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Deep-SKAI™ Use Case · 03

Inbound Carrier & Accessorial Intelligence

Find the accessorial leak in inbound freight — then stop it going forward.

Illustrative scenario · modeled targets, not deployed results
The operator (composite)

A composite Tier-1 eVTOL airframer moving from flight-test prototypes into FAA type certification and early rate production, with a defense-adjacent variant under DCMA oversight.

~140 suppliers shipping across LTL, FTL, expedite, and parcel · accessorial charges (detention, reconsignment, liftgate, fuel, expedite premiums) paid largely unexamined · carrier invoices validated by hand, if at all.

The situation

Every part you buy also has to move. As rate production ramps, inbound freight volume climbs and carrier invoices multiply — and riding inside them is a layer of accessorial charges nobody has time to challenge. Each one small, all of them paid, most never checked against contract terms or what the lane should bear. It is the should-cost problem wearing a different hat: a defensible-price question on the part, and an identical one on the freight that carries it — where today there is no basis at all.

The decision this protects

Is this accessorial defensible — pay it, dispute it, or prevent it?

Target KPIs

What this engagement is designed to move — modeled targets for an operator of this profile, not measured results.

Metric
Starting point
Target
Accessorial charges under a defensible basis
Accepted as billed
100% of audited lanes
Invalid or unsubstantiated charges identified
Caught rarely, by exception
Flagged systematically
Accessorial spend as % of inbound freight
Unmeasured
Quantified · ↓ 10–25%
Time to validate a carrier invoice
Manual or not done
Automated read
Preventable accessorials (detention, etc.)
Recur unaddressed
Flagged for upstream fix
How the engagement runs

From first look to running loop.

01 First Light 2–3 weeks
No integration

Your freight invoices, audited in 2–3 weeks. No integration required.

Send a 90-day sample of inbound carrier invoices — a file, not a system connection. Deep-SKAI audits each accessorial against contract terms, lane norms, and reasonableness, applying the same should-cost discipline to freight billing it applies to part quotes: every accessorial scored defensible or questionable, the charges most likely invalid or preventable with the basis for each, and the modeled recoverable and preventable leak across the sample.

02 Pilot 4–6 weeks
Connected

One decision, running live in your environment. 4–6 weeks.

This is where the audit becomes prevention. You stand up an advanced carrier-execution and tracking capability — delivery and load information flows in, carriers move the freight, and the tracking signal accrues — and Deep-SKAI authors decisions live across carrier selection and accessorial validation as the data builds. The audit found the leak; the Pilot starts closing it going forward.

03 Production Ongoing
Continuous

The decision becomes a governed operating habit. Ongoing.

The pilot lane set becomes standing practice — the loop runs continuously above your existing systems. Every carrier invoice arrives with a defensibility read attached, preventable accessorials are flagged upstream before they recur, and each decision is explainable, human-approved, and auditable. The loop expands to adjacent decisions as it earns trust.

Unlike the supplier and should-cost cases, this value is capability-first and forward-looking — there is no historical corpus to score cold, so the loop compounds as loads accumulate. First Light is an honest backward-looking audit of invoices you already have; the live, forward-looking carrier-execution capability (provided through a capability partner) enters at Pilot.

Illustrative scenario. The composite operator is fictional, created to show how an engagement is structured; it does not depict a specific NexStratus customer, and the KPIs shown are modeled targets, not measured results from a deployment. Deep-SKAI is patent-pending.

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Pick one high-value decision pattern, connect the minimum data needed, and model the consequence before expanding.

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