Fleet Intelligence Dashboard.

The problem

20/20 Onsite operates a fleet of mobile optometric clinic units serving clinical trial sites, where fleet downtime directly reduces billable capacity. The fleet data owner starts each day scanning Slack alert channels (~40 people posting), manually deciding what needs action, and hand-updating a Fleet Status Snapshot spreadsheet.

Maintenance is purely reactive — zero scheduled preventive downtime — so vehicles break mid-week and collapse clinic schedules. Whiparound defect reports go into an "abyss" with no closure, Slack is the de facto source of truth for 40 people but isn't analyzed, and there's no unified view of vehicle health versus equipment health. Reactive firefighting consumes 8-12 hours a week.

The solution

The Fleet Intelligence Dashboard is a daily AI operations system that unifies telemetry, generator data, work-order history, and maintenance schedules into one dashboard that scores vehicle health and proposes prioritized recommendations. The broader aim is moving the fleet from reactive maintenance to predictive visibility, forecasting service windows 2-4 weeks ahead.

It is decision-support, never decision-replacement: every recommendation goes through human approval and is logged for auditability, with a permanent UI disclosure and 16 human verification checkpoints. The design extends the fleet owner's existing daily-maintained snapshot rather than replacing it, lowering adoption risk, and targets cutting reactive firefighting from 8-12 hours a week to 2 or fewer.

How it works

A daily pipeline reads the Fleet Status Snapshot, Whiparound, Azuga, and Slack, then writes a state file the dashboard loads. Claude Sonnet 4.5 handles routine forecasts and Claude Opus 4.7 handles batch multi-vehicle analysis, returning structured JSON with a risk score (1-10), urgency, predicted window, confidence, reasoning, acknowledged blind spots, and recommended action. A YAML-structured prompt enforces calibration rules — confidence capped when service history is thin, manual inspection recommended below 60% confidence, and no prediction of random failures like tire blowouts.

Application logic, not the model, assigns statuses, and a RAG knowledge base over manufacturer manuals and Slack fixes answers troubleshooting queries with source citations. A manual backtest over 90 days across 11 vehicles and 33 prediction runs reached 88% pass rate after prompt iterations (V1.1-V1.3), with 73% forward accuracy above the 65% deployment threshold. Evaluation is a hybrid of automated schema and rule checks on every prediction plus weekly human grading.

Who it's for

This is an internal operational tool, not a customer-facing product. The primary user is Albi, the Fleet Data Owner, who manages vehicle maintenance and work orders and currently juggles 3-4 disconnected systems. Secondary is Ivan Quiroz, VP Clinic Operations and executive sponsor, who needs KPI visibility; tertiary is the inventory manager who owns documentation accuracy.

Downstream beneficiaries include clinic managers who receive capacity forecasts. The delivering business is B2B service-led — 20/20 Onsite contracts with CROs and clinical trial sponsors for mobile screening — and this AI product is the operational infrastructure enabling that expansion.

Why it matters

The global mobile health services market is projected to grow at 25%+ CAGR through 2030, and mobile clinic demand tracks trial volume directly. 20/20 Onsite holds a first-mover position — no competitor is building AI predictive maintenance specifically for mobile clinical research fleets — backed by proprietary operational data no external vendor can replicate.

Launch is a phased pilot running to an August 31 decision gate, with kill criteria documented. Success targets include Fleet Readiness trending from a ~68% baseline toward 90%+, reducing unplanned maintenance costs 20%+, and 3+ hours a week saved for Albi. Governance is central: no PHI or PII in Phase 1, all work under the company's POL-0017 AI policy, and a full audit trail of every prediction with reviewer and timestamp.

At a glance

Project

Fleet Intelligence Dashboard

Built by

Jonathan Vasnarungruengkul

One-liner

Fleet Intelligence Dashboard is a daily AI operations system for managing a healthcare fleet of 11 vehicles across multiple asset types.