The UK AI Development Company That Builds Custom AI Into the Systems You Already Run

AI development is the work of designing, building, testing and running software that uses artificial intelligence to do a useful job. For most businesses today that means software built around a large language model, the kind of AI behind ChatGPT, connected to your own data and systems. It covers the whole journey: picking the job, preparing data, building, checking accuracy, going live and keeping it working.

An AI development company turns a business problem into working AI software. It works out which job AI should do, checks your data, chooses a model, builds the software and the connections to your systems, tests how accurate it is, puts it live securely, and supports it afterwards. The good ones also tell you when a ready-made tool would do the job better than a build.

AI development services are the separate pieces of work an AI development company sells. The usual list is custom AI software development, AI integration into existing systems, AI implementation (taking an idea into live use), document search and question answering over your own files, data preparation, model evaluation, and ongoing support. Most projects use several of these together rather than one on its own.

AI integration means connecting AI to the software your business already uses, so it can read the right information and put results in the right place. Examples: AI that reads incoming emails and creates tickets in your helpdesk, drafts quotes inside your CRM, or tidies product data in your ecommerce platform. The value comes from the connection, not the AI on its own.

Common examples for UK businesses: a search box that answers staff questions from your policies and manuals; AI that reads emailed purchase orders and keys them into your ERP; summaries of long customer threads inside your helpdesk; product descriptions drafted from supplier data in your shop; and meeting notes that land as actions in Microsoft 365. Each one saves a repeated manual step.

AI implementation is the step from a promising idea or demo to AI that your team uses every day. It covers the unglamorous work most demos skip: preparing real data, connecting to real systems, testing accuracy on real cases, setting permissions, meeting UK GDPR duties, training staff, monitoring results and fixing problems. It is where most AI projects succeed or stall.

RAG stands for retrieval-augmented generation. In plain English: before the AI answers a question, the system first looks up the relevant pages from your own documents, then asks the AI to answer using only those pages. It is how you get AI that answers from your price lists, policies or manuals instead of guessing from general knowledge, and it lets the answer show where it came from.

An AI agent is one thing an AI development company can build. It is AI that takes actions across your systems within rules you set, such as updating a record or booking a slot. AI development is the wider discipline, which also covers document search, integrations, data tools and internal apps. For agents specifically, see our UK AI agents page.

There is no single best one; it depends on your size and the job. Large enterprises often use IBM or big global engineering firms such as ScienceSoft or Itransition. UK SMEs and mid-market firms more often look at UK specialists such as Geeks Ltd, Chilliapple, Imobisoft, Ronins or TechGropse. We list them, including us, on this page with a neutral note on each. Talk to two or three before you choose.

Ask to see systems they have put live, not demos. Ask who will do the work and whether the same team supports it after launch. Check that you will own the code, prompts and data. Ask how they measure accuracy, how they handle UK GDPR, and when they would tell you to buy a tool instead. A clear answer to all six is a good sign.

A freelancer is fine for a small prototype or a single, contained script. A company makes more sense when the AI needs to connect to several systems, handle personal data, or run every day for years, because you need testing, security, cover for holidays and someone on call when things change. Many businesses start with a freelancer and move to a team once the idea proves itself.

Buy first when the job is general: writing, summarising or searching documents. Tools like Microsoft Copilot or ChatGPT Enterprise are quick to roll out. Build when the job depends on your own systems, rules or data, or when a tool cannot reach the software your team uses. The UK government’s own research found most firms adopting AI bought ready-made tools. Build only where buying falls short.

Yes. We work with businesses across the UK, including London, Manchester, Birmingham, Leeds, Bristol and Scotland. AI development is done well remotely: workshops over video, shared test environments, and regular demos of working software. Searching for AI development near me matters less than finding a team that answers quickly and stays involved after launch.

No. We do not resell any AI platform. We choose between models from OpenAI, Anthropic, Google, open-source options and Microsoft Azure based on accuracy on your test cases, where your data needs to stay, and running cost. We build so the model can be swapped later without rewriting the whole system, because the best model today may not be the best next year.

