Client work is confidential, so the scenarios below are illustrative composites rather than named engagements. What is real is the structure: what got scoped, what got ruled out, and how the result was measured.
These are composite scenarios used to show how we scope work. They are not client testimonials, and no performance figures are claimed. Named references are available under NDA during an engagement discussion.
Inspection notes lived in free text, so recurring defect causes were invisible until a customer complained.
Classification of existing inspection notes into defect categories, with a supervisor confirming any low-confidence label.
Automated line-stop decisions. The cost of a false positive was higher than the cost of the manual check it replaced.
Time from defect occurrence to identified root cause, compared against a four-week baseline.
Senior staff spent hours reassembling past proposals, and the reuse depended on who remembered which document.
Retrieval across approved past proposals with a drafted first section, always edited by the owning partner before sending.
Automatic pricing generation. Pricing depended on judgment the documents did not contain.
Hours to first complete draft, and the share of drafts partners kept rather than rewrote.
Incoming faxes and scans were sorted by hand, creating a queue that grew every Monday.
Document type classification and routing only, with all clinical content left to staff review.
Any extraction of clinical values. The accuracy and audit requirements exceeded what the workflow could verify.
Queue age at end of day and rework rate on misrouted documents.
Dispatchers rewrote the same explanatory messages dozens of times a day during delays.
Drafted customer notifications from delay reason codes, sent only after dispatcher approval.
Autonomous rerouting. The systems of record could not expose live constraints reliably enough to trust.
Notifications sent per hour and inbound status calls per hundred shipments.
Bring one workflow to a call. We will walk the same structure against your process and tell you where AI would and would not help.