Use cases by industry
For each use case: the job it does, the data it needs, what can go wrong and how we would check it, and where to go next.
Good first use cases are repetitive, data-heavy jobs where a mistake can be caught: document checks, staff questions over your own policies, support replies and triage. The right choice depends on the data you can reach, the cost of errors and whether a pilot beats the current process. These are examples to evaluate, not promised results.
Lots of documents, strict rules and a high cost of mistakes. Start with work that supports staff, not decisions about customers.
Reads identity and business documents, pulls out the fields and flags gaps for a person to review.
Answers staff questions from your own policies and circulars, with a link to the source paragraph.
Sorts incoming claims or disputes by type and urgency and drafts a first summary.
Projects by the organisations named, credited to their sources.
We focus on admin and documentation work. Anything that informs a clinical decision needs clinical and regulatory sign-off first.
Pulls key details from referral letters and forms into a draft record for staff to confirm.
Handles routine booking, rescheduling and opening-hours calls, and hands anything medical to staff.
Projects by the organisations named, credited to their sources.
Paperwork, quality records and machine data are good starting points, because the results are easy to check.
Extracts fields from invoices, certificates and inspection reports and checks them against your records.
Flags likely defects in product images so inspectors can focus on the doubtful ones.
Answers technicians’ questions from manuals and past work orders, with the page reference.
We have not found a well-documented public example for this sector yet. See the examples we do have.
Projects by the organisations named, credited to their sources.
High volumes of simple questions and product data make this a good place to test cost per task.
Answers order, return and delivery questions, and hands complaints and edge cases to a person.
Fills gaps and fixes inconsistencies in product titles, attributes and descriptions.
Projects by the organisations named, credited to their sources.
Teams here often already use AI tools. The work is making them reliable, affordable and safe at scale.
Answers product questions from your docs and past tickets, inside your app or help centre.
Tracks model and cloud spend per feature and per customer, and finds cheaper set-ups that keep quality.
Projects by the organisations named, credited to their sources.
AI in the Wild
Timing, integration, ROI and support.
AI helps with repetitive, data-heavy work in any industry. Do not assume your sector benefits by default. Start where you have data you can reach, a clear owner and a result you can measure.
It depends on scope, data access, integrations and review needs. After we look at the workflow, we estimate the prototype and the production system separately.
We first check the available APIs, access rights, system limits and how things fail. Then we plan the integration around your ERP, CRM or other systems. We never assume it will be easy.
There is no standard ROI figure. Compare a pilot with today’s cost, time, error rate and workload, and include model usage, integration, support and human review.
Yes, if we agree it in the scope: training, documentation, monitoring and support. Regulated workflows also need input from your own operations, security and legal people.
Tell us how the work runs today, where it gets stuck and what better would look like.
The examples above are summarised in our own words. Full details are on AI in the Wild.
Each project belongs to the organisation named. Each figure belongs to its source.
Summarised in our own words. The original reports and figures belong to their publishers.
