We’ve deployed production-level AI solutions for fintech, legal, and SaaS clients around the UK. Unlike others, Qubitron Labs builds custom models, LLM integrations, and AI automation. There are no demos. Every single project results in working software integrated with your systems.  

What Exactly is an AI Development Agency?  

AI development agencies create large-scale AI systems on-demand for clients needing custom ML, LLM integrations, computer vision, etc. Unlike other software agencies, Qubitron Labs includes all aspects of the AI stack, such as data engineering, model training, assessment, and deployment. Most software agencies can implement AI features, but we create systems that work for production environments.  

From startups to large enterprises, we’re transparent about the projects we take on, from rapid proofs of concept to production platforms.  

Our AI Development Services  

We take on any AI development project. Below are the three main service areas we deliver, often in combination within the same project.  

Custom AI Model Development  

Most off-the-shelf AI solutions are generic, and AI technologies are typically constructed on average datasets for average use cases. We craft proprietary models using your data, and these can range from a transactional fraud classifier to demand forecasting models or document classification aligned with your internal structures.  

Our engineers manage every step of the build including the data prep phase, building out the architecture, ML training, testing and evaluations with precision/recall metrics, and the final handoff to your infrastructure.  

Uses of ML:  

Supervised and unsupervised ML with deep learning and neural networks for complex pattern recognition.  

Building Demand forecasting, churn and fraud prediction, and dynamic pricing models.  

Building recommendation engines for e-commerce, media and SaaS.  

Building Computer vision for defect detection, OCR, and visual quality control.  

Building NLP models for sentiment analysis, document summarisation, and smart search.  

AI Strategy & Consulting  

We have found that the biggest error in AI has been building the solution without carving out the problem. From this standpoint, your only real question should be where does AI actually bring value to your business?  

We have conducted many clearly defined discovery sessions, mapped multiple data landscapes and put together a prioritised AI roadmap with value based estimates of AI implementation versus costs.  

AI readiness on assessment of your data and infrastructure  

AI use-case prioritisation: which problems to solve first  

Selection of the appropriate model and architecture  

Establishment of a value based cost of implementation  

AI roadmap on a phased implementation  

AI Integration into Existing Systems  

There are a few exceptions, but you most likely do not need to replace your current tech stack; you need to instil AI into it. We routinely offer AI functionality via LLMs and other forms of document automation and smart search as an Integrated Solution within your systems via APIs.  

We have layers of AI connected to CRMs, ERPs, customer support platforms, and internal tools. Our integration projects are designed to lower disruption: new AI capabilities will run next to your current tools and workflows until you decide to fully enable them.  

AI models embedded in web/mobile applications via API  

User-friendly, LLM (Large Language Model) driven features seamlessly integrated with existing CRMs, ERPs, and customer support tools.  

AI dashboards and reporting tools integrated on top of existing data.  

Automation of approval processes and document workflows.  

AI chat and virtual assistants seamlessly integrated in existing communication tools.  

Why do UK Businesses Partner with Qubitron Labs?   

 

Before we code a single line, we make sure each AI project is aligned with business goals. As a result, the first thing you may actually see is a short, technical document that describes the system to be built, as opposed to a working demo. With us, you will exactly know what you are paying for and the reasons it will work.  

Everything from data engineering to API integration and MLOps stays in-house. During your project, you will be in direct contact with the engineers that will be building your system. We aim to be as flexible as possible and have a variety of contracting models, including a dedicated team model. Pricing will be made clear in the initial contact. After the project is built, we will continue to work along side you by monitoring and maintaining updates to the system in order to adapt to your evolving needs.  

Generative AI & LLM Development  

As a generative AI development agency, Qubitron Labs invests significant resources beyond developing ChatGPT wrappers. Our partnerships span OpenAI, Anthropic, and both Llama and Mistral. We select the best model for your project considering the most sensitive data you plan to process, speed of results required, and the associated costs.  

A comprehensive technical stack for LLM integration in the UK is part of our expertise. This integration includes the construction of RAG systems (Retrieval-Augmented Generation), which helps LLMs ground their generated answers in a specified corpus or database to minimize the effect of LLM hallucinations. When generic model performance is insufficient, we also support the fine-tuning of base models to specific domains. Finally, we offer the design of AI agents to provide the capability for LLMs to undertake multi-step processes, call different APIs, and perform tasks fully autonomously.  

