3 papers
Foundational AI for Physical Product Development
An in-depth look at how purpose-built AI architectures outperform general-purpose LLMs when applied to engineering and manufacturing documentation.
Unstructured Data Extraction in Aerospace Engineering
How leading aerospace manufacturers are unlocking value from decades of unstructured technical documentation using semantic AI models.
Featured studies
Aerospace OEM
Reducing Design Change Cycles by 11% at a Major Aerospace OEM
By connecting engineering change orders with historical FMEA and test data, Apiphany helped the program team catch late-stage design issues 3 weeks earlier in the development cycle.
11%
Fewer late-stage changes
3wk
Earlier risk detection
43%
Fewer recurring issues
Automotive Tier 1
37% Faster Issue Resolution Across Cross-Functional Teams
A leading Tier 1 automotive supplier deployed Apiphany to unify quality, manufacturing, and field service data — resulting in dramatically faster root cause identification.
37%
Faster issue resolution
5x
More relevant AI output
60%
Less manual data search
Latest posts
Engineering AI · FEATURED
Why AI for Manufacturing Keeps Getting It Wrong — And What Physics-First Models Change
The promise of AI in manufacturing has been real, but the gap between hype and production-ready results comes down to one core issue: domain specificity. Here's why general-purpose LLMs fail at engineering data and what physics-trained AI does differently.
Manufacturing
Tribal Knowledge in Manufacturing Is Your Biggest Risk Factor
Product
How Apiphany Handles Multi-Modal Engineering Data
Podcast
Titans of Manufacturing: Episode 1 Now Live
Quality
FMEA AI Automation: How Leading OEMs Are Connecting Failure Modes to Field Data in Real Time
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