Operations and business leaders
Every job visible as it happens.
Get the right crew to the right site with the right parts, and prove what happened.
The service at a glance
Field teams run on phone calls, paper forms and end-of-day reports.
Nobody knows where technicians are, which jobs slipped, or what was actually done until the customer complains.
We give field teams a mobile workflow with live location, job dispatch, proof of work and AI-assisted reporting, and give managers a live map of everything.
Who it is for
Every job visible as it happens.
Dispatch by location and skill.
Accurate arrival times and proof.
More jobs per team per day.
The goal: an AI-ready organisation
Connected, clean, governed and allowed for AI use.
Agreed measures, one definition each, a named owner.
Roles, decision rights and skills to adopt AI safely.
Approach
Ride along and map the real workflow.
Deploy the mobile app to one team.
Turn on smart assignment and tracking.
Extend to all teams and regions.
Engagement options
Where time and jobs are lost.
Live dispatch and proof of work.
All teams, regions and contractors.
Scope
Bilytica frameworks behind this service
Autonomy tiers, tool boundaries and human checkpoints, so agents can act without becoming a liability.
See the frameworkVALUEFrom use-case long list to booked benefit, with unit economics attached at every step.
See the frameworkScheduling and dispatch driven by skills, SLAs, travel time and parts availability, with a mobile app for the crew: offline forms, photo evidence, guided checklists and automatic job closure. Vision models check the evidence photo so a job is not closed on a blurred picture of the wrong asset.
Scheduling is an optimisation problem
Skills, SLA windows, travel, shift rules and parts solved together beat any dispatcher's spreadsheet.
Evidence closes the loop
Geotagged, timestamped, vision-checked photos end the dispute about whether the visit happened as reported.
Offline is the normal case
Remote sites lose signal; the app queues everything and reconciles when it reconnects.
First-time fix is the number that matters
A second visit costs travel, parts, a customer's day and the SLA.
We inspect the systems, the data behind them and the quality of the records this capability will depend on, then write down what has to be fixed before anything is built.
One workflow, one team, real traffic, measured against the baseline we captured before we started.
Guardrails, approval gates, evaluation sets, logging and the evidence pack your auditor and your regulator will ask for.
Additional workflows, sites or languages, then runbooks and training so your team can operate it without us.
Great AI cannot hide bad data. These are the conditions this capability depends on, we fix them as part of delivery, not after go-live.