SI
SiliconIQ
Semiconductor Engineering Intelligence · Specs · RTL · UVM · Regressions · Coverage
AI learning from 1,742 engineering outcomes
ENV: ENGINEERING · ROLE: VERIFICATION LEAD
Dashboard
TrendIQ 10
DecisionIQ 8
ActionIQ 8
EffectivenessIQ 21
OntologyIQ 212K
KnowledgeIQ 6
Verification Engineering Operations
Continuous engineering intelligence across specifications, RTL, tests, regressions, coverage and defects
◊ Engineering graph: 212,480 linked entities
First-Pass Silicon Confidence
87%
↑ 4pts this quarter
Requirements-to-Test Traceability
91%
↑ 6pts since baseline
Weighted Coverage Closure
94%
Weighted by design criticality
Regression Triage Time
-27%
↓ vs. 8-week baseline
Open Verification Risks
7
3 critical
Continuous Intelligence Loop
Signal → Decision → Execution → Outcome → Organizational Learning
● Active across all modules
TrendIQ
10 active signals
→
DecisionIQ
8 initiatives
→
ActionIQ
7 in execution
→
EffectivenessIQ
21 outcomes learned
Mean Triage Time — 12-week trend
Engineer hours per regression, by subsystem
Engineer Time Recaptured
Hours per engineer per week, before vs. after AI assist
Requirements Traceability Health
Linked and current vs. stale vs. missing
Signal Mix — Last 30 Days
Open signals by owning team
AI Confidence Distribution
Across active recommendations
Top Verification Risks — AI-prioritized
Highest-impact items pulled from TrendIQ
AI Operational Copilot
Live workflow optimizations the system is recommending
TrendIQ — Verification Signal Detection
Signals detected across specifications, RTL, UVM testbenches, regression logs, coverage databases, defect trackers and Git
● Connected to 9 engineering systems
Active Signals
10
↑ 3 this week
Pattern Clusters
5
2 emerging
Cross-Domain Correlations
6
3 high-strength
RCAs in Progress
3
Avg conf 86%
AI Recommendations
5
Pending review
Signals 10
Patterns 5
Correlations 6
Root Cause Analysis 3
Recommendations 5
DecisionIQ — Initiative Management
Active verification initiatives created from accepted signals, with AI-suggested follow-on tasks
8 active initiatives 7 AI suggestions pending
AI Continuously Recommending
The system is learning from 1,742 historical engineering outcomes to improve every recommendation below
Initiative owners
94% acceptance
Task owners
89% acceptance
Closure paths
82% accepted
Follow-on tasks
71% accepted
ActionIQ — Execution Coordination
Two-tier Kanban: initiatives at the top, task drill-down on click
Initiatives Kanban
AI Operational Copilot
Live workflow optimizations across the board
Automation Opportunities
Workflows AI can orchestrate end-to-end (with verification engineer signoff)
EffectivenessIQ — Organizational Learning
What worked, what failed, and how the system is getting better at verification recommendations
● Continuous learning active
Outcomes Captured
1,742
↑ 86 this month
Engineer Corrections
138
→ model refinement
Initiative Success Rate
78%
↑ from 64% (Q1)
AI Recommendation Accuracy
88%
↑ 9pts vs baseline
Failed Closures Studied
6
Root-caused
Recent Learning Events
Outcomes from closed initiatives feeding back into the recommendation model
Improvement Over Time
Recommendation acceptance rate by quarter
What's Working (Reinforced)
Patterns the AI is doubling down on
What's Not Working (Down-weighted)
Patterns the AI is moving away from
OntologyIQ — Engineering Knowledge Graph
A living semantic model connecting every entity and risk relationship across active verification work
◊ 212,480 linked entities · 846,200 relationships
Entity Types Modeled
9
Requirements, Design Blocks, Interfaces, Tests, Coverage Points, Regression Runs, Defects, Code Changes, Decisions
Relationship Types
14
implements · validates · measures · executes · captures · modifies · resolved-by…
Graph Freshness
97%
Entities updated within the last cycle
Query Accuracy (validated)
93%
Against SME-reviewed answer set
Ask the Knowledge Graph
Not sure where to start? Try a sample question below, or type your own — answers are traced back to the underlying graph nodes and edges.
◊ Grounded answers only
Knowledge Graph Schema
Core entity and relationship types behind every answer
Highest-Connectivity Entities
Nodes with the most cross-domain relationships — usually the highest-leverage risk points
KnowledgeIQ — Institutional & Tacit Knowledge Capture
Tracks where verification engineering know-how is captured, at risk, or missing entirely, and visualizes it as a knowledge graph
◊ Linked to the OntologyIQ knowledge graph
Knowledge Assets Captured
27
↑ 5 this month
Experts at Retirement/Rotation Risk
6
2 uncaptured
Capture Coverage
71%
Of active work with a linked knowledge asset
Avg. Time-to-Capture
12d
↓ from 18d last quarter
Knowledge Graph — Experts, Assets, Programs & Sites
Instance-level view of the graph: who holds the knowledge, what's been captured, and which programs and sites it feeds. Click any node.
At-Risk Expertise
Operators and specialists nearing retirement or rotation, ranked by capture status
Knowledge Asset Library
Captured video, process sheets, and annotated drawings, linked to the program and initiative they came from
Add Custom Task
AI has pre-filled this based on the initiative's closure path
AI Pre-fill — Suggestions below are editable. Owner defaults to AI's recommendation.