Enterprise LLM consulting and development services from the engineers who built Stable Audio, Shell's enterprise question-answering platform and LLM systems for an FCA-regulated car-finance lender. RAG, fine-tuning, multimodal LLMs, agents and on-prem deployment, delivered as one engagement.
Tell us about your RAG pipeline, fine-tuning need, multimodal LLM project or on-prem rollout, and we'll tailor an approach. Typically two to four weeks from first call to kick-off.
LLM consulting services help an enterprise decide which large language model strategy fits its business, retrieval-augmented generation (RAG), fine-tuning a base model, agent design, evaluation, or deploying open-source models on-prem. LLM development services are the engineering work to ship those systems into production. Winder.AI delivers both as one engagement, the same senior engineers who built Stable Audio for Stability AI, the enterprise QA platform for Shell and LLM systems for an FCA-regulated car-finance lender. RAG pipelines, custom fine-tuning, multimodal LLMs, [LLM agents](/services/ai-agent-development/index.md), evaluation harnesses and on-prem or air-gapped deployment, in one consultancy.
2026 update. Frontier model providers (OpenAI, Anthropic, Google) now ship strong default RAG, structured tool use and built-in evals, so the build-versus-buy line has moved. The hard parts of production LLM development services in 2026 are evaluation harnesses, retrieval quality, inference cost at scale and on-prem or air-gapped deployment for regulated data. We pick the lightest LLM stack that meets your accuracy and compliance bar, often a small fine-tune over an open-source base model plus RAG with proper evaluation, rather than a heavyweight platform.
| Provider type | What they deliver | Best for | Main weakness |
|---|---|---|---|
| Big-4 / global strategy consultancy | LLM strategy decks, governance frameworks, large delivery teams | Multi-year LLM transformation programmes | Hands-on LLM engineering offshored or thinly staffed |
| OpenAI / model-vendor solutions team | Reference implementations on the vendor's models and APIs | Adopting a single closed-source model family | Lock-in by design, weak on open-source, on-prem and multimodal alternatives |
| Generalist AI agency | Broad AI capability with LLMs as one offering among many | Bundled vendor relationships | Shallow LLM bench, light on RAG evaluation, fine-tuning and LLMOps |
| LLM platform vendor (with services) | Their LLM platform, plus implementation services around it | Standardising on a single LLM tooling vendor | Conflict of interest, every problem looks like their platform |
| In-house build (your team) | An LLM 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 evaluation, fine-tuning, LLMOps and inference cost delays first production LLM by 6 to 12 months |
| Specialist LLM consultancy (Winder.AI) | LLM consulting, custom and private LLM development, RAG, fine-tuning, multimodal LLMs and LLMOps, delivered by senior AI engineers | Enterprises that need production LLMs, multimodal models, on-prem deployment and regulated environments | Boutique scale, not designed for 100-seat staff augmentation |
Winder.AI is the LLM consulting partner for organisations that need large language models to run in production, not in a demo notebook. Our LLM consulting services span strategy and architecture, custom LLM and RAG development, and LLMOps for production deployment, the full lifecycle, by senior engineers who have shipped LLM systems for Stability AI, Shell and regulated finance clients.
The major benefit for us was winning TIME’s Best Invention of 2023! Without Winder.AI’s innovative AI consulting, this success would not have been possible. It resulted in over 500k generations in its first two months.
A specialist large language model consultancy with a public record of production LLMs, multimodal generative systems and regulated-industry delivery, and senior engineers who write the code.
Production LLMs and generative AI delivered across technology, energy, finance, publishing and regulated industries.
Production LLM systems are the difference between a generative AI demo and a reliable business product. Winder.AI delivers LLM solutions as discrete service lines, from RAG and custom LLM development through to multimodal models, agents, LLMOps and alignment, so you can engage at any point in your large language model journey:
We cover the full large language model stack across open-source and closed-source models, retrieval, fine-tuning, agents, evaluation and on-prem inference, the technical disciplines that turn an LLM prototype into a reliable production system:
An end-to-end retrieval-augmented generation system over a named arXiv corpus, with real RAGAS scores from a live run. Open source on GitHub, clone and reproduce.
The RAG explainer demo shows how we build and measure LLM systems for clients: ingestion, chunking, embedding, hybrid retrieval (dense fused with PostgreSQL full-text), generation with cited sources, and a RAGAS evaluation harness. One OpenRouter key runs the whole pipeline, and every number on the page comes from a single live run on 187 arXiv papers, not a marketing claim.
Real numbers from the run on 14 held-out questions (RAGAS 0.2, Claude Sonnet 4.6 judge, seed 42):
| Metric | Score |
|---|---|
| Faithfulness | 0.94 |
| Answer relevance | 0.85 |
| Context precision | 0.91 |
Full source, eval harness and committed results: github.com/winderai/winder-demos-rag-explainer.
Case study
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.
Case study
We’re pleased to announce the release of Stable Audio, a new generative AI music service. Stable Audio is a collaboration between Stability AI and Winder.AI that leverages state-of-the-art audio diffusion models to generate high-quality music from a text prompt.
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.
AI Agents
I’ve spent the last few months on Helix-Org, my attempt at rebuilding an organisation as AI agents. The first version had me writing hyper-specific agents in code, each one specialising in a single job. It worked, and it became tiresome: every new job meant another small program to write, test and maintain. Eventually I tried describing one of them in Markdown instead and handing it to a harness. It did the same job. Markdown is now code.
DeepSeek shipped an agent harness in developer preview on 13 August 2026, and it collected 95,386 GitHub stars in about two days, one of the fastest adoption curves GitHub has recorded. The thing I stumbled into has a name, a plugin standard and nine products worth choosing between. We run seven of them. Which one you pick matters less than which layer you need, and no feature grid answers that.
AI
How do you build an AI agent in 2026 that survives production? You wrap a capable model in a good harness, provide it with information and tools, a sandboxed environment, a store with write rules, an evaluation loop, and you put a human in the loop on anything irreversible.
This guide is the playbook we use at Winder.AI when scoping and delivering agentic engagements. It includes a framework comparison with an opinionated “best for” column, two worked examples (a constrained agent in code and an open-ended agent defined in markdown), the environment, store, harness, and evaluation patterns that actually survive contact with real users, and a collection of pitfalls that can kill agent projects.
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.
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 an LLM strategy review, a production RAG pipeline, a fine-tuned or private LLM, a multimodal model or an on-prem rollout, talk to the team that has been shipping production language models since 2017 and production AI since 2013.
