Skip to content
← All blueprints
BP-015Operations

AI Field Service Dispatcher

Match the right technician to every job and optimize routes in real time.

Project range
$7,000–15,000
AWS running cost
$70–320/mo
Time to deploy
4–6 weeks
Best-fit industries
HVAC, Plumbing

Executive summary

A dispatch brain that assigns each work order to the best-suited, best-located technician and continuously re-optimizes the day as new jobs, cancellations, and delays arrive. It weighs skills, parts, SLAs, and drive time, keeps customers informed with accurate ETAs, and turns a whiteboard scramble into a data-driven schedule.

Business problem

Dispatching by hand is reactive and error-prone: technicians are sent without the right skills or parts, routes zig-zag across town, and emergencies upend the plan. The result is wasted windshield time, missed SLAs, and frustrated customers waiting on vague ETAs.

Architecture

AWS services

Amazon API Gateway

Networking
  • Work-order intake and status endpoint
  • Rate limiting

AWS Lambda

Compute
  • Run assignment and re-optimization logic
  • Coordinate notifications and updates

Amazon Bedrock

AI / ML
  • Triage incoming requests by urgency and type
  • Summarize job context for technicians

Amazon Location Service

Networking
  • Routing, drive-time, and accurate ETA calculation

Assignment engine

Compute
  • Match skills, parts, and SLA to the right tech
  • Re-sequence the day as conditions change

Amazon DynamoDB

Database
  • Job, technician, and schedule state
  • Real-time availability

Amazon EventBridge

Messaging
  • Propagate schedule changes and dispatch events

FSM / SMS adapters

Integration
  • Sync to field-service software and the tech mobile app
  • Send customer ETA texts

Amazon CloudWatch

Observability
  • Logs, metrics, and cost alarms

Data flow

  1. 1

    A work order arrives via call, form, or FSM; Bedrock triages type and urgency.

  2. 2

    The assignment engine matches required skills, parts, and SLA to the nearest suitable technician using Location Service drive-times.

  3. 3

    The schedule is written to DynamoDB and dispatched to the technician app; the customer gets an accurate ETA by SMS.

  4. 4

    New jobs, cancellations, or delays trigger EventBridge, and the day is re-optimized on the fly.

  5. 5

    Managers see live status and exceptions, intervening only when needed.

Security considerations

  • Customer and technician location data encrypted and access-controlled.
  • Least-privilege IAM; FSM and SMS credentials in Secrets Manager.
  • Assignment logic is transparent and overridable by dispatchers.
  • Audit trail of every assignment and reschedule decision.

Cost considerations

  • Location Service requests and Bedrock triage are the main variable costs.
  • DynamoDB, Lambda, and EventBridge are pay-per-use and cheap at idle.
  • Route optimization reduces fuel and overtime — often the larger real-world saving.

Scalability

  • Serverless throughout; scales from a few trucks to a large fleet.
  • Assignment weights (skills, SLA, distance) are configurable per business.
  • Additional FSM, telematics, or messaging systems attach as adapters.

Deployment roadmap

Phase 1 — Model the operation

Weeks 1–2
  • Capture skills, SLAs, parts, and territories
  • Provision AWS foundation and connect the FSM

Phase 2 — Build & integrate

Weeks 3–5
  • Build the assignment and routing engine
  • Wire the tech app, SMS, and dispatch board

Phase 3 — Pilot & tune

Week 6
  • Pilot with one crew or region
  • Tune weights for jobs-per-day and first-time-fix

Future enhancements

  • Predictive parts pre-loading based on job type.
  • Demand forecasting to guide staffing and on-call.
  • Automated post-visit follow-up and review requests.
  • Fleet utilization analytics into the executive dashboard.