About 
Transforming industrial expertise into durable, auditable knowledge
QognyX was created by practitioners who’ve lived the operational reality: expertise gets lost, decisions get repeated, and audits demand defensible proof.
Transforming industrial expertise into durable, auditable knowledge
QognyX was created by practitioners who’ve lived the operational reality: expertise gets lost, decisions get repeated, and audits demand defensible proof.
“Human expertise should not be lost. It must be captured, structured, and made auditable — so teams can operate faster and safer.”
Industrial performance depends on expert judgment — but that knowledge is fragile: turnover, scattered documentation, inconsistent practices, and reliance on external consulting.
AI should not be a black box. For high-stakes work, it must be bounded, reviewable, and anchored to evidence — so humans stay in control.
A production-grade platform that turns expert reasoning into reusable “knowledge capsules” and operational deliverables — not just chat responses.
We combine industrial expertise and AI engineering to deliver systems that are usable on the shop floor and defensible in front of stakeholders.
Co-Founder & CEO
Background in aeronautical and industrial environments, focused on performance and optimization. 30+ years in composite industry innovation, with a long-standing mission: preserve and scale human expertise.
Strengths: industrial performance, operational adoption, field reality.
Co-Founder & CTO
Technology architect (10+ years) and steel manufacturing expert (20+ years). Building transparent, scalable, auditable AI systems designed for enterprise constraints.
Strengths: system architecture, reliability, governance, and industrial AI deployment.
We built QognyX because industrial teams need something different from general-purpose AI: a platform that captures expert decision patterns, enforces validation, and produces deliverables that integrate into real operational workflows — with traceability for audits, governance, and continuous improvement.
We prioritize step-by-step decision logic and operational artifacts over long-form generic text.
Outputs are bounded by scope and anchored to evidence so humans can review, approve, and trust them.
Knowledge becomes a versioned asset that improves over time, not a one-off response that disappears.
We start with a scoped pilot: one site, one workflow, clear success metrics, and weekly operational outputs.