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Deep-SKAI™ Use Case · 02

Should-Cost Intelligence

Evaluate cost reasonableness and negotiate with stronger decision support.

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 active suppliers · machined parts, castings, composites, harnesses, and electronics · a high share sole-source or limited-competition · pricing decided quote-by-quote, on judgment.

The situation

In prototype mode, price was noise: a handful of units, relationships over leverage, speed over cost. Rate production rewrites that. Every part price now multiplies by the build rate, and the margin that makes the program viable lives in the gap between what you are quoted and what a part should actually cost. Procurement has no independent basis to close that gap — they negotiate on relationship and instinct, accept sole-source quotes because challenging one requires a number they do not have, and re-accept new prices every time an engineering change resets the part.

The decision this protects

Is this price defensible — and if not, what is the number we can defend?

Target KPIs

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

Metric
Starting point
Target
Targeted spend under a should-cost basis
Fragmented / none
100% of targeted categories
Defensible price basis at negotiation
Buyer judgment, no benchmark
Benchmark on 80%+ of awards
Unit cost on negotiated parts
Accepted as quoted
↓ 5–15%
Time to build a should-cost position
Days–weeks, manual
Hours
Engineering-change repricing
Re-accepted reactively
Flagged before acceptance
How the engagement runs

From first look to running loop.

01 First Light 2–3 weeks
No integration

Your spend, scored for reasonableness in 2–3 weeks. No integration required.

Send a slice of spend — a category, a BOM with prices, or a set of open quotes. Nothing is connected. Deep-SKAI scores each price against proprietary aerospace & defense award- and quote-pricing benchmarks built since 2017, alongside should-cost modeling, and returns every targeted part scored for reasonableness, the parts and suppliers where you are most likely overpaying with the defensible number for each, and the modeled recoverable margin across the slice.

02 Pilot 4–6 weeks
Connected

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

Connect the systems behind the decision — ERP spend and PO history, the BOM, open quote data. Deep-SKAI encodes your policy and pricing constraints and runs the governed should-cost loop live on an active negotiation set: a defensible target price per part, the modeled consequence of each position, a human-in-the-loop approval, and a basis of record defensible to finance and, on the defense-adjacent variant, to DCMA.

03 Production Ongoing
Continuous

The decision becomes a governed operating habit. Ongoing.

The pilot category becomes standing practice — the loop runs continuously above your existing systems. Every quote and award arrives with a defensible should-cost position attached, each negotiated price is a decision of record, and engineering-change repricing is flagged against its basis before acceptance, not after. The loop expands to adjacent decisions as it earns trust.

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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Start small. Prove the decision creates value.

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

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