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Operational Decision Intelligence

Operational decision intelligence for aerospace, defense, and advanced manufacturing supply chains.

Your systems see everything and decide nothing. Deep-SKAI™ closes the gap between knowing and doing.

Turn fragmented operational signals into governed decisions, modeled consequences, and measurable outcomes — across suppliers, production, logistics, and critical operations.

To explore the platform, or get started with a First Light engagement…

Built for high-stakes environments — supplier risk, disruption, cost, compliance, and timing.

Operational complexity made whole
The problem

The decision is where the cost and the margin live.

Complex operations don’t fail because leaders lack dashboards. They fail because the right signal arrives late, context is scattered, constraints are unclear, and teams have no fast way to compare options before acting.

The big picture

One operation. Eight handoffs. No single view.

Your operation runs as a single chain — and every link carries its own inefficiency.

Contracting & Compliance

DFARS flow-downs and ITAR/EAR rules interpreted by hand; guardrails live in PDFs.

audit exposure
110 CMMC Level 2 controls, largely tracked by hand NIST 800-171 / DFARS · 2025
Supplier Risk

Cert status, OTD, and risk scattered across systems; problems surface at receiving.

tier-1 blind spot
40%+ have limited or no Tier-1 supplier visibility World Economic Forum · 2025
Should-Cost & Sourcing

Prices accepted without a defensible basis; margin conceded at award.

margin conceded
13% raw-materials savings from a should-cost capability McKinsey · 2025
Global Logistics

International lanes tracked in separate tools; disruptions surface late.

delay & demurrage
37% of companies lose track of shipments in transit Tive · 2025
Inbound Transportation

Carrier invoices accepted as billed; accessorials never audited.

accessorial leak
5–10% of freight invoices carry errors, most favoring the carrier Industry (Tompkins) · 2026
Production

Supplier and material constraints hit the line as surprises.

line-down risk
~11% of revenue drained by unplanned downtime at the largest manufacturers Siemens · 2024
Outbound Fulfillment

Commitments made without a live view of what’s actually moving.

missed OTD
88–92% reported industrial OTIF, often 15–20% lower once tracked MetricHQ
Customer Commitment

The promise to the customer rests on signals no one fully sees.

penalties & lost trust
~6% of businesses have full end-to-end supply-chain visibility Industry survey · 2026

Tap or hover a card for the research behind it.

Your systems grew up around single functions, each in its own frame.

CLM / GRC SRM + risk monitors S2P / e-sourcing GTM / freight visibility (RTTVP) TMS + freight audit/pay ERP / MES WMS / OMS CRM / customer portal

So they were never built to connect. Even company-wide, only 29% of enterprise applications are integrated — so how is anyone supposed to manage the whole?

MuleSoft / Salesforce Connectivity Benchmark · 2025

Worse, the biggest costs don’t sit in any link. They rest in the seams between them — and beneath it all is the manual work holding the chain together.

Manual reports, presentations, emails, meetings, and endless spreadsheets — the glue between every system.

The Inventory Buffer

Stock held to cover for what no one can see — cash tied up as a hedge against blindness.

working-capital drag
$1.7T trapped in excess working capital — 11% of revenue Hackett Group · 2025
Firefighting

Every rush order is a decision that reached you too late to handle through normal channels.

reactive premium
64% of manufacturers spend 10%+ of budget reacting to disruption LeanDNA · 2026
Decision Latency

Days lost gathering and reconciling before anyone can actually decide.

time-to-decision
2–5 days to gather info post-disruption; ~32% of time on reconciliation KNOSC · 2026
Data You Can’t Trust

The same fact carries different numbers in different systems; every handoff inherits the gaps.

silo drag
~$12.9M/yr average cost of poor data quality; siloed data is the named root cause Gartner (2020 est., standing)

The seams add up. Supply-chain disruptions cost the average organization
45% of a year’s profits over a decade.

McKinsey

Sources: McKinsey Global Institute (disruption cost) · World Economic Forum 2025 (Tier-1 visibility) · McKinsey 2025 (should-cost savings) · Tive 2025 (shipment tracking) · Tompkins/industry 2026 (freight invoice errors) · Siemens 2024 (unplanned downtime) · MetricHQ (industrial OTIF) · industry survey 2026 (end-to-end visibility) · MuleSoft/Salesforce Connectivity Benchmark 2025 (app integration) · Hackett Group 2025 (working-capital survey) · LeanDNA 2026 (expediting & aerospace proof point) · KNOSC 2026 (decision latency) · Gartner 2020 est., standing (poor-data-quality cost) · NIST 800-171 / DFARS 252.204-7012 (CMMC Level 2). Illustrative framing; no named customer.

How the decision happens

The same decision, two ways.

A flight-critical supplier’s AS9100 certification is lapsing and on-time delivery is slipping. Here’s how that decision plays out today — and with Deep-SKAI™.

Earlier Later
Today Status quo — visibility, but the decision is left to you.
Blind window — the lapse is already forming, but nothing surfaces here.
Signal arrives late

The lapse surfaces at receiving — or when a delivery is already missed.

Context is scattered

Cert PDFs, a delivery spreadsheet, a buyer’s memory. No single picture.

The team scrambles

Manual calls, premium freight, re-sourcing under pressure.

Too late
Outcome is murky

Line-down risk, expedite cost, schedule slip — and nothing is logged.

Modeled exposure: line-down + expedite premium
With Deep-SKAI The governed loop — Sense, Decide, Act, Learn.
Weeks earlier
Sense
Risk surfaces weeks earlier

Cert expiry and OTD trend, scored against benchmark data, before it bites.

Decide
Options, with constraints visible

Ranked against cost, schedule, and compliance limits — ITAR, approved-supplier.

Act
A governed decision, owned

The owner approves; action executes across existing systems, on the record.

Learn
The outcome teaches the loop

Expected vs. actual logged; the next score is sharper.

Risk contained — expected vs. actual on the record

Illustrative scenario — no named customer. The modeled exposure is directional and qualitative, not a quoted figure.

What Deep-SKAI does

One governed loop, above your existing systems.

Deep-SKAI connects the full decision loop — so signals become governed decisions, decisions become accountable action, and outcomes make the next decision sharper.

Governed decision layer
Sense

Bring fragmented signals and context into one place.

Decide

Weigh options against real cost, service, and policy constraints.

Act

Nothing executes without your sign-off — then it carries across your systems, logged.

Learn

Outcomes feed back, so the next decision is sharper.

See all use cases
Where it fits

It sits above your systems. It doesn’t replace them.

Deep-SKAI connects to the systems you already run — from the engineering BOM to the receiving dock — applies your policies and constraints, and turns what they know into decisions your teams can defend. And if a decision platform already sits in your stack, Deep-SKAI works with it — reading the state you’ve authorized and handing back the governed decision on top. No rip-and-replace, no either/or.

Deep-SKAI
reads authorized state → returns the governed decision
Your decision platform
(if any)
Plan & resource

ERP · MRP · Finance

Engineer & build

PLM · MES · QMS

Source & schedule

Procurement · Scheduling

Move & store

TMS · WMS

… and the partner systems in your ecosystem.

The advantage

An advantage that stays yours.

Orchestration

You keep the stack you already run.

Proprietary data

Priced against what the market actually paid.

Governed · Patent-pending

A decision you can defend, on the record.

Why now

More AI models will not close this gap. A copilot that suggests and then shrugs still leaves the decision — and the liability — with you.

Start small. Prove the decision creates value.

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

To explore the platform, or get started with a First Light engagement…

Get Started