AI

For personal drafting, often yes, provided the account terms suit the information being used. Once a model handles organisational records, or its output feeds another system, access, logging and review need to be designed. That is where this work starts.

Yes. AI Remediation and Rescue covers systems where AI-generated development has left software that is hard to explain, test or change.

Language and document models are useful for extracting information from documents, classifying incoming requests, summarising material, searching internal knowledge and drafting content for review.

We start from the job: what goes in, what must come out and what a wrong answer costs. If fixed rules can do the work reliably, conventional software or automation is usually the better choice.

It depends on the provider's contractual and technical controls and on how the system is built. We design data access, permissions and logging around the sensitivity of the information.

Accuracy varies with the task and the material, so we test on representative examples from the organisation's own records and report how the system performs before it is relied on. Review steps cover the cases it gets wrong.

We work with hosted models from commercial providers and with self-operated models, and choose between them on capability, cost and controls. Where it is justified, the design keeps the provider replaceable.

Every system is designed with a plan for wrong output: validation checks, human review where the consequences justify it, logging so cases can be investigated, and fallback behaviour if a model or provider fails.

In the systems we build, a model handles defined steps such as reading or drafting, and people keep responsibility for decisions that matter. Review points are set by the cost of a wrong answer.

Book a free 15 minute discovery call and describe the job and the systems involved. We will say whether AI looks suitable or whether another approach would do the job better.