QURASYNC ONTOLOGY · THE CORE TECHNOLOGY

Three worlds that
never talked to each other.
Now they do.

Every quality failure in safety-critical supply chains shares the same anatomy: the measurement existed, the language existed, the context existed — but they lived in separate worlds. No shared code. No common signal. QuraSync's ontology is that code.

The fundamental gap

Your quality data exists.
It just speaks three different languages.

No single person in any organisation masters all three simultaneously. The engineer who understands the measurement doesn't always understand the standard. The person who understands the standard doesn't always speak the supplier's language. The person who speaks the language doesn't always understand what the number means. This is not a people problem. It is an architecture problem.

01
Real-time measurement data
Numbers from machines, sensors, and gauging stations. Pull-out forces. Bore diameters. Weld pressure. RPN scores. Precise, timestamped, objective — and completely meaningless without context.
"48 N · 3× out of spec this shift · Machine 3 · Post-repair"
Precise but silent
02
Human language
Emails from production managers. Voice notes from the shop floor. Customer complaints. 8D reports. Rich with context, urgency, and meaning — in whatever language, format, and level of structure the sender happened to use.
"die Teile kommen zu heiß raus und werden sofort in die Kiste geschmissen ohne Abkühlzeit"
Rich but unstructured
03
Standards and classification
AIAG-VDA rules. IATF requirements. Severity scales. Detection ratings. The formal system that defines what a quality signal means — and what must happen next. Known by few. Consulted under pressure.
AIAG-VDA Rule 4 · S=9 implies safety boundary · No detection control = audit finding
Authoritative but inaccessible

"The reason quality data fails to travel is not technical. It is linguistic. The same event is described in three different ways by three different people — and no system exists to recognise that all three are talking about the same thing."

— Bernd Mischke, Co-Founder & CCO · 25 years as technical interpreter across automotive supply chains
How the ontology works

The same insight,
explained for your context.

The ontology means different things to different people. Select your perspective.

The strategic answer to a problem
that costs you every year.

Every safety-critical supply chain generates enormous amounts of quality data. The problem has never been a lack of data. It has been the absence of a shared code — a system that all parties in the chain can read, act on, and trust. QuraSync's ontology is that code.

48 N
A measurement — precise, silent
RPN 280
A risk score — calculated, unactioned
Signal
What QuraSync produces — structured, routable
T3→OEM
Travels the chain — without raw data
WITHOUT QURASYNC
✗Quality problem known at T3 on Monday
✗Written up in German, filed internally
✗T1 receives email Friday — context lost
✗You hear about it at the audit
✗Line stop. Recall. Non-conformity.
WITH QURASYNC
✓Quality signal generated Monday 14:22
✓Structured, severity-rated, language-bridged
✓T1 receives actionable signal Monday 14:23
✓You see it in your dashboard — not the audit
✓Wednesday identified. Friday resolved.

"A competitor who tries to replicate this needs 25 years of technical interpretation, three industries, two languages, and three years of engineering. Knowing what was built is not the same as being able to build it."

You've been the bridge.
QuraSync automates the bridge.

As a quality engineer you live in all three worlds every day — measurements, standards, and human language. Until now, you were the translation layer. Manually. Every time. Here is what that translation looks like when the ontology does it.

Example 1 — voice note, shop floor German
Raw input: "die Clips brechen beim Fügen wenn die Halle kalt ist, vielleicht 0.3% Ausschuss schätze ich, der Werkzeugmacher meint das Werkzeug ist ok"
→
Structured signal

Failure mode: clip fracture on assembly
Cause: thermal shrinkage, insufficient cooling
Occurrence: ~0.3% (unverified — flagged)
Detection gap: no written complaint, no measured rate
Effect: customer impact confirmed
Example 2 — English shopfloor note, measurement data
Raw input: "pull-out force borderline 48N spec says 50N, crimping machine 3 repaired last month, die alignment maybe off, only testing every 200 pieces"
→
Safety flag

Failure mode: insufficient crimp joint strength
Cause: tooling alignment post-repair
Detection gap: 200-piece interval — D-rating review required
Action: re-calibration + interim 100% check
Example 3 — mixed DE/EN, severity ambiguity
Raw input: "Wastegate leaking above 2.1 bar, spec 2.5 bar, optical inline catching 60%, Tobias fragt ob S=9 oder S=10"
→
S=9 recommended

Effect chain: weld leakage → turbocharger damage → engine failure
Severity: S=9 (safety-relevant, indirect injury mechanism)
Detection: D=6 — optical gap quantified
Recommended: second optical station or fixture redesign
Why the language bridge matters technically: the ontology contains 135+ mapped term pairs — not just translations, but functional equivalences. Fehlerart maps to the specific AIAG-VDA column definition, the severity logic, and the detection requirements that come with it. Bandstillstand triggers a different system response than Qualitätsabweichung — because a line stop is not the same as a deviation, and the ontology knows the difference.
Why an ontology — and not just an AI
that reads documents?

