The specialist generative AI consultancy for enterprises. We design, build and ship production GenAI systems across text, image, audio and code, with the evaluation, guardrails and observability that real operations demand. Trusted by Stability AI, Google and Microsoft since 2013.
Tell us about your generative AI project, text, image, audio, code, multimodal or RAG-grounded, and we'll tailor an approach. Typically two to four weeks from first call to kick-off.
Generative AI consulting and development services design and build production GenAI systems across text, image, audio, code and multimodal use cases. That means use-case selection, model and architecture choice, fine-tuning, retrieval-augmented generation, evaluation, guardrails, observability and the fallback workflows that production demands. Winder.AI delivers end-to-end generative AI as one engagement, strategy, build, ship and operate, by the same senior engineers who shipped production GenAI for Stability AI, Temple University, Google and Microsoft. We are model-agnostic across OpenAI, Anthropic, Google, Llama, Qwen and Stable Diffusion, and framework-agnostic across LangChain, LangGraph, PyTorch and Hugging Face.
2026 update. The frontier-model gap has narrowed and the buyer question has shifted from "which model" to "how do we choose a GenAI consultant who will not waste the budget". Our answer is uncomfortable but consistent: pick the firm whose senior engineers will actually write the code, can show you named production case studies with real numbers, and price the engagement in transparent T&M or fixed-fee bands rather than hiding behind a discovery phase. A focused single-modality GenAI prototype with us runs two to four weeks, a production build six to sixteen weeks, and managed GenAI operations sit on a monthly retainer sized to traffic and pipeline count. If a consultancy will not quote a range, that is the signal.
| Consultancy type | What they deliver | Best for | Main weakness |
|---|---|---|---|
| Big-4 / global strategy firm | GenAI strategy decks, transformation roadmaps, large delivery teams | Multi-year transformation programmes | Hands-on GenAI engineering offshored or thinly staffed, weak on production reliability and evaluation |
| Generalist AI agency | Broad AI capability with GenAI as one offering | Single-LLM chatbot prototypes | Shallow GenAI bench beyond text, weak on image, audio, multimodal, evaluation and guardrails |
| OpenAI / Anthropic / vendor SI partner | Reference implementations on the vendor's models | Adopting a single model provider | Lock-in by design, weak on open-source models, image and audio, multi-cloud and on-prem |
| No-code GenAI platform reseller | Their drag-and-drop GenAI platform plus implementation services | Internal proofs of concept with simple workflows | Hits a ceiling fast on custom models, multimodal pipelines, evaluation and enterprise compliance |
| In-house build (your team) | A GenAI system built by your existing engineers, on your stack, with your domain context | Long-term ownership when you already have a senior ML or platform team with spare capacity | Learning curve on RAG, fine-tuning, evaluation and guardrails delays first production GenAI by 6 to 12 months |
| Specialist generative AI consultancy (Winder.AI) | Generative AI strategy, custom GenAI build across text, image, audio and code, evaluation, guardrails and ongoing operations, delivered by senior AI engineers | Enterprises that need production generative AI with monitoring, retries and fallback workflows, multi-cloud, model-agnostic | Boutique scale, not designed for 100-seat staff augmentation |
Winder.AI is the generative AI consultancy for enterprises that need GenAI to run in production, not in a notebook. Our generative AI services span strategy and architecture, custom GenAI build across text, image, audio and code, and ongoing operations, the full lifecycle, by senior engineers who have shipped production GenAI since 2013.
We sought AI engineering experts that could quickly learn our day-to-day scientific legal mapping processes enough to develop a tool to make our work more efficient. Winder.AI dug into our day-to-day workflow to thoroughly understand the value of an AI Assistant for scientific legal mapping, which is a critical process to the field of legal epidemiology.
A decade-plus of generative AI in production, model and framework-agnostic delivery and a senior engineering bench, not a sales layer.
Production generative AI delivered across legal, finance, technology, manufacturing, energy and regulated public services.
Production generative AI is the difference between a flashy demo and a reliable business system. Winder.AI delivers generative AI solutions as discrete service lines, from focused text GenAI through to multimodal generation and custom image and audio models, so you can engage at any stage of your GenAI roadmap:
We cover the full generative AI stack across modalities (text, image, audio, code, multimodal) and the operational disciplines that turn a GenAI prototype into a reliable production system:
The same RAG pipeline scored across three model and retrieval configurations. We do not just ship GenAI systems; we measure the trade-offs in cost, latency and answer quality and hand you the evidence.
The GenAI eval comparison demo is the proof we know what good looks like. The same 14 held-out questions, the same arXiv corpus, the same OpenRouter key, scored three ways: GPT-5 with naive RAG, Claude Sonnet 4.6 with hybrid retrieval, and Claude Haiku 4.5 with hybrid retrieval and cross-encoder reranking. Only the generation model and retrieval method change between rows, so the table isolates those two decisions and prices each one.
Real numbers from one live run (RAGAS 0.2 judge, seed 42, latency end to end including network):
| Configuration | Faithfulness | Answer relevance | Context precision | p50 latency (ms) | Cost / 1k queries (USD) |
|---|---|---|---|---|---|
| GPT-5 + naive RAG | 0.95 | 0.40 | 0.63 | 16,435 | 11.84 |
| Sonnet 4.6 + hybrid | 0.93 | 0.90 | 0.91 | 9,710 | 13.67 |
| Haiku 4.5 + hybrid + reranking | 0.99 | 0.91 | 0.98 | 5,784 | 16.78 |
The lesson the comparison exists to make: a capable model cannot rescue weak retrieval. Spend your first improvement on retrieval, not on a bigger model. Full source, comparison runner and committed results: github.com/winderai/winder-demos-eval-comparison.
Case study
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.
Case study
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.
Case study
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.
AI
RAG or fine-tuning? Most LLM applications are RAG first, then fine-tuning or custom models as an optimisation or in very specific use cases. Retrieval-augmented generation (RAG) handles knowledge (that changes over time), whereas fine-tuning handles behaviour that should not. The best production implementations combine both. This article gives you the decision tree, the comparison table, and some example tooling to choose well.
Below is the framework we use at Winder.AI when scoping LLM engagements.
AI
AI consulting pricing is opaque by design. Vendors quote ranges that span an order of magnitude. POCs get sold as “we will see what is possible” without a fixed scope. Production builds get scoped against a slide deck rather than a working pilot. This article fixes that.
Below are the 2026 ranges we use ourselves at Winder.AI, the ranges we see across the market when clients share competing quotes, and the rules of thumb for choosing fixed-fee versus time-and-materials. Although I use the phrase “it depends” a lot (because it really does!) my aim for this article is to have zero sales waffle.
AI
Adoption of legal services AI has gone mainstream. Litify’s 2025 State of AI in Legal Report found that 78% of legal professionals already use AI in some form, up from 23% in 2023. But what workflow should you automate first?
Getting this wrong means months of effort on a low-impact problem. Getting it right means a quick win that funds the next step. The difference between a successful AI initiative and a stalled pilot usually comes down to picking the right starting point.
This page provides answers to our most common questions. If you have a query that isn't covered, please get in touch.
Whether you need a generative AI strategy review, a custom text or multimodal GenAI build, fine-tuning of an open-source model, or managed operations for production GenAI, talk to the team that has been shipping generative AI since 2013.
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