AI Development
AI that works in production, not just in demos
We design and build AI agents, knowledge assistants, copilots and custom models — wired into your data and product, measured with evaluations, and protected with guardrails.
What we build
AI solutions for real business problems
Every engagement starts from a workflow worth improving — then we pick the simplest AI approach that measurably does the job.
AI Agents
Autonomous agents that plan, call tools, and complete multi-step workflows — support, research, and back-office automation that runs itself.
Custom AI Models
Fine-tuned and purpose-built models (LLMs, vision, speech) trained on your data and deployed to your own cloud or on-prem.
RAG & Knowledge Assistants
Retrieval-augmented chat over your documents and databases — grounded, cited answers with guardrails and access control.
Computer Vision
Image and video understanding: detection, OCR, quality inspection, and visual search built into your product or pipeline.
Predictive ML & Analytics
Forecasting, recommendations, and anomaly detection that turn your historical data into forward-looking decisions.
AI Copilots & Automation
In-product copilots and workflow automations that remove busywork, draft content, and accelerate every team.
Use cases
Where AI is paying off right now
The fastest wins come from repetitive, information-heavy work. A few examples by industry:
Customer support
Agents that resolve tickets end-to-end, look up orders and escalate with full context.
Sales & marketing
Lead research, personalised outreach drafts and CRM updates that write themselves.
Healthcare
Document intake, summarisation and coding assistance with audit trails and PHI controls.
Real estate
Listing descriptions, buyer Q&A over property data and lead qualification.
E-commerce
Product search that understands intent, recommendations and catalogue enrichment.
Logistics & operations
Invoice and document extraction, exception handling and demand forecasting.
How we deliver
From idea to reliable AI in four steps
Measured at every stage, so you know what you are getting before you scale it.
Use-case discovery
We map your workflows, data and goals, then score AI opportunities by value, feasibility and risk — so you build the one that pays back first.
Prototype with evals
A working prototype on your real data in weeks, measured against an evaluation set we agree up front — accuracy is a number, not a feeling.
Production hardening
Guardrails, access control, prompt-injection defences, cost controls, fallbacks and human review where it matters — then integration into your product.
Monitor & improve
Tracing, quality dashboards and feedback loops keep the system accurate as models, data and usage change.
Models & tooling we build with
AI insights
What's changing in AI — and what to do about it
Practical guides from our team on the latest in AI, the skills worth learning and how to put AI to work.
FAQ
AI development questions, answered
What does an AI development company actually build?
Software that uses AI models to do useful work inside your business — for example AI agents that complete multi-step tasks, chat assistants grounded in your documents (RAG), copilots inside your product, document and image processing, and forecasting models. The model is only one part; most of the work is data access, integration, evaluation, security and the user experience around it.
Should we use RAG or fine-tune a model?
Most business use cases start with retrieval-augmented generation (RAG): it keeps answers grounded in your current data, cites sources and is cheaper to update. Fine-tuning helps when you need a consistent style or format, or a specialised task at lower cost and latency. We often combine both after measuring what each adds.
Which AI models do you work with?
We are model-agnostic: OpenAI, Anthropic Claude, Google Gemini and open-weight models such as Llama, Qwen or Mistral, hosted by the provider or in your own cloud. We choose per use case based on quality on your evaluation set, cost, latency and data-residency requirements.
How do you keep our data secure?
Data stays in your cloud or with providers under enterprise terms that exclude training on your data. We apply role-based access to retrieval, redact sensitive fields, defend against prompt injection, log every model call and add human approval for high-impact actions.
How long does an AI project take?
A focused prototype on real data typically takes a few weeks; a production rollout with integrations, guardrails and monitoring usually takes one to three months depending on scope. We agree success metrics before building so progress is measurable.
Can you add AI to our existing app or website?
Yes. Most of our AI work is integrated into existing web and mobile products — an assistant in your dashboard, smart search, automated workflows in your back office or an AI feature in your mobile app — without rebuilding what already works.
Have a workflow AI should be doing?
Tell us about it. We'll come back within 24 hours with how we'd approach it and what to measure.