A focused first project usually takes 6 to 14 weeks from kick-off to live use. Discovery takes 1 to 2 weeks, data preparation 1 to 4 weeks, a working prototype 2 to 4 weeks, the production build and integrations 3 to 8 weeks, and a pilot with real users 2 to 4 weeks. Messy data and older systems stretch the timeline; a clear, narrow job shortens it.

It depends on scope, not a rate card. The biggest drivers are how many systems the AI must connect to, how clean your data is, how accurate it needs to be, how much personal data it touches, and whether you want ongoing support. We scope after a free first call and quote a fixed price per phase, so nothing is open-ended. Model usage is billed to you directly by the provider.

Start with one job, not a strategy document. Pick a repeated task where mistakes are low-risk and results are easy to measure. Check the data it needs. Try a ready-made tool first. If that falls short, build a small prototype, test it on real cases, run a short pilot with the people who do the job, then roll out with training and monitoring. Add the next job only after the first one works.

We use seven: discovery (pick the job and the success measure), data preparation, model choice, prototype, production build and integration, evaluation and pilot, then launch with monitoring and support. Different firms name them differently, but every serious AI development lifecycle includes testing accuracy before launch and watching it after, because AI output can drift as data and models change.

Five we hold to. Start with a measurable business problem. Keep a person checking outputs where mistakes matter. Use the least personal data you need. Test accuracy on real cases before launch and keep measuring after. And make sure you own and understand what was built, so you are never stuck with a black box only one supplier can touch.

You can build a simple prototype at low cost using free tiers and no-code tools, and it is a good way to test an idea. Running AI properly in a business is different: model usage is billed, systems need secure connections, and someone has to test, monitor and fix it. Free is fine for learning. For anything customers or staff depend on, budget for the build and the upkeep.

We stay on. Monthly support covers monitoring accuracy, fixing problems, updating prompts and connections when your systems change, and testing new models as they are released. You get a simple report of how the system is used and where it gets things wrong. You can also take support in-house at any point, because the code and documentation are yours.

Senior engineers, with the founder, Bhavesh Barot, involved in every engagement. FactoryJet has served 500+ businesses since 2014, mostly in commerce. The people who scope your project are the people who build it and support it. There is no hand-off to a junior team after the contract is signed.

No. Checking how good your data is for a specific job is part of the work. Many useful first projects run on data you already have in documents, emails, your CRM or your shop. If a job needs better data, we tell you exactly what to fix and in what order. What you do need is someone who knows the process well enough to judge whether the AI got it right.

If the AI touches personal data, UK GDPR applies from the first design decision. The ICO says the accountability principle makes you responsible for complying, and for showing you comply, in any AI system that processes personal data. In practice we map what personal data each feature uses, limit it to what is needed, check which providers see it, and help you prepare a DPIA.

Usually. A DPIA, or data protection impact assessment, is a written check of the risks a project creates for people’s data and how you reduce them. The ICO says that in the vast majority of cases, using AI involves processing likely to result in a high risk to people’s rights and freedoms, which triggers the legal requirement for one. We flag it at discovery.

Not if the system is set up properly. We use AI providers through their business terms, which by default do not use your inputs for training, and where it matters we use UK or EU cloud regions that providers such as Microsoft Azure and AWS offer. We document which provider sees which data, so your data protection lead and your customers can get a straight answer.

There is no single UK AI Act. AI is governed by existing law, above all UK GDPR and the Data Protection Act 2018, plus sector rules from regulators such as the FCA or the CQC where they apply. If you sell AI-powered services into the EU, the EU AI Act may also apply. We build to these requirements and suggest you confirm your position with a legal adviser.

Yes. The code, the integrations, the prompts, the test sets and the documentation are yours. We are not a platform you rent. If you move the work in-house or to another provider, it keeps running. You hold the accounts with the AI model providers and pay them directly, with no markup through us.

Nothing removes the risk completely, so we design around it. We make the AI answer from your own documents (the RAG approach), show its sources, refuse when it does not know, and keep a person approving anything that matters. Before launch we test it against a set of real questions with known answers, and we keep scoring it after launch.