One platform, fed by the data your fleet already has.
Clearly normalizes every source into one model, validates what's missing, benchmarks every asset against its true peers, and detects the cross-domain issues no single system can see.
One connected model of your fleet.
Everything else stands on this. Clearly pulls in the systems you already run and turns their mismatched, gap-ridden exports into a single model where every value knows what it means and where it came from.
- 1
Ingest
Connect telematics, fuel cards, maintenance and finance, or upload spreadsheets.
- 2
Normalize
Every source mapped to one canonical schema, units and timestamps aligned.
- 3
Resolve
The same vehicle, driver and site matched across systems into one entity.
- 4
Derive
True efficiency, cost and utilization computed, and gaps reconciled.
- 5
Score
Completeness measured per record, so you know what to trust.
vehicle_4012
completeness 98%
Analysts that can't make things up.
Vera and the agent team reason only from the connected data layer, so every answer is grounded and shows its sources. Ask a question, or let the agents work the fleet overnight and bring you findings.
Ask
Plain-English questions, or a standing brief the agents run on their own.
Retrieve
Pulls the exact records from the semantic layer through ~40 data tools.
Reason
Investigates, compares peers, rules causes in or out.
Cite
Every number traced back to the record it came from.
Act
Drafts and, within limits you set, executes the fix.
12 vehiclesidling above baseline
Every opportunity, priced and ranked.
Clearly scans every connected dataset for cost-saving opportunities, prices each in annual dollars, and stacks them so the biggest wins sit at the top, before you lift a finger.
- 1
Scan
Every dataset swept for patterns that cost money.
- 2
Price
Each opportunity valued as annual dollar impact.
- 3
Rank
Sorted by dollars, severity and trend.
- 4
Assign
Routed to the owner best placed to fix it.
- 5
Verify
The realised saving confirmed against a locked baseline.
Ranked opportunities
annual $ impact
Every signal, scored together.
One odd number is noise. Clearly scores every signal across fuel, maintenance, utilization and cost together, so you act on a case, not a hunch.
- 1
Collect
Signals gathered across fuel, maintenance, utilization and cost.
- 2
Cross-reference
Each checked against the vehicle's own history and peers.
- 3
Score
Signals combined into one confidence score.
- 4
Flag
Only cases past the threshold surface for review.
- 5
Assemble
The evidence packaged into a case ready to act on.
Case · Truck 4012
Flagged
Judged against its true peers.
A long-haul truck shouldn't be measured against a city van. Clearly benchmarks every vehicle against its real peer group and a self-calibrating baseline, so the outliers are genuine.
- 1
Classify
Each vehicle grouped by class, depot and duty cycle.
- 2
Calibrate
Baselines set from the peer group, not arbitrary thresholds.
- 3
Compare
Every metric placed against its peer distribution.
- 4
Surface
The real outliers, and the benchmark worth copying.
Truck 4012 · Class 6
vs 23 peers
peers · Class 6 · Vallejo hub · 90 days
1.1× peer
on pace
−8%
1.3× peer
Targets tracked, escalations early.
Define the KPIs your operation runs on, set targets, and track every hub against them, with problems surfacing before they become a quarter-end surprise.
- 1
Define
The metrics your operation actually runs on.
- 2
Target
Thresholds set fleet-wide or per hub.
- 3
Track
Live status and trend against every target.
- 4
Escalate
Drift flagged early, before the number slips.
KPIs vs target
trailing 13 weeks
Fleet idling per stop
3.8 min
≤ 3.5
Fleet-wide MPG
19.2
≥ 18.0
Cost per mile
$2.18
≤ $2.10
On-time arrival
95%
≥ 92%
From raised to resolved.
Finding the issue is half the job. Clearly routes each priced insight to the right owner, keeps it moving, and confirms the saving, so nothing quietly stalls.
- 1
Raise
A priced insight becomes a tracked item.
- 2
Route
Auto-assigned by hub, role and workload.
- 3
Nudge
Reminders keep outstanding items moving.
- 4
Confirm
Resolved and the saving banked, with a full audit trail.
Idling spike review
auto-assigned
Health you can trust.
Trustworthy answers need trustworthy data. Clearly monitors the health of your feeds the way it monitors your fleet, so gaps are visible before they reach a dashboard.
- 1
Monitor
Every feed watched for silence and drift.
- 2
Detect
Dark vehicles, unmatched fills and missing records found.
- 3
Flag
Each gap surfaced with its provenance.
- 4
Reconcile
Records matched back across sources.
Data health
412 vehicles · live
See what your fleet is leaking.
A 14-day pilot on your own data: true fuel efficiency, your top 10 cost anomalies, and your worst-performing assets. No commitment until you've seen the money.