Telecom · Field-service optimization
Banao deploys constraint-based field-service scheduling for telecom operators — AI-driven dispatch that matches each job to the right technician, carrying the right parts, within the SLA window. The system eliminates the failed first visit by solving the assignment before the van leaves the depot.
It runs against your workforce management, inventory, and CRM data. We deliver a wired-in production system, not a scheduling tool that needs a full-time analyst to babysit.
Every failed dispatch has a cause: wrong skill, missing part, unrealistic window, or a job that should have been preventive. We address the cause, not the symptom.
Each open job is matched against technician certification, current location, parts on the van, and the contracted SLA window before the dispatch controller clicks approve. The system handles the combinatorics; the dispatcher handles exceptions.
The model reads historical job records and flags which assignments are likely to fail without a specific part or certification. High-risk jobs are pre-checked at scheduling time, not after a technician reports failure from site.
Field van stock, warehouse locations, and part lead times feed into every assignment. A job that requires a specific card or splice kit does not go to a technician who does not have it — or who would need a warehouse detour that breaks the SLA.
The scheduler surfaces which open jobs are approaching their SLA window with insufficient buffer, before they breach. Dispatch teams see a ranked exception list rather than discovering a breach after the customer calls to complain.
Asset health data and maintenance history trigger a proactive job before a site fails. The truck roll that prevents a fault costs less than the one that responds to it — and preserves the SLA record the reactive roll destroys.
First-visit completion rate, utilisation by region, SLA performance by job type, and parts-related failure rate — surfaced to field operations leads daily, so patterns are visible before they become a quarterly review problem.
Metrics shown dotted (··) are being finalised in our case-study metrics pack. The deployments are live; we publish numbers only once they are verified.
a European telecom operator
Dispatch was running on a workforce-management system that assigned by proximity only, ignoring skill and parts inventory. Banao built a constraint-based assignment layer over the existing WFM, processing open jobs each morning and re-ranking on SLA risk through the day.
Banao operates a ~300-person engineering business on its own AI products. InterviewGod handles our hiring pipeline. Vikaas runs our demand generation. We do not ship AI we have not already depended on ourselves.
A field-service scheduling system is unforgiving — a wrong assignment at 7am is a failed visit by noon, and a missed SLA by end of day. The version that reaches your dispatch floor has been built by engineers who run consequential AI daily, not ones who prototype it.
The wrong kind of dispatch optimisation creates paperwork for controllers and resentment from field engineers. We would rather tell you where it will not work than sell a system that gets switched off in quarter two.
We do not quote a scheduling system off a job description. We audit your actual dispatch data first.
We pull your last six to twelve months of job history and map where first-visit failures cluster — by job type, skill, part, or geography. You walk out with a failure root-cause breakdown, a constraint model design, and ROI maths — yours to keep whether you proceed or not. If you continue, the Sprint cost is credited against the build.
Data integration with your WFM, inventory, and CRM first — then the constraint assignment layer and the real-time SLA risk ranking. Integration into your existing dispatch workflow is part of the deliverable, not a follow-on project.
Live deployment with dispatcher override and a field operations dashboard. The model retrains on completed job outcomes each night, so first-visit rates improve over time rather than settling at launch performance.
What data does the scheduling model need to get started?
Historical job records from your WFM system — job type, assigned technician, skill codes, parts used, outcome, and SLA result. Six to twelve months is the target; less is workable if you have good coding discipline. The Discovery Sprint maps what you have and what it can support.
Does this replace our workforce management system?
No. Banao builds an assignment and optimisation layer that sits alongside your WFM and feeds decisions back into it. Your controllers keep the same screens — the system changes what the ranked list looks like before they confirm dispatch.
How does it handle complex shift rules and union constraints?
Shift limits, travel-time agreements, skill-grade restrictions, and overtime caps are modelled as hard constraints. The scheduler never proposes an assignment that breaks a work rule. Where rules are complex, the Discovery Sprint maps them before the constraint model is designed.
What is a realistic improvement in first-visit completion rates?
It depends on why your first visits are failing. Where parts mismatches or skill gaps are the primary cause — the addressable cases — constraint-based assignment typically recovers a meaningful share. The Discovery Sprint gives you an estimate grounded in your own job history, not an industry benchmark that may not match your job mix.
How long before something reaches the dispatch floor?
A typical path is a 2-week Discovery Sprint, a 6–8 week build, and a 2–4 week rollout with dispatcher training and sign-off. Banao's ~300-engineer bench means the build starts within days of Sprint completion — not after a months-long staffing or contracting delay.
In 45 minutes we can tell you which job types are driving your failed first visits, where the SLA risk concentrates, and what constraint-based scheduling would change.
Book a 45-min scoping call
