AI development & automation

Problems this addresses

• A task involves reading, classifying or extracting information from documents or text that varies too much for fixed rules alone.
• Manual review of routine documents consumes time that could go toward exceptions and judgement calls.
• You want to explore where AI could help, but need a clear view of what it can and can't be trusted to do unsupervised.

Examples of what this can include

• Document data extraction with a human review step before data is accepted
• Text classification or routing — for example, sorting enquiries or requests
• Drafting assistance reviewed by a person before use
• Automation of repetitive multi-step tasks between existing systems
• Simple rule-based automation where AI isn't the right tool, evaluated honestly alongside AI options

Our Document Review demo is an illustrative example of a reviewed extraction workflow. It uses a predefined result rather than a live AI service, so the review experience can be explored without depending on one — our initial illustrative example is document preparation for bookkeeping and accounting firms, but the same pattern applies more broadly.

Scope and deliverables

Agreed upfront: the specific task the AI-assisted step targets, what "correct" looks like for that task, and where human review sits in the workflow.

Defined together during scoping: acceptable error handling, the review interface itself, and how edge cases are flagged rather than silently accepted.

Process for this service

The same definition, design, build-and-verify, and operation stages described in our approach apply here, with an explicit evaluation stage that compares AI-assisted, simpler rule-based and manual options before committing to build anything.

Integrations and constraints we evaluate

• Where the documents or text actually originate, and in what format.
• Data sensitivity — what can be processed, by which provider, and what must stay restricted.
• Volume and variability of inputs, which affects both feasibility and cost.
• Acceptable error rates and the consequences of an incorrect result going unreviewed.

What affects budget and timeline

These factors shape the scope of a quote — we don't set prices or delivery dates without reviewing your specific project.

• The volume and variability of the documents or text involved.
• Whether a general-purpose AI service is adequate or a more tailored approach is needed.
• The complexity of the human-review interface required.
• Data-handling and confidentiality requirements specific to your sector.