reinforcement learning project

Whether you need a custom RL environment built, an RLHF training pipeline for your LLM, or a production reinforcement learning system for industrial control, talk to the team that wrote the O'Reilly book on the subject.

You'll talk to PhD-level RL engineers, never a sales layer
Welcome call booked within 48 hours
Typical RL proof-of-concept: 8 to 12 weeks

Ready when you are

Send us a brief and book a welcome call within 48 hours.

Talk to the RL engineers

Need an RL company that ships, not just publishes? Start your RL project