Custom AI, built around how your business works
Off-the-shelf AI tools can’t solve problems that are specific to you. We design and build intelligent systems — LLM assistants, computer vision, predictive models — that fit your data and your workflows, and get a production-ready AI MVP into your hands in 30 days.
All servicesAI that fits your business, not the other way round
Generic tools get you part of the way. Custom AI is engineered for your industry, your data and your objectives — and it plugs into the infrastructure you already run.
- Models trained and tuned on your own data and terminology
- Clear milestones, deliverables and success metrics from day one
- GPU provisioning, hosting and APIs handled for you
See something working in weeks, not quarters
We work iteratively, so you can see and validate results early — reducing risk and keeping the build aligned with the outcome you actually want.
- Proof of concept in as little as two to four weeks
- Production-ready MVP with auth, dashboard and monitoring
- Fixed, itemised quote before any work begins
Custom AI solutions, end to end
From language models to computer vision, we build the AI that automates processes, sharpens decisions and opens up new revenue.
Custom LLM development & fine-tuning
We turn general-purpose language models into specialists that know your terminology, your documents and your workflows — using fine-tuning, retrieval-augmented generation (RAG) and careful prompt engineering.
Typical uses: Document processing, support assistants, knowledge bases, content generation.
Conversational AI & voice
Chat and voice assistants that handle real conversations: multi-language, context-aware, and connected to the systems your team already uses.
Typical uses: 24/7 support, sales qualification, internal help desks, voice-activated tools.
Computer vision
Models that read images and video — object detection, OCR, quality inspection and video analytics — running fast enough to be useful in the real world.
Typical uses: Quality control, retail analytics, security, medical image analysis.
Predictive analytics & machine learning
Forecasting, classification, anomaly detection and recommendation models that turn the data you already have into decisions you can act on.
Typical uses: Demand forecasting, risk scoring, churn prediction, process optimisation.
Natural language processing
Extract meaning from unstructured text at scale — sentiment, entities, classification and information extraction across your documents and customer feedback.
Typical uses: Feedback analysis, document workflows, content moderation, market intelligence.
AI integration & deployment
The part that decides whether a model ever gets used: APIs, cloud or on-premise deployment, legacy-system integration and CI/CD pipelines that fit your stack.
Typical uses: CRM and ERP integration, multi-platform rollout, secure, scalable infrastructure.
AI research & prototyping
For problems that have no off-the-shelf answer, we run focused feasibility studies and build working prototypes so you know what is possible before you commit to a build.
Typical uses: Feasibility studies, proofs of concept, novel applications, competitive differentiation.
A structured, transparent AI process
Six stages that take you from AI idea to a system your team relies on.
Discovery & strategy
Workshops to understand the problem, audit your systems and data, and pick the AI opportunities with the best return. You leave with success metrics, a roadmap and a clear scope.
Data preparation
We assess data quality, clean and structure what you have, and set up secure pipelines — with privacy and access rules decided up front, not bolted on.
Model development
We build, fine-tune or assemble the models, RAG systems and pipelines the job needs, and benchmark them against the success metrics from step one.
Integration & interface
Dashboards, APIs and connections into your CRM, calendar and internal systems, with authentication and access controls in place from the start.
Testing & validation
Accuracy and performance validation, security testing and user acceptance testing — so what ships has been proven on real inputs.
Launch & optimise
Staged rollout, team training, monitoring and alerting. After launch we track accuracy, retrain on new data and keep improving what the system does.
Pick the engagement that fits your stage
Every project starts with a free scoping call and ends discovery with a fixed, itemised quote — no surprises. Timelines are indicative and depend on scope, data readiness and integrations.
AI Proof of Concept
Prove one core AI workflow works on your real data before you commit to a full build.
- One focused workflow, validated
- API-based models (GPT, Claude and others)
- Working demo on your own data
- Feasibility report and scaling roadmap
AI MVP
A production-ready first version your team and customers can actually use.
- Production-ready application
- RAG over your own data
- One or two integrations (CRM, calendar…)
- Authentication, dashboard and monitoring
- Post-launch support included
Enterprise AI System
For complex, regulated or large-scale deployments with deeper integration needs.
- Fine-tuned or self-hosted models
- Multi-agent workflows
- Compliance and data-residency requirements
- Deep enterprise integrations
- SLA and ongoing optimisation
What makes our AI work different
Built for your industry
No one-size-fits-all models. We design around your sector, your data and your constraints — whether that is healthcare, finance, retail, logistics, legal or manufacturing.
Full-cycle delivery
Strategy, data, models, integration, launch and optimisation under one roof, so nothing gets lost between a consultant, a data team and a developer.
Tool-agnostic engineering
We pick the right model and framework for the job — OpenAI, Claude, Llama, open-source or your own — instead of forcing a favourite stack onto your problem.
Security by default
Encryption in transit and at rest, access controls and audit trails as standard — plus private cloud or fully on-premise deployment when your data cannot leave the building.
Not sure AI is the answer? Prove it first.
A proof of concept validates the technical feasibility, shows stakeholders the value and sharpens the requirements — before you commit to a full build.
Custom AI Development — your questions, answered
What is custom AI solution development?
It is building artificial intelligence around your specific business — your data, terminology, workflows and systems — rather than adapting a generic off-the-shelf tool. That can mean a fine-tuned language model, a RAG assistant over your documents, a computer vision pipeline, a forecasting model, or a combination of them.
How long does it take to build a custom AI solution?
It depends on scope. A focused proof of concept typically takes two to four weeks, a production-ready AI MVP four to eight, and a complex enterprise system eight to sixteen. We aim to put something working in front of you early, so you can validate results long before the final build.
How much does custom AI development cost?
Cost depends on the problem, how ready your data is and how many systems we need to integrate. After a free scoping call we give you a fixed, itemised quote with milestones — so you know the number before any work starts. A proof of concept is the lowest-risk way to begin.
Do we need AI expertise in-house?
No. We handle strategy, data preparation, model development and deployment, then train your team to run and maintain the system. You do not need AI engineers on staff to benefit from it.
What is the difference between custom AI and pre-built AI tools?
Pre-built tools are designed for the average user, so they often get you most of the way and then stall on your terminology, data structures and processes. Custom AI is built for your exact use case, which means better accuracy, deeper integration and a clearer return on investment.
Can you integrate AI with our existing systems?
Yes — integration is a core part of every build. We connect to CRMs, ERPs, databases, legacy platforms and cloud services through APIs and webhooks, so the AI works inside the tools your team already uses.
What data do we need to get started?
It depends on the use case, but generally historical data relevant to the problem: documents, images, customer conversations, transactions or sensor readings. We assess the quality and quantity of what you have during discovery, and help you plan collection and preparation if there are gaps.
How do you keep our data secure?
We use encryption at rest and in transit, strict access controls and secure infrastructure, and we design to the privacy and compliance requirements that apply to you, such as GDPR or HIPAA-aligned workflows. Where data must stay in-house, we can deploy entirely on-premise.
What technologies do you work with?
We are tool-agnostic: OpenAI GPT models, Claude, Llama, TensorFlow, PyTorch, Hugging Face, LangChain and the major cloud AI platforms on AWS, Google Cloud and Azure. We choose what fits your problem, budget and hosting needs.
What support do you offer after launch?
Every build includes post-launch support, and many clients stay on a plan covering monitoring, retraining, performance tuning and new features. We also hand over documentation and training so your team is never dependent on us to keep the system running.
Ready to put AI to work in your business?
Tell us the problem you want to solve — we’ll come back with a clear plan, a realistic timeline and a fixed quote.