An AI that reads documents is impressive. An AI that reads documents within a structured system of meaning is reliable enough to use in safety-critical industries. The difference is the ontology.

📝
Layer 1
Raw human language
Unstructured · Context-dependent · Cannot be routed
"die Teile kommen zu heiß raus und werden sofort in die Kiste geschmissen"
A sentence that contains a failure mechanism, a cause, and an urgency signal — but in a form that no standard system can parse, classify, or route. Meaningful to a human expert. Invisible to any quality management system.
ontology interprets
🔢
Layer 2
Structured quality concept
Classified · Standard-mapped · Linked to schema
Failure mode: dimensional drift from thermal deformation. Cause: insufficient cooling time at ejection. Process step: post-moulding handling.
The ontology activates a chain of relationships. This isn't translation — it's interpretation within a formal structure. The concept is now linked to its PFMEA entry type, its severity classification logic, and the AIAG-VDA rules that apply to it.
ontology classifies
📡
Layer 3
Canonical quality signal
Language-independent · Tier-transmissible · Auditable
Failure mode code · Severity indicator · Detection gap flag · Recommended action type · Structured packet — travels T3 to OEM.
The signal contains everything the receiving tier needs to act — and nothing it doesn't need to know. No raw data. No supplier internals. This is not achieved through anonymisation. It is achieved through semantic abstraction — the signal is constructed from a different layer of meaning than the data it was derived from.
What an ontology is — and is not: an ontology is not a glossary. A glossary tells you what a word means. An ontology tells you what a concept is, what it relates to, what rules apply to it, and what consequences follow from it. When QuraSync encounters Fehlerfolge, it doesn't translate it as "failure effect" — it activates a set of relationships: effect chain → severity classification → safety boundary check → escalation logic. That chain is what makes the system reliable enough for safety-critical use.
Why this is hard to replicate

Knowing what was built
is not the same as being able to build it.

The QuraSync ontology is not a technology decision. It is the accumulated output of 25 years of technical interpretation, three industries, two languages, and three years of engineering. A competitor who reads this page understands what they would need to build — which is precisely what makes it a moat, not just a feature.

🕐
25 years of domain calibration
The term mappings are not the result of reading AIAG-VDA documents. They are the result of 25 years of working at the boundary between language and measurement data — in factories, in quality reviews, in supplier audits, across cultures. You cannot buy that calibration. You accumulate it.
🌐
Cross-industry, not single-vertical
Most quality software is built for one industry. QuraSync's ontology spans automotive, aerospace, MedTech, defence, and rail — because the underlying failure mode taxonomy is industry-neutral. Building that requires deep knowledge of five standards bodies simultaneously. That breadth is rare.
🔗
Relationships, not just terms
A term database can be duplicated. A relational ontology — where every concept is connected to its classification logic, its standard-specific rules, its detection implications, and its escalation triggers — cannot be reverse-engineered from the outside. The relationships are the product.
📡
The abstraction layer is the IP
The ability to produce a canonical quality signal that carries meaning without carrying raw data is not a feature of the AI — it is a property of the abstraction layer the ontology creates. Building an AI on top of raw data produces a different result. The architecture matters as much as the content.
🏭
Calibrated on real supply chain data
Every rule, every mapping, every classification boundary was calibrated against real quality events — real PFMEAs, real 8D reports, real field complaints — from real safety-critical supply chains. Synthetic training data produces synthetic results. Ours produces ones engineers trust.
⚡
Regulatory tailwind locks it in
AIAG-VDA mandatory from January 2027. Tightened EASA requirements. EU MDR. Every new regulation is a forcing function — companies need QuraSync's ontology to be compliant. The moat deepens as the regulatory surface expands. Every standard update we absorb is one a competitor must start from scratch.

"For ten thousand years, people communicated across distances with signals through the air. The smoke didn't need to explain itself — sender and receiver shared the same code. QuraSync is that shared code for safety-critical supply chains."

— The smoke signal philosophy · QuraSync architecture
Early access
See the ontology
working on your data.

We're inviting a small group of quality and operations leaders to experience the ontology on real supply chain inputs — in their industry, in their language.

Request early access

No sales team. A direct response from the founding team in Ingolstadt.

Quality intelligence for safety-critical supply chains · Ingolstadt, Bavaria · 2026
© 2026 QuraSync