Dylan Hoback
Accu-Tube
My Praxie analytics software transformed our operations, enabling data-driven decisions and streamlining our manufacturing process.
You know the scrap rate. Nobody can tell you what is driving it.
Replace static quality dashboards with AI that connects inspections, NCRs, suppliers and process data — so a defect trend comes with its cause attached, not just its number.
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Free pilot on your own quality data. You keep what we find.
AI quality analyticsfor quality and operations leaders
Customer Success Stories
Dylan Hoback
Accu-Tube
Jason Carpenter
Environmental Pest Management
With Praxie, I found a way to take what was in documents and spreadsheets and provide my team with a visual application environment to drive our strategy with full accountability.
Elizabeth Pridham
Perfection Fresh
Praxie dramatically improved our process through digital transformation. We now have visibility and can drive decisions at a fraction of the time.
Scott Russell
NUCOR Vulcraft
I would heartily recommend the Praxie team to any organization seeking to seriously undertake a lasting and successful innovation process.
Mike Bainbridge
Dover Food Retail
Our MFG Ops application greatly improved our daily management initiatives. It is easy to see how we are doing, identify issues, and track improvements—no matter where we sit.
Jeff Piotrowicz
ChemLink
With Praxie we've created a way to report on complex projects that gives management full visibility. Executives have visibility to projects, assignments, and more at their fingertips.
Maureen Thompson
American Nurses Association
Our Praxie App makes it easy to track progress on strategic objectives across the organization and includes an executive-level dashboard with real-time reports to the board on key initiatives.
Tom Anderson
Springfield Armory
Praxie is used every day to track and analyze every aspect of production, quality, safety, and more. We improved quality by 10%.
Peggie Pelosi
Innovators Alliance
Almost overnight, Praxie created a customized solution for our 100 member organizations across Canada to drive strategy and innovation.
Kate Merton
Anthem
Praxie's innovation solution stood out from other options because it can be customized so quickly to fit our exact process requirements. Plus, it is incredibly easy to use and manage.
Every quality review ends with an action to investigate.
Measuring the defect rate was the easy part. Explaining it, every month, by hand, is the job.
“What is your corrective action for this trend?”
“Which line is actually generating the scrap?”
A dashboard that shows the rate rising and nothing about why.
You do not need another chart. You need the data to name the driver — while the batch is still running.
If you own quality
Is this your quality review?
Most quality and operations leaders recognise at least four of these. If you recognise six, the rest of this page is written for you.
- Dashboards show what failed, never why it failed.
- Inspection, NCR and supplier data live in separate systems.
- The monthly quality pack takes days to assemble.
- Two teams report different defect rates for the same line.
- Trends are spotted after the customer complains.
- Root cause is whichever theory was argued best.
- Corrective actions are tracked separately from the data that triggered them.
- “What changed?” takes a week to answer.
None of this is a measurement problem. It is an explanation problem.
Is this you?
Worked example — multi-line quality operationFinding the driver used to take days of pulling data.
Days↓Minutesto see what is driving a defect trend
Thousands of inspections, NCRs, supplier records and process readings — in systems that did not talk to each other.
Because the quality picture was assembled by hand for each review, causes were approximate and the analysis arrived after the batch had shipped.
With those sources connected, a defect trend arrives with its contributing factors already identified, so corrective action starts from evidence rather than from a theory.
Quality review, 9am
What you walk into the quality review with
Today
A rate, and a theory.
- Assembled by hand from four systems
- Shows what failed, not why
- Numbers disputed before they are acted on
- Trends found after the complaint
→
With Praxie
A trend with its driver attached.
- Inspection, NCR and process data connected
- Contributing factors surfaced automatically
- One set of numbers everyone works from
- Early warning while the batch still runs
Key ROI Elements
25%
Lower Cost of Poor Quality
Reduce scrap, rework, warranty, containment, and failure costs.
30%
Faster Quality Analysis
Spend less time building reports and more time solving problems.
20%
Fewer Recurring Defects
Use root cause insights to prevent repeat quality escapes.
40%
Faster Corrective Action
Move from issue detection to containment, CAPA, and verification faster.
Better
Yield & First Pass Quality
Improve output by identifying process drift and high-risk conditions earlier.
Higher
Customer & Audit Confidence
Connect evidence, traceability, and actions in one quality intelligence layer.
See Praxie in Action Now
Which quality metric can you not explain?
Now select a day and time below.
Why Quality Dashboards Stop at the Number
Spreadsheets &
Manual Quality Tracking
- Manual data entry and reconciliation
- Slow defect and NCR reporting
- Hidden rework, scrap, and warranty costs
- Limited root cause visibility
- Hard to prioritize corrective actions
ERP / QMS
Standard Reporting
- Captures quality records and transactions
- Often focused on compliance reporting
- Limited cross-system analytics
- Root cause work still manual
- Slow to customize for operations
Traditional BI
Dashboards
- Good static KPI visualization
- Requires structured, clean data models
- Often shows what happened, not why
- Limited action management
- Heavy analyst dependency
Point
AI Tools
- Useful for isolated analysis tasks
- Can summarize defects or reports
- May lack process and cost context
- Limited workflow accountability
- Requires stitching across systems
★ BEST FIT
Praxie AI Quality
& COPQ Analytics
- Connects quality, process, supplier, and cost data
- Prioritizes issues by COPQ impact
- AI root cause insights and recommendations
- Dashboards, alerts, CAPA, and action workflows
- Faster deployment with adaptable analytics
Cross-system quality visibility
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COPQ impact quantification
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✓
AI root cause insights
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✓
Corrective action workflow
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✓
Speed to deploy and adapt
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Continuous improvement loop
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✓
Why Praxie
Stands Out
Stands Out
Connects quality, process, supplier, and cost context
Prioritizes quality work by COPQ and business impact
Explains likely root causes instead of only showing charts
Turns insights into CAPA, alerts, and accountable actions
AI-Powered Quality & COPQ Analytics Case Study
Global Luxury Goods Manufacturer | Global Delivery & Service Network
A representative example of how Praxie helps a global luxury goods manufacturer connect quality data, process signals, warranty feedback, service records, and cost-of-poor-quality analytics across highly customized products delivered and maintained worldