Custom AI Solutions

Off-the-shelf AI tools have improved dramatically, but they cannot cover every use case. Custom development makes sense when:

Your requirements are genuinely unusual. Standard products cannot adapt without awkward compromises.

You need tight integration. Your workflows depend on proprietary systems or business rules that off-the-shelf tools can’t respect.

Competitive advantage matters. You need capabilities competitors cannot buy in a settings panel.

Data handling constraints are strict. Security, privacy, or governance rules rule out shared platforms.

You want long-term ownership. You want control over behaviour and evolution, not dependency on a vendor roadmap.

Custom is not always the right answer. Sometimes standard products do the job well enough. We help you make an honest assessment before committing to custom development.

Our custom AI work spans a wide range of applications.

Conversational systems that go beyond what chatbot platforms offer: specialised domain knowledge, complex dialogue management, unique interaction patterns.

Document processing that extracts information from your specific document types: contracts, claims, applications, reports. Trained on your formats and terminology.

Decision support that analyses data and recommends actions based on your criteria: risk assessment, resource allocation, prioritisation, classification.

Process automation that handles workflows specific to your operations: approvals, routing, monitoring, exception handling.

Content generation that produces materials in your voice and style: reports, summaries, correspondence, descriptions.

Custom projects require thorough understanding before building begins.

Discovery and design explores your requirements in depth. We document what the system must do, how it fits your operations, and what success looks like. This phase prevents expensive mistakes later.

Architecture determines the technical approach: which AI capabilities to use, how to structure the system, how to handle data, how to integrate with existing technology. Good architecture enables future evolution.

Iterative development builds the solution in stages with regular review points. You see progress, can give feedback, and can adjust direction as understanding develops.

Testing and validation ensures the solution works correctly in real conditions. We test with real data, real scenarios, and users before considering the project complete.

Deployment and support puts the solution into production and ensures it keeps working. Custom systems need ongoing attention; we can provide this or transfer responsibility to your team.

We choose technology based on your requirements, not our preferences. Custom solutions often combine multiple AI capabilities: large language models for natural language understanding, machine learning for pattern recognition, traditional software for reliability.

Where possible, we use established platforms and frameworks. Custom does not mean building everything from scratch. It means assembling and extending components to fit your specific needs.

Custom development usually takes longer to deliver than standard products. This is unavoidable: building something new requires more effort than configuring something existing.

Timelines typically range from eight weeks for focused applications to six months or longer for complex systems. Investment varies accordingly.

We provide detailed estimates after discovery, once we understand what you need. Where possible, we agree fixed scope and clear change control to protect you from scope creep.

Custom solutions belong to you. We deliver full source code, documentation, and knowledge transfer. You can maintain the system yourself, engage us for ongoing support, or work with another provider.

This ownership gives you control. You are not locked into a vendor relationship or dependent on a third-party platform. Your AI capability is truly yours.

Use these prompts to pressure-test whether custom is warranted and what “good” looks like.

“What constraints make off-the-shelf tools fail for this use case, and what would a tailored system need to do differently?”

“What integrations and data access are required, and what governance controls must be in place?”

“What success metrics prove this is working: quality, adoption, time saved, and failure modes?”