Generative AI development UK product teams can ship: content engines, creative tools, copilots, and image pipelines built as real products with the copyright, provenance, and disclosure questions answered before you ship, not after someone asks.
A generate button is easy to ship and easy to abandon. Users try it twice, get something generic, and never come back. Generative features earn their place when they are wired into a real workflow with the user's own context, not bolted onto a dashboard as a demo.
Teams reach for fine-tuning because it sounds serious, spend weeks and a lot of money, and end up with something a good prompt and retrieval would have beaten. The choice between prompting, RAG, and fine-tuning is the single biggest cost decision in a GenAI build, and most people get it wrong in the expensive direction.
Then a customer asks whether their data trained the model, legal asks what licence the training images carried, or a regulator asks whether users knew they were reading AI-generated text. If you cannot answer, the feature comes back out of the product. In the UK these questions arrive sooner than teams expect.
Our generative AI development ships features with provenance you can explain and a workflow users come back to.
See what you get →Every build ships with the same core package. It is the baseline any generative AI development UK teams pay for should meet, whether it writes, drafts, summarises, or generates images.
Deployed in your infrastructure with streaming, caching, retries, and fallbacks when a provider degrades or rate-limits you.
Prompting, RAG, or fine-tuning, chosen against your data and your budget, with the reasoning written down and the cost comparison shown.
Tested, versioned prompts with documented reasoning behind every decision, so the behaviour is reproducible and not folklore.
Benchmarks on your real inputs: quality, latency, cost per generation, and the failure cases nobody wants to find in production.
What was generated, by which model, from which sources, with the AI-generated labelling your users and regulators expect.
Prompt tuning, model updates, and drift monitoring after go-live, from the team that built it.
If any of these describe you, we will tell you on the first call rather than waste your time or ours.
Founder at Seconddate
Foundry 5 really impressed me with their work on my AI-powered dating app. They nailed the UI/UX, starting with core app screens and then translating that vision to the web. The React site they built is sleek and polished - exactly what I was hoping for. What I appreciated most was their thoughtful approach and how easy they were to work with. They got the job done without needing me to micromanage.
CEO at StreaksAI
Foundry 5 surpassed all expectations in delivering our web development product swiftly and flawlessly. Their speed and eye for detail, especially under tight time constraints, showcase their commitment to excellence.
CEO, Xcelsior Capital
Working with Foundry 5 has been a great experience from start to finish. They seamlessly translated our vision into a website that not only showcases our brand and operations but also enhances our online presence as an investor in natural resources. Nothing was too much for them and they always put the client first.
CEO of Ove
Working with Foundry 5 to create the Ove app has been an absolute pleasure. Their professionalism, work ethic, and willingness to always go the extra mile has really impressed me. I would recommend them to anyone.
Loom Founder
Foundry 5 have gone above and beyond to bring my vision of Loom to life. Not only this, but they continue to be an integral part of our team. They are lovely people to work with, and I recommend them to anyone looking for a software partner. Particularly for non-technical tech founders (like me!) who need to ensure that their business is in safe hands.
Chief Product Officer Gather
The team has been instrumental in driving both the design and development of Gather, pairing a proactive, highly responsive workflow with the technical depth needed to handle our platform's complexity. Their partnership continues to move the product forward in a reliable and impactful way.
| Feature | Foundry 5 | Wrapper agency | In-house |
|---|---|---|---|
| Prompting vs RAG vs fine-tuning, costed | |||
| Output quality benchmarked before launch | |||
| Provenance and disclosure handled | |||
| Built into a real user workflow | |||
| Streaming, caching, and fallbacks | |||
| Cost per generation monitored | |||
| Versioned prompt library you own | |||
| Honest advice on model choice |
Pricing for generative AI development UK teams can plan around: we scope the work, agree a price, and stick to it. No surprise invoices, no scope creep charges. You get a written scope document, and a cost model for what each generation will cost you at your expected volume.
For generative products that evolve as you learn what users actually generate. You reprioritise at the start of each sprint, and the prompt library and evaluation set grow with the product.
Anything else? Just book a call. We'd rather talk than write another FAQ entry.
✓Risk-free first project · Full refund if you’re not satisfied
Prompting handles most tasks and costs the least, so it is where we start. RAG is the answer when the model needs to know your specific content, such as your documents, your catalogue, or your policies. Fine-tuning is for when you need a consistent style, format, or behaviour that prompting cannot hold reliably, and it is the most expensive option by a distance. Most teams that ask for a fine-tune do not need one, and we will tell you that in week one rather than take the budget.
Content generation tools, AI writing assistants, copilots that work inside your product using your users' own context, summarisation and drafting inside existing workflows, and image generation pipelines that run at volume. The common thread is that the feature sits in a workflow somebody already has, rather than being a generate button hoping to find a use.
This is exactly the question to ask before you build, not after. The short version is that ownership depends on what the model was trained on, which provider you use, what their terms say about output rights, and how much human authorship shapes the result. We work through it with you as part of the build, document what the position is for your specific setup, and design the pipeline so you are not relying on a claim you cannot support. For anything with real commercial exposure, take our documented position to your own legal counsel rather than treating an agency as a substitute for one.
In most consumer-facing cases, yes, and increasingly it is expected rather than optional. We build disclosure and labelling into the product from the start: what was generated, by which model, and where a human reviewed it. It is far cheaper to design this in than to retrofit it when a regulator, a marketplace, or a customer asks.
We use providers under agreements that prohibit training on your data, and we document exactly what is sent to a model and what is retained. If you fine-tune, we are strict about provenance: you need the rights to the data you train on, and we will not build on a dataset you cannot account for. Where the use case demands it, we run open models in your own environment so nothing leaves it.
Fintech, where drafting and summarisation sit under a compliance requirement. Healthtech, where provenance and human review are non-negotiable. Creative tools, where generation is the product itself. Legal, where document drafting and retrieval over large corpora are the use case. And e-commerce, where product content and personalisation run at volume.
Generic output is almost always a context problem, not a model problem. The feature has to know something the user knows: their data, their past work, their tone, their catalogue. That means retrieval and product design, not a bigger model. We benchmark output quality against a rubric your team agrees with, because “does this actually sound like us” is a question only you can score.
Book a 30-minute call. No pitch. We will tell you whether prompting, retrieval, or a fine-tune is the right answer for the generative AI development UK teams like yours actually need, and what it costs to build properly.
✓Risk-free first project · Full refund if you’re not satisfied
