连接与治理
接通你的系统、政策与知识，让 AI 在你的规则之内工作，而不是绕过它们。
/ Operations & Automation /
Catch defects before they reach the customer.
AI-powered quality monitoring that inspects, classifies and flags issues across production, service delivery and customer interactions — at a scale no manual process can match.
The problem
QA sampling misses what actually matters
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QA sampling misses what matters
Manual review covers a small fraction of output. Issues that fall outside the sample go undetected until they escalate.
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Quality data is collected but not acted on
Defect logs, customer complaints and inspection records exist but aren't connected. Patterns are invisible until they become crises.
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QA teams focused on reporting, not prevention
Most QA effort goes into documenting problems after they occur, not into detecting and preventing them earlier in the process.
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Defect patterns nobody connects
Issues repeat across lines, shifts or channels because root causes stay buried in logs instead of driving prevention.
How it works
从信号到成果——治理内置
自动化与辅助
让工作流上线：分流、起草、解决或升级，全程带完整上下文与审计追溯。
度量与改进
跟踪运营 KPI、质量与风险——再和你的团队一起调优剧本。
流程序列会适配你的工具、渠道与风险态势。
Catch defects before they reach the customer.
What's included
What you get when you run this with Thinkia
A governed layer across data, workflows, and handoffs—so teams ship safely and scale with metrics.
100% interaction or output coverage
AI monitors every transaction, interaction or production output — not a sample.
Defect classification
Automatically categorises quality issues by type, severity and root cause signal.
Pattern detection
Identifies recurring issues across time, product lines, teams or customer segments.
Real-time alerts
Notifies the right team the moment a quality threshold is breached.
Root cause analysis
Connects defect patterns to upstream process variables for faster resolution.
QA reporting automation
Generates quality reports and trend summaries without manual data assembly.
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Results
What changes when this runs in production
Results vary by process type, data availability and existing QA infrastructure.
100% coverage
Every unit inspected vs. 5–15% with manual sampling.
Orientative — confirmed in discovery; depends on the starting point.
–80%
Faster pattern identification vs. periodic manual review
Orientative — confirmed in discovery; depends on the starting point.
–55%
Reduction in time to produce quality reports per period
Orientative — confirmed in discovery; depends on the starting point.
How we work
From sample checks to continuous evidence across lines and shifts
Defect taxonomy
Defect taxonomy
Week 1–2
Failure modes, specs, and visual standards are aligned with engineering and operations.
Vision & data
Vision & data
Week 3–5
Cameras, lines, and labelling strategy are set; golden sets anchor model performance.
Line pilot
Line pilot
Week 6–9
Inline or end-of-line inspection runs in shadow; escapes and false rejects are tuned.
Plant network
Plant network
Week 10+
Rollout by site with central monitoring; change control when products or tooling shift.
Cycle time and lighting conditions affect vision models; scope follows stable SKUs first.
Ideas, trends, and tools to stay ahead
Get started
Ready to scope this for your context?
We start with a focused session—no commitment—to map constraints and a sensible path.