Bespoke AI Development & Integration

An AI project should begin with the task you want to improve. Who does it today? What information do they need? Where do delays or errors occur? What would a better result look like?

We work with your team to answer those questions before choosing a model or platform. Where an existing product meets the need, we can help evaluate and integrate it. Where your requirements call for custom software, we can build a tool around your data, processes and customer experience.

Internal knowledge tools

Help teams find and use information across approved documents, product information and internal guidance. Responses can include source references so users can check the information behind an answer.

Workflow automation

Extract and organise information from documents, classify incoming requests and prepare updates for review. Connect these steps with existing systems, with clear rules for approval and handling exceptions.

Customer service and clienteling support

Give service teams tools to retrieve relevant information, summarise enquiries and prepare responses for review. Shape the experience around your brand's tone and the judgement of the people delivering the service.

Product and content operations

Support tasks such as organising product attributes, checking content completeness and preparing draft descriptions. Keep editorial approval and product accuracy central to the workflow.

A useful AI tool needs access to the right information and a clear place in your operations. We can connect it with ecommerce platforms, content management systems, CRM, product information and other business applications, subject to their integration capabilities.

Our work brings together bespoke development and systems integration: interfaces your team can use, permissions appropriate to each role, and processes for reviewing output and investigating failures.

Related services: Bespoke Development, Systems Integrations and Technical Strategy.

1. Define the opportunity. Review the task, users, available data and success criteria. Identify where AI could help and where conventional software is a better fit.

2. Test with representative examples. Build a focused prototype and assess usefulness, accuracy, response time and operating cost. Include difficult cases as well as straightforward ones.

3. Build and integrate. Develop the interface and system connections, with appropriate access controls, review steps and error handling.

4. Introduce and improve. Support rollout, monitor performance against the agreed criteria and refine the tool as needs change.

AI output can be incomplete or incorrect. The level of checking and human approval should reflect the task and the consequences of a mistake.

During discovery, we agree which information the tool can access, who can use it, what it can do and where approval is required. Provider selection includes a review of relevant data-use terms, retention settings and hosting requirements. Testing and monitoring should cover the quality of the output as well as whether the software is running.