AI Consulting & Development Services

2026/Case 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.

2025/Case study

How Winder.AI Helped Apartment List Eliminate Data Drift and Scale MLOps Automation

Winder.AI helped Apartment List modernize its machine learning operations by unifying data pipelines, automating Kubeflow workflows, and introducing enterprise-grade governance. The outcome: consistent training and inference data, faster deployment cycles, and self-service capabilities that enabled Apartment List’s data science team to scale model delivery with confidence.

2025/Case study

AI in Aviation Case Study: Flight Scheduling Using Digital Twins and Reinforcement Learning

Using digital twin data to build flight traffic simulators and train reinforcement learning AI agents. A leading aerospace business and Winder.AI opened new horizons for dynamic, data-driven scheduling solutions that integrate with our client’s advanced flight planning technology.

2025/Case study

AI in Aviation Case Study: Predicting Taxi Times

Leveraging predictive analytics and a stand-to-runway modelling approach, Winder.AI and our aviation client improved taxi time predictions to reduced ground delays and improve fuel efficiency.

2024/Case study

Transforming Legal Research with AI Legal Text Analysis

Winder.AI built a legal AI assistant for the Center for Public Health Law Research at Temple University, automating the scientific legal mapping work behind its MonQcle platform. In testing, the CPHLR team measured it as nearly 80% accurate at identifying the appropriate legal text for a coding question.

2023/Case study

MLOps in Supply Chain Management

Interos, a leading supply chain management company, partnered with Winder.AI to enhance their machine learning operations (MLOps). Together, we developed advanced MLOps technologies, including a scalable annotation system, a model deployment suite, AI templates, and a monitoring suite. This collaboration, facilitated by open-source software and Kubernetes deployments, significantly improved Interos’ AI maturity and operational efficiency.

2026/AI

AI Agent Evaluation: How to Test an Agent Before You Ship It

We spent five months with Duetto working out whether reinforcement learning could price hotel rooms better than the heuristics they already had. The algorithm was never the hard part. Nobody can observe what demand would have been at a price the hotel did not charge, so there was nothing to check an answer against, and the measuring instrument had to be built before anything we said about the agent meant much. When we turned on that instrument and looked at it properly, the revenue lift it reported correlated with the error in the demand model underneath it. It had been flattering the agent in proportion to how wrong it was.

Agents built on language models have a smaller version of the same problem, and it arrives the week someone senior asks whether the thing is safe to ship. Until the measuring instrument exists, everything the agent produces is an anecdote.

2026/AI

Why AI Agents Fail in Production, and the Observability That Catches It

The agent has been fine for six weeks. Then a customer complains, you open the trace, and every step is green. Nothing timed out, nothing threw an exception, and the summary at the end says the job is done. It is not done.

Agents fail in production in a small number of recognisable ways, and almost none of them are the model being wrong. They call the right tool with the wrong arguments. They run out of context partway through a long task and forget a constraint you gave them at the start. They report success after a step that failed. They loop, and you find out when the bill arrives. Or they answer confidently from data that stopped updating on Tuesday.

Every one of those has an engineering fix, and every fix is code inside your agent loop, not a product you buy.

2026/AI

Top AI Agent Development Companies in 2026: Who Has Actually Shipped One

The right AI agent development company depends on which tier your problem sits in. For a bespoke agent that has to survive production and be trusted near a system of record, the engineering-led specialists: Winder.AI and Neurons Lab. For capacity on a long programme, the large offshore benches: LeewayHertz, Appinventiv and N-iX. For a configurable product instead of a commissioned build, the platform vendors, which since December 2025 means ServiceNow, because it bought Moveworks. And for a multi-year transformation where agents are one workstream of twenty, Accenture and Deloitte.

We are Winder.AI, so we sell against most of the firms below. We appear in the specialist tier, and this comparison is ours.

Every firm here sells agent development. The question that sorts them is narrower. Can they name a client, name the agent they put into production for that client, and quote a number that agent moved? I went looking on 14 August 2026. Of the eight firms in the table below, one answers it in full and one answers it halfway. Being the firm that passes is less flattering than it sounds, because the bar is a single number on a single case study.

Buyers building this shortlist are afraid of one thing: spending six months and a budget on something that demos beautifully and then cannot be trusted near a system of record. That fear has numbers behind it. In a survey of 1,340 practitioners run between 18 November and 2 December 2025 by LangChain, whose own users it over-samples, 57.3% had agents in production, quality was the top barrier for everyone else at 32%, and only 37.3% of respondents ran any online evaluation at all (LangChain).