Bespoke AI development

Bespoke AI Development for Business Automation

I build bespoke AI tools that reduce repetitive work and fit around your existing systems. From processing documents to connecting business workflows, I develop practical solutions with clear controls, human review where needed, and maintainable code.

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Bespoke AI Development

AI development built around the way your business works.

Start with a business problem and a measurable goal. I can help you decide where AI adds value, where conventional automation is more reliable, and how the two should work together.

Automate repetitive workflows

Connect incoming enquiries, documents and business records to the next useful action. AI can interpret unstructured information while ordinary application logic handles validation, permissions and predictable rules.

Extend the software you already use

Add classification, extraction, summarisation or assisted writing to an existing application. Bespoke integrations can connect your website, CRM and internal tools without forcing your team into a separate workflow.

Build focused assistants and agents

Give your team a controlled way to find information or prepare tasks. Define which sources an assistant can read, which tools it can use and which actions require a person to approve them.

Keep people in control

Design review queues, useful activity logs and clear escalation routes into the product. Uncertain outputs should be checked, and important actions should have explicit limits rather than relying on an AI response alone.

Delivery and controls

A practical route from idea to a working AI tool.

The right solution depends on your data, existing software and tolerance for mistakes. I scope the smallest useful version first, with agreed acceptance criteria and visibility over ongoing costs.

Discovery and feasibility

Map the current process, identify bottlenecks and review representative inputs. Agree what success means, including time spent reviewing results, exception rates and the work that should remain manual.

Prototype with real examples

Check the proposed approach against realistic inputs before expanding it. Include incomplete records, ambiguous requests and unusual documents so the prototype reveals limitations early.

Integration and controlled rollout

Connect approved systems, add authentication and validation, and start with a limited workflow. Plan failure handling, cost limits and an easy route back to manual processing.

Handover and ongoing operation

Understand how to run the tool, review its outputs and change its configuration. Agree ownership of hosting, model usage, maintenance and future improvements before launch.

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Example project

Example project: turn incoming enquiries into reviewable next steps.

An illustrative workflow, rather than a client case study: a business receives free-text enquiries that staff currently read, categorise and copy into a CRM.

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Read and organise

Capture an enquiry, identify the requested service and extract useful details. Validate required fields before preparing a record.

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Review the proposal

Show a suggested category, draft response and missing information to a member of staff. Keep ambiguous requests in the review queue.

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Update approved systems

After approval, create or update the CRM record and assign the next task. Log the result and flag failed updates for attention.

Have a similar process?

Tell me what happens today, which systems are involved and what a useful improvement would look like.

Discuss your AI project

Bespoke AI Development FAQ

Frequently asked questions

It means building software around your process, data and existing tools. A project can combine an AI model with integrations, business rules, a review interface and monitoring. The scope is agreed around a useful outcome rather than adding AI to every step.

Cost depends on the workflow, integrations, data quality and review requirements. After an initial discussion, I can scope a first phase and explain the likely development, hosting, model usage and maintenance costs. There is no single price that fits every project.

Often, an existing model combined with clear instructions, your approved data and application logic is enough. Model training is only worth considering when there is a specific requirement and suitable data to justify the extra work.

Yes. AI features can be added to an existing Laravel application through APIs, background jobs and your existing authentication and permissions. I can also assess a standalone tool when that is a better fit.

We identify what data the workflow needs, who may access it and which providers would receive it. Retention, hosting, permissions and provider terms need to be reviewed for the proposed setup before sensitive information is connected.