AI Development Services

The right approach depends on what your AI system needs to do, what data you have, and what constraints you're operating under. RAG (retrieval-augmented generation): if you need to answer questions from your existing documents, knowledge base, or data, without training a model. AI agents: if you need to automate multi-step workflows where the AI needs to use tools, make decisions, and adapt to intermediate results. Fine-tuning: if you have a specific, narrow task and a labelled dataset, and a general model's accuracy isn't sufficient. Custom ML: if you have a prediction or classification problem, labelled historical data, and need a model trained on your specific data. We diagnose the right approach in a scoping session before recommending a build.

No. We use OpenAI (GPT-4o, GPT-4o mini), Anthropic (Claude 3.5 Sonnet, Claude 3.7), Google (Gemini 1.5 Pro, Gemini 2.0), Meta (Llama 3), and open-source models depending on what's right for the use case. Model selection is driven by performance on your task, cost at your volume, data residency requirements, and latency constraints. We have production experience with all the major frontier models and will tell you the trade-offs honestly, including when a cheaper or open-source model is a better fit than the most capable frontier model.

An AI proof of concept (POC) is a time-boxed build that answers a specific technical question: does this approach work on our data, with acceptable quality, at feasible cost? POCs make sense when: the task is novel enough that there's genuine uncertainty about whether AI can do it well; the data quality or availability is unknown; or there are compliance, latency, or cost requirements that need to be validated before a full build. We run focused 2-4 week POCs with a defined success criterion. If the POC succeeds, we scope the production build. If it doesn't, you've spent a fraction of what a failed production build would cost.

Quality in production requires evaluation infrastructure, not just good prompts. We build: evaluation datasets representing real query distribution, automated quality scoring using LLM-as-judge for qualitative outputs, regression testing to catch quality degradation when prompts or models change, production monitoring for output quality metrics over time, and human review queues for flagged outputs. Quality evaluation is not optional for production AI systems: it's the only way to know if the system is working.

Costs range significantly by scope. An AI proof of concept runs $8,000 to $20,000 for a 2-4 week focused investigation. A production AI feature integrated into an existing product runs $25,000 to $75,000. A standalone AI application with RAG, evaluation, and monitoring runs $50,000 to $150,000. A complex multi-agent system or custom ML pipeline runs $100,000 to $300,000+. We provide fixed-cost proposals after a scoping session, not hourly estimates that shift as scope changes.

We have shipped AI products for healthcare (HIPAA-compliant remote patient monitoring), financial services (fraud detection, document extraction for lending), logistics (route optimisation, demand forecasting), retail (recommendation engines, dynamic pricing), and legal (contract review, document analysis). Most AI approaches — RAG, agents, ML classifiers — translate across industries because the underlying engineering patterns are similar. The domain knowledge that matters is understanding your data, your compliance constraints, and what good output looks like in your context.