You're considering AI for your product, your operations, or your customer experience. What we will do is audit the actual problem you are looking to solve, present our proposal on the matter, recommend which of our AI services fits your requirement, and even tell you if none of our services fit your needs, recommending that perhaps a much more simple solution is more appropriate to the task.
AI development means building, deploying and maintaining machine learning and LLM systems inside your production stack, not PoCs that never leave a notebook. We do plenty of web app development, mobile apps, and custom software too, so there's no reason we'd push AI into places where it really doesn't belong. As of today, our engineering team has launched over 50 AI features into production and those systems are still running, monitored, and maintained. See how we did it below, or reach out and tell us what you need built.
Senior AI engineersAI, design, apps and infrastructure in-houseFrom startups to enterprise scaleWorldwide delivery
12k tickets a month, 3 support agents, 46 hours average first response time. An omnichannel bot, with the integration to their order management system, and with confidence scores and escalation logic.
Fast forward six months and the company has now been able to achieve 74% deflection and qualifying queries are taken care of by the bot within 2 mins, and everything else is taken care of by the agent team.
Two full-time employees were spending their work hours copying data from PDF invoices to their ERPs. The error rate was around 4%, adding up to quarterly reconciliation challenges. We constructed an extraction pipeline configured for their 12 most common types of invoices with confidence score.
After six months, 91% of the invoices are processed without human intervention, and the rest are presented in pre-extracted form, with doubtful fields marked, for 30 seconds human review.
Inventory planning was based on last year's numbers and gut feel. Overstock consumed precious working capital, understock was bleeding sales. To train our model, we used three years of historical transactions with information related to local events and seasonality included. To maintain model accuracy, we performed retraining on a weekly basis.
At the six-month mark, inventory waste is down 28% and the model can now forecast problems before they hit the P&L. But the real win is: our planning team actually uses the model now.
Over 400 employees were having to dig through SharePoint, Confluence, and three legacy systems looking for answers related to compliance. It was, on average, 35 minutes from start to finish to locate the right document. We implemented a RAG-enabled assistant with permission-based access controls tied to each user's SharePoint and Confluence permissions.
Six months later, average answer time is under 8 seconds, and 89% of the questions that need an answer are solved without the need to launch a different system.
An AI platform we designed, engineered and shipped from first conversation to 17 countries. We still maintain it.
AgentWise is an AI-powered operating system for real estate, which we created and developed from scratch. It brings together agent matching, workflow automation, and real-time secure messaging in a unified platform which replaced a fragmented ecosystem of WhatsApp groups. 900+ active real estate agents from 17 countries used AgentWise in the first five months and we achieved a growth of 500% of users in just a few months.
Most AI agencies won’t give you any of these three things until after you sign the dotted line. They won’t tell you who is actually going to work on your project until after you sign. They won’t tell you the cost until you sit through a discovery call. They won’t talk about your post-launch monthly bill unless you specifically ask about it. We’re laying all of them out on this page, because burying them doesn’t make them disappear.
The same team that shipped AgentWise to 17 countries, including lead engineer Michal Vavra, will scope and review your architecture. Your engineers can talk to ours before you sign anything. We don't throw a project down the stairs to a B-team after the sales call and we don't rotate contractors through client work to protect margins.
We always have a scoped phase at the beginning of an engagement, so you'll know what you're getting involved in before you agree to proceed to the next phase. Assessments are priced starting at £5,000; PoC and MVP efforts usually range from £5,000 to £40,000; and enterprise deployments go on up from there. One client came to us anticipating they would need a custom LLM, and walked away with a £5,000 automation that addressed their need. We could have upsold them on a £40,000 build. We didn't, and they were back in four months to do a project that warranted that level of investment.
AI, just like any software, requires ongoing upkeep, support and maintenance. Models degrade as your data evolves. Inference pricing fluctuates as vendors update their rates. We offer annual support retainers typically calculated at 15-25% of your original build budget that include continuous monitoring, retraining and optimisation of your ML systems. Or we can help you hire a team to support and maintain your AI systems in-house. We'll equip you with your full architecture, codebase and technical documentation to transition complete ownership of your AI systems. We design for an in-house transition from Day One rather than at the end of our contract term.
You bring us the problem, we take a look at your data, your systems and your current tech stack and we come back to you within a few days with three to five things that we would build; how much they might cost; how long they might take to build and an honest analysis of whether it makes sense. If nothing makes sense then we tell you that and you leave, after having paid for a week of our time, not six months.
We pick one use case from the list and develop an MVP in your own data. It doesn’t run on sample data or artificial inputs; it works on your real documents, questions, and edge cases. We measure accuracy, speed, and inference costs to give you concrete figures to present to the organisation before approving the full investment. It’s a sprint of two to four weeks.
Proof of Concept validated the approach, now it is time to build the full version for your team and for your end-users, so we are talking about operational monitoring, exception handling, connection to your current stack and data sources, writing manuals to use the product, and implementing governance controls aligned to your tolerance for risk. Two months was our fastest turnaround time to go from discovery meeting to production. Typical projects go for 2-4 months given that each client’s current data sources are different.
