Knowledge is fragmented
Critical know-how lives across PDFs, drawings, spreadsheets, emails, ERP, MES and people.
Industrial AI · Traceable by design
Turn industrial knowledge into decisions you can defend.
FYLAI connects technical documents, RFQs, manufacturing data and company know-how into one auditable intelligence layer — so every recommendation can be traced back to its source.
No source → no output
Human review where it matters
Built for industry, not demos
INPUT
RFQ / Drawing / 3D
Customer requirements
FYLAI
Industrial Truth Layer
OUTPUT
Routing · Cost · Evidence
Traceable recommendation
Source graph
Shop-floor data
Company rules
RFQ Intelligence•
Knowledge Graph•
Costing•
Traceability•
Shop-floor Data•
Auditability•
RFQ Intelligence•
Knowledge Graph•
Why now
Industrial companies have data. What they lack is trusted context.
AI answers are hard to audit
Generic copilots can generate plausible output without proving where a decision came from.
Quoting is still too manual
RFQ interpretation, routing and costing depend on scarce expertise and slow cross-functional loops.
The platform
One intelligence layer between company knowledge and industrial execution.
FYLAI structures evidence before generating recommendations, preserving provenance from source to output.
Understand industrial inputs
Technical documents, drawings, RFQs, historical quotations, machine data and operational rules.
Build the industrial truth graph
Link requirements, evidence, parts, operations, machines and company knowledge.
Generate traceable recommendations
Routing, machine choice, cost logic and supporting evidence — with confidence and provenance.
Improve with every validated case
Human-reviewed outcomes become reusable company knowledge instead of disappearing into individual inboxes and spreadsheets.
Source→Rule→Decision→Evidence
Pilot first
Start with one real workflow. Prove value before scaling.
We focus on bounded industrial use cases where quality, traceability and measurable time savings matter.
1
Connect
your real source data
2
Validate
against historical cases
3
Measure
accuracy, time and coverage
4
Scale
only after evidence
Built for trust
AI for environments where “probably right” is not enough.
↳
Source provenance
Every output can point back to supporting evidence.
↳
Human validation
Critical decisions stay reviewable and controllable.
↳
Company-specific logic
Your validated rules stay distinct from generic model knowledge.
↳
Deployment flexibility
Designed for industrial environments with security and integration constraints.
Team
Built at the intersection of industry, product and AI.
AM
Andrea Mauri
Product · Industrial Systems · AI Transformation
EB
Edoardo Binda
AI · Knowledge Systems · Product Engineering
MA
Matteo Alberti
Manufacturing · Data · Industrial Operations
Team descriptions are intentionally concise in this first version and can be replaced with final bios before launch.
Build the first use case with us
Bring us one difficult industrial workflow.
We’ll help determine whether FYLAI can make it faster, more consistent and more auditable.