Our LangChain and LlamaIndex based orchestration models integrate memory and other tools to form reliable systems. All our deployments support a combination of prompt design, assessment, and system checks to ensure that results are not only contextually relevant, but also correct.  

Industries We Serve  

We design generative AI solutions in the competitive and high-impact sectors where the greatest value and most feature-rich solutions converge.  

Healthcare and life sciences  

Documentation and manual processes consume hours, if not days, of a clinical team’s time. We offer systems for AI driven predictive clinical reasoning, AI systems for processing clinical documentation, and AI systems for predicting modeling patient outcomes.  

Finance and fintech  

Intensely regulated and data-rich markets like fraud and credit risk are challenging. We offer systems for fraud detection, risk-based models for credit assessment, fully automated systems for compliance, and systems for analysis of trading risks.  

Retail and e-commerce  

Consumers and businesses have never had higher expectations, and profit margins have never been lower. We offer AI systems for recommendations, forecasting models for demand, and systems for optimizing inventory, as well as AI systems for automating customer service.  

Manufacturing & Supply Chain  

Scalability leads to a decrease in margins due to increased downtime and defect rates. We create predictive maintenance systems, computer vision defect detection tools, frameworks for production optimization, and tools to automate logistics.  

Legal & Professional Services  

The bottleneck caused by the manual reviewing of documents is particularly acute for high-value knowledge work. We develop tools for contract review, automate the document review process, and create smart systems for the management of knowledge.  

SaaS & Technology  

For B2B SaaS, the inclusion of AI features is rapidly becoming a standard. We deliver the integration of LLMs, the incorporation of smart search systems, and automation frameworks that enhance your product roadmap.  

We are also happy to take on projects outside these focus areas that present interesting data challenges.  

How We Work  

We adhere to a specific process throughout the five phases of each project. While tools and deliverables may differ between projects, the underlying methodology remains constant.  

1: Discovery & Problem Definition  

This phase includes the running of a structured discovery session that focuses on business objectives, workflow processes, data, and the definition of project success. The deliverables are a one-page problem statement and an outline of possible AI solutions.  

2: Data Assessment & Engineering  

Data is rarely clean and ready to be trained by a model. We undertake an audit of existing data to identify gaps and the design of data pipelines to automate the processes to clean, label, and structure data. We use both dbt and Airflow to develop data pipelines and maintain a log of all transformation decisions.  

3: Model Design & Development  

We develop the chosen model and validate it with testing that goes beyond accuracy and includes the metrics of precision, recall, and F1.  

4. Integration & Deployment  

The integration of the AI system into currently available applications is performed. A new interface can also be developed as part of the customization of the AI system. All deployments are version controlled, containerized, and include roll back procedures. Security and scalability are planned before the build, rather than added afterwards.  

5. Monitoring & Optimization  

Model performance is monitored through automated monitoring dashboards after deployment, and alerts are set for data drift. Data drift occurs when real world data diverges from the data used during model training. Retraining cycles and performance reviews are included in our support agreements.  

What is the Cost of AI Development in the UK?  

There are many factors at play, but as a result of our experience, we are able to set realistic benchmarks for customers when they are considering costs for AI Development in the UK.  

Proof of Concept  

£8000 - £25000. A focused build typically lasting between four and eight weeks to validate a hypothesis using actual data.  

Integration Project  

£20000 - £60000. A focused build lasting between six and sixteen weeks to incorporate AI functionality into an existing product or workflow.  

Full Custom AI Platform  

£60000 - £200000+. A build typically lasting between four and nine months to design and develop a fully functional AI platform that is production ready.  

Data Quality and Availability  

Clean, labeled data reduces costs. Messy, sparse data increases data engineering costs.  

Model Complexity  

Large language models (LLM) require fine-tuning for costs to be comparable to baseline models for other types of AI.  

Depth of Integration  

Integration of one API will always be less expensive than building multiple enterprise-level integrations.  

Compliance  

Regulated industries such as finance and healthcare add significant costs due to required security and auditing.  

Scope of Support  

Monitoring and retraining and new features and enhancements come at an additional cost.  

Our free discovery session leads to a fixed-price estimate, and there is no obligation to proceed.