Expectations for a 10k interactions are vastly different from what we would have set at 100. Models evolve, users uncover edge cases we had never considered, and pricing from the providers will change. That’s what we do; monitor, retrain, reduce costs, and alert you before it becomes your users’ issue. This can go on for as long as you want. Eventually we’ll hand over the reins for you to run this phase in-house when you’re ready.
You might also have a time when you want to manage everything yourself. We prepare each project with that idea of it eventually happening. We keep notes of architecture choices as we make them, rather than making them up later on. Our delivery includes training documentation and a recruitment guide. When you’re ready to hire in-house, the handover should be straightforward. If you would rather we stick around as a vendor, that is fine, but it needs to be your call, not something that is inevitable due to poor planning.
Your AI system will confidently provide the wrong answer at some stage. The only decision is whether you will hear it from your monitoring dashboard or from a disgruntled customer. We always deploy systems equipped with a working confidence calculator, drift detector and alerting service, so they know when things are going wrong the first time.
Frontier model APIs change often. In the last 18 months, major providers have shifted pricing, deprecated endpoints and reworked model families multiple times. When your AI product is tied to a single provider, each change forces you to rebuild the connection. On every single project, we implement a switching layer between the AI application and the model provider. It is a settings adjustment your team can handle independently to swap from GPT to Claude, or to move from a commercial API to a locally-run model with an open source licence.
We’re not tied to an exclusive partnership deal with OpenAI, Anthropic, AWS or any other company. If we suggest a certain model, cloud provider, etc. for your project, there’s no commission for it. The suggestion includes our rationale so your CTO can argue with it, test it against other suggestions or pass it on to another team when you decide to proceed further. Our AI software development stack is built around your requirements.
We select a vector database aligned with the data size, query responsiveness, and whether you already use Postgres, Elastic, etc. We ensure that each RAG system we implement incorporates permissioned access controls and source citations.
We've shipped production systems using OpenAI GPT-4, Anthropic Claude, Llama 3, Mistral and Qwen, matched to the workload. It all depends on the accuracy you are aiming for, whether it's okay to send data out of network and what your monthly inference costs are at a reasonable query volume.
LangChain and LlamaIndex are both great solutions for common use-cases. We only write custom pipelines when either one introduces more complexity than they solve, or when it doesn't enable the functionality your systems needs to handle. We don't use a technology just because it is popular.
Each and every one of the systems we deploy comes equipped with out-of-the-box evaluation, tracing, and alerting capabilities. We record what the AI does, the confidence it attaches to its responses, and any bottlenecks it encounters. In the event of any drift, your team will become aware of it well before your end users do.
We run your environment where your data is, and where your security and compliance frameworks require. Whether that’s AWS, Azure, Google Cloud Platform, on-premises, or any combination of those, we will configure it for you. If regulation requires UK data residency, everything runs in UK data centres by default.
Straight answers to the questions we hear in every initial conversation.
At Pixelfield we start our projects at £5,000 for an assessment workshop (where we'll evaluate your data, systems and business case). PoC and MVP work is typically £5,000 to £40,000, and enterprise rollouts with multiple integrations and compliance requirements go up to six and seven figures. Our main cost drivers are data complexity, number of integrations, compliance requirements, and whether we need to host your model on your own hardware or not. We scope projects in phases, so you're in control of spend and you get results in each phase.
An assessment workshop will take days to a week, whereas a Proof of Concept takes two weeks to a month on actual data. An MVP usually takes two to four months. Our fastest timeline from the first meeting to a working system was two months. This depends on how many systems have to be integrated, the state of your data and the governance approvals that need to be given.
We tell you. If your problem is better solved with a web application, a rules engine or standard automation, we will recommend that instead. Pixelfield builds applications, web platforms and custom software alongside AI, so we are not incentivised to push AI where it does not belong. You pay for the right solution, not for us to justify a technology.
No, our engineers take care of every aspect, including data preparation, model deployment, and ongoing monitoring. However, if you wish to develop skills within your own organisation, we offer support in hiring, training, and documentation. Most clients work with us initially, and once their requirements are satisfied, they can take over 12 to 18 months later.
You do. Code, trained models, data pipelines, documentation, infrastructure configuration. Full ownership, full access, no lock-in. If you want to move to another vendor or bring development in-house, everything transfers. We plan for that handover from day one.
AI systems need ongoing attention. Models drift as your data changes, usage patterns reveal edge cases, and providers update API pricing. Post-launch typically costs 15-25% of the original build annually for monitoring, retraining and infrastructure. We offer support retainers covering all of this with defined SLAs. If you prefer to bring it in-house, we help you hire and hand over. Either way, we scope the ongoing cost before the project starts so there are no surprises at month six.
Yes. We are vendor-neutral and build on whatever stack you already run. CRMs, ERPs, data warehouses, legacy APIs, third-party services. No platform migration required. If UK data residency or GDPR constraints apply, we configure the infrastructure to keep data within approved environments by default.
Yes. We start with an architecture and model quality audit. We look at the data pipeline, operational risk, inference costs and what is actually working versus what was oversold. Then we give you an honest assessment: what is salvageable, what needs rebuilding, and what the realistic cost and timeline look like. Some clients come to us after a failed PoC from another agency and leave with a working system inside three months.
