AI Development & Automation.
Give AI a useful job. Then make it dependable.
I develop AI-powered software around specific business tasks: finding answers in approved documents, turning unstructured information into usable records or helping a team complete a repeatable workflow. The starting point is a real task and a way to judge the result, rather than adding a chatbot because everyone else has one.
Explore your AI use case→
SYSTEM
Start with the task, the data and the risk
We identify who uses the system, what information it may access and what happens when an answer is wrong. An internal document assistant has different requirements from a workflow that updates customer records. That distinction shapes the interface, permissions, checks and level of human review.
Choose the smallest useful system
A document assistant may need retrieval from an approved knowledge base. A structured extraction task may need an LLM and validation rules. A fixed workflow may only need conventional automation. I assess these options before introducing an agent that can choose tools or take actions.
Make quality something we can inspect
We define representative tasks, expected results and examples the system should decline or hand to a person. The implementation is reviewed against those cases. Where useful, the interface shows supporting sources and gives users a way to report an unhelpful answer. Output quality, response time and usage cost are considered together.
Connect it to the work around it
A useful AI feature belongs inside a workflow. I can connect model APIs, document sources, databases and existing application features, with permissions and failure handling defined for the agreed use case. Actions with meaningful consequences should have explicit boundaries and suitable approval steps.
What you receive
The agreed AI feature or workflow, integration code, configuration notes, a documented evaluation set and a handover covering data flow and operating limits. Model, hosting and third-party charges are identified separately. Python, LLM APIs, retrieval systems and agent frameworks are selected where the task justifies them.
Where I draw the line
I do not promise perfect answers or unrestricted autonomous agents. Sensitive decisions need appropriate oversight. A successful engagement starts with a narrow, testable use case and expands when the evidence supports it.
Explore specialist services.
AI Workflow Automation & Agent Development
Connect the steps. Keep control of the actions.
Explore the serviceLLM Integration
Add summarisation, drafting, classification or structured extraction to existing software, with validation and sensible usage controls.
Explore the serviceMachine Learning Development
ML features and manufacturing analytics for predictive maintenance, quality monitoring and real-time production dashboards.
Explore the serviceRAG Development Services
Help people find answers in the knowledge you can stand behind.
Explore the serviceAI Chatbot Development Services
A conversation that helps someone reach the next useful step.
Explore the serviceBefore
we begin.
Can you add AI to my current software?+
Yes, subject to the access and integration options available. Share the application, the task you want to improve and the information the feature will need.
Will my business documents be used to train a model?+
That depends on the provider, product, configuration and agreement. Data handling must be reviewed before selecting the service; it should never be assumed from the word “AI.”
Tell me what
needs to change.
Describe one task, the information involved and what a useful result would look like.
Explore your AI use case