The business problem
Assigning a field intervention is not simply about choosing the nearest technician. Skills, geographic coverage, schedules, unavailability, workload and operational constraints all need to be considered.
In FieldFlow AI, AI first structures and qualifies the request. Assignment is then driven by a deterministic and explainable business engine.
This separation matters: a generative model is excellent at interpreting free-form text, but it should not become the sole decision-maker for a critical operational choice.
Separating AI from business decisioning
The LLM is not given final responsibility for dispatch. It produces structured qualification together with confidence, key signals and readable reasons.
The scoring engine can then compare technicians using controllable rules: skills, distance, workload, availability, history, coverage area and SLA constraints.
This architecture makes decisions replayable, explains why a technician was selected and keeps behavior predictable even when the AI model changes.
Designing an explainable score
A useful score should not be just a number. It should expose a breakdown by criterion so operations teams can quickly understand the recommendation.
For example, a technician may score 92/100 with a breakdown showing perfect skill match, compatible coverage area, low workload, immediate availability and reasonable distance.
This level of detail improves business adoption and makes anomalies much easier to diagnose.
Securing automatic assignment
A recommendation computed at one point in time may become invalid seconds later. A technician can be assigned elsewhere, become unavailable or fall outside an allowed area.
Before automatic assignment, the backend should therefore revalidate critical constraints in a short transaction protected against concurrency conflicts.
The system can also define minimum confidence or score thresholds below which human validation remains mandatory.
Observability and continuous improvement
Decisions should be stored with AI qualification, final score, criteria used, selected technician and any human corrections.
That history can then be used to measure rule quality, qualification errors, human validation rates and differences between recommendations and final decisions.
AI becomes a measurable component of the system rather than an opaque black box.
