Find the signal.
Fix the problem.

Introduction

Field service teams often fix the same fault again and again, one ticket at a time. This film shows how FaultFlow turns one engineer's fix into the whole team's starting point, online and offline. Everything you'll see uses demo data.

Mia · Connecting your systems

Tickets reach FaultFlow from the service desk you already use, such as Jira or Zoho Desk. Build records come in from your ERP, such as NetSuite.

Mia · Logging a ticket

Your team can also log a ticket directly in FaultFlow. They pick the instrument and describe what's wrong in their own words. The ticket is raised straight away, ready to assign.

Mia · A familiar fault, and its history

Mia runs the service team, and a familiar fault has just come in. A customer's microscope keeps losing light through its fibre during runs. She opens the instrument's full history: every part and batch it was built with, and its factory test results. Its fibre coupling came from lot 2231.

Mia · The pattern across the fleet

Because FaultFlow links every ticket to how its instrument was built, a pattern stands out. All six instruments built with lot 2231 have an open ticket, against sixty-two percent across the fleet. Between the factory and the customer site, their coupling efficiency dropped by two to four points. And in six earlier investigations of this problem, the fibre coupling was the fix four times. That's a strong lead to test, not a diagnosis.

Mia · AI that shows its sources

Mia asks the AI assistant what coupling efficiency the instrument should reach, and how to restore it. The answer draws on the troubleshooting guide and on past cases, and makes clear which is which. It names its source, so she can check it.

Mia · A procedure drafted by AI, checked by her

She then asks the AI to draft a troubleshooting procedure from the manuals. It lists the likely causes, with a test to confirm or rule out each one. Mia checks every step, and adds the pass mark from the build manual. She adds a check her team learned the hard way. Then she saves it for tickets that already point to the fibre coupling. Teams can also build their own procedures from scratch.

Mia · Asking Sam

Then she asks Sam, who's visiting the customer tomorrow, to start with the fibre coupling. The question stays open until he answers. Sam gets the request by email too. It shows who's asking, what they need, and which instrument it's about.

Sam · Offline at the customer site

Sam uses the FaultFlow app on his Windows laptop. Before he left, it downloaded everything for the instruments he's looking after. At the customer site, the connection drops. The app shows it's working offline. Offline, he still has everything he needs: the instrument and its ticket. Mia's question is there too, and he answers it on the spot.

Sam · AI on the laptop

He asks the laptop's own AI assistant the same kind of question. It runs entirely on the laptop, and reads only the manuals stored there. It names its sources. He opens the manual to check the figure for himself.

Sam · Finding the fault

He starts troubleshooting with the same fishbone diagram as the web app. He tests the light source first. He attaches the power reading. It isn't the cause, so he rules it out. Then he checks the fibre coupling, and that's the fault. Everything is saved on the laptop, ready to send when he's back online. Back in the van, the laptop reconnects and sends everything.

Mia · From fix to guide

Back at the office, Mia sees the ticket resolved, with Sam's answer, the faulty part and the evidence. And the pattern is now a confirmed finding: lot 2231 has a real fault. The other instruments built with it are flagged, along with their open tickets. Sam's fix now counts too: the fibre coupling has fixed this problem five times out of seven. From that evidence, Mia creates a guide, with the checks in the order that works best and the numbers behind each one. AI tidies up the wording. She checks it against the sources before publishing. The AI helps with the words. The facts come from real investigations.

Priya · The next engineer

The following week, Priya gets a call about another instrument from the same lot. She logs it in her own words: the signal fades partway through a run. She doesn't have to match anyone else's wording. FaultFlow recognises it as the same problem, and opens the procedure Mia wrote for it first. The guide Mia published is right there too, with the checks in the order that works. The general fishbone is still one click away. Its star marks the check that fixed this before on this lot: the fibre coupling. Priya tests it on her own instrument, and it's the fix. And her fix makes the next suggestion stronger.

Everything else a team needs

Around all of this is what a team needs day to day. Everyone gets a home page with their next steps. Managers get a dashboard of tickets and trends across the fleet. Admins manage people, teams and roles, so each person sees and changes only what they should. Every change is recorded in an audit log. And admins can see every laptop set up to work offline.

Summary

Patterns found in the data, not in someone's memory. Fixes recorded on site, with or without a connection. Knowledge that grows with every ticket. That's FaultFlow. This is a first look at FaultFlow. If you lead service, engineering or operations for complex instruments, let's talk about what this could look like for your team.