Client support that answers in seconds, day or night
A software company with a small support team
- 34% of conversations resolved without a person
- Seconds after-hours reply time, down from the next business day
- 8–12 hrs a week back
One small team handled every support request by hand, in business hours: platform administrators, learners on several client programs, and new sales inquiries. Most tickets took a minute to answer, a password reset or a missing certificate, yet each one pulled someone off real work. Anything sent after hours waited until the next business day.
An AI agent now answers from the company's own help content, matched to who is asking by the inbox or the page they write from. Routine requests, including name and email changes, are handled end to end, a level of automation the company chose. Anything complex goes to a person with the whole conversation attached.
The team still reads the conversations every day.
Stopping a wrong yes before it reaches the factory
A construction company with about 20 approval specialists
- 5 sources checked by hand for every request
- 800 written rules across 18 product groups
- 4 in 10 requests arrive too thin to act on
About 20 specialists approve custom orders by hand. Each request means checking five sources: around 800 rules written for experts, several calculators and a hardware chart. The same rule lives in two places and drifts out of step, rules change every couple of days, and the hardest cases live in the heads of a few veterans. A wrong yes sends an unbuildable order to the factory.
In the pilot, an AI checker reads each request, finds the rule that applies, and runs the calculators in code, so the AI never does the arithmetic. Every request comes back as OK, needs a closer look, or cannot build, with the rule and the reasoning shown. When a standard calculation fails, it offers the documented alternative instead of a flat no.
The pilot is being graded against past orders with agreed outcomes, and the measure that matters is how often it says yes when the answer was no. Anything unclear, and every case that needs a physical fit check, goes to a specialist.
A roadmap that only says "released" when it's true
A product company with a two-person docs team
- 30 of 30 released features linked to a help page
- 19 internal items decided once, never asked again
- 2 days to plan and build
The public roadmap was kept in sync by hand. Someone read the engineering team's planning document, which changed scope and order without warning, matched its items to features on the roadmap, and worked out what had moved. Nothing confirmed that an item marked done had actually shipped, and help links were often forgotten.
Now one command starts an update. An AI workflow compares the planning document with the last copy, strips out names, and asks every question it has in one numbered list. Before it marks anything released, it looks for real code changes in the product. A colleague approves every change on a private preview before it goes live. Engineering changed nothing about how they work.
Only real code changes count as proof of release, and the team can overrule any status.
Catching wrong instructions before customers do
A small team writing help pages for customers
- 5 errors found on live pages
- 9 source documents condensed into one guide
- 3 days to build
Every help page was checked by hand, against a long style guide and against other pages, and facts were hard to confirm against the product. Errors slipped through: field names the product didn't use, and a file size limit off by a factor of fifty. An earlier attempt at giving an AI the whole guide made the writing stiff.
The team rebuilt the guide in four parts: a short brief to write from, a review checklist, a reference, and a log of settled decisions. An AI writer gathers the facts from the product's code and drafts from the brief alone. Two separate AI reviewers then read the page, one as a newcomer and one against the checklist. A person still checks every page with the product open before it's published.
The writer uses the labels customers see and asks about anything it can't confirm.
Moving past administrative details during check-in meetings
A loans team at a financial services organization
- 63 active loan clients
- 2 hrs roughly, to write one client report by hand
- 1 place every statement will land
A lending team was looking after 63 active loan clients. Each client sent financial statements on their own schedule and through whatever channel suited them. Every review meant gathering the numbers and writing a report by hand, which took a couple of hours per client. With that much time going into paperwork, check-ins felt transactional to both sides.
We designed an AI analyst that files every statement in one structured record, charts the trends and writes a first draft of the coaching note. The loans lead edits the draft and decides what goes out. Reminders come from the organization's own email address and every step is logged. When a statement is late, the system suggests a personal check-in instead of another automated nudge.
The design is complete and the build is next.
Fewer hours finding gaps in business plans
A lender with two and a half advisors reviewing business plans
- 81–110 business plans a year
- 60–90 min per first read today
- 30 min target advisor time per plan
The advisory team receives 81 to 110 business plans a year. Each plan needs a full first read before anyone can discuss it, and that takes 60 to 90 minutes. Most of the time goes into finding what's missing or inconsistent, and the real assessment only starts after that.
We specified an AI first reader that goes through each plan as it arrives, checks it against the team's criteria, and flags gaps, weak assumptions and numbers that don't add up. Each flag points back to the page it came from. The advisor gets a short summary, and the assessment stays with them.
The specification is done and the build hasn't started. The goal is 30 minutes or less of advisor time per plan, and we're treating that as a target until it's measured. Their existing application portal may already handle part of the intake, so the build could turn out to be mostly configuration.
