AI development services
Artificial Intelligence
AI that does real work inside your business.
We take AI from idea to production: choosing the use cases that pay off, preparing the data, building assistants, agents and models, and putting the guardrails in place so people can rely on them.
01What we build
From strategy to agents in production.
Claude, GPT or your own private LLM, set up securely in your environment and built into assistants, agents and products.
AI strategy & consulting
Decide where AI pays off, and get the organisation ready for it.
AI strategy & roadmap
Where AI creates value in your business, in what order, and what it will take.
AI readiness assessment
Data, systems, skills and risk reviewed, with the gaps to close before you build.
Use-case discovery workshops
Working sessions with your teams to find, size and rank the use cases worth pursuing.
AI security & governance
Access control, data protection, guardrails and audit trails, so AI is used safely and within the rules.
AI training & enablement
Practical training so teams and leaders can use, question and manage AI well.
AI development & engineering
Build AI into production systems that people rely on.
Custom AI development
AI features and systems designed around a real task, built to production standards.
Machine learning & predictive models
Forecasting, scoring, classification and recommendations, trained on your data.
NLP, document AI & computer vision
Read documents, extract fields, and understand text and images.
AI integration
AI connected to Microsoft 365, Dynamics 365, SAP, your CRM and your own systems.
Systems integrationPrivate LLM setup & LLMOps
Claude, GPT, open models or your own, set up in your cloud or data centre, then evaluated, monitored and kept within cost.
Generative & agentic AI
Assistants and agents that understand, decide and act.
Generative AI applications
Assistants and copilots on large language models, built around a real job.
AI agents & agentic workflows
Agents that plan and take actions across your systems, with people in the loop.
Enterprise knowledge (RAG)
Answers grounded in your own documents and systems, with sources shown.
AI chatbots & virtual assistants
Customer and employee assistants on your website, WhatsApp and Microsoft Teams.
AI proofs of concept & MVPs
A working pilot that proves the value before you scale it.
02How we deliver AI
From pilot to production, safely.
Most AI projects stall between a promising demo and a system people trust. This is how we close that gap.
- 01
A use case worth solving
A real task, an owner, and agreed measures of success before anything is built.
- 02
Data that is ready
Sources, quality, access and privacy reviewed up front, not discovered late.
- 03
Evaluated before launch
Quality and failure cases tested against your own examples, with your team.
- 04
Guardrails and people
Permissions, sources shown, and a clear hand-off to a person when the AI is unsure.
- 05
Run, measure, improve
Usage, quality and cost tracked in production, so the system keeps earning its place.
03Problems this solves
Sound familiar?
- 01
Knowledge is trapped in documents and in people’s heads.
- 02
Teams spend hours answering the same questions.
- 03
AI pilots never make it into production.
04Built by us
Our own AI products.
Questions
Artificial Intelligence, answered.
Where should we start with AI?
With an AI readiness assessment or a use-case discovery workshop. Together we find where AI would pay off, check your data, systems and risks, and rank the use cases. Then we prove the best one with a working pilot before you scale it.
How do you take an AI project from pilot to production?
Start with one use case that has an owner and agreed measures of success. Check the data is ready. Evaluate quality against your own examples before launch. Add guardrails and a clear hand-off to people. Then track usage, quality and cost in production.
What is retrieval-augmented generation (RAG)?
RAG makes an AI model answer from your own content. The system first retrieves the relevant passages from your documents, policies or systems, then the model writes the answer from them and shows its sources. Use it when answers must reflect your organisation’s knowledge, not the model’s general training.
What is the difference between an AI assistant and an AI agent?
An assistant answers questions and drafts content when someone asks. An agent also takes actions across your systems, such as updating a record or routing a request, within the permissions you set, and hands off to a person when it should.
Still have a question?Ask Aira
Your AI initiative.Our intelligence.
Have a complex challenge, an ambitious product idea, or a process ready for reinvention? Let’s turn it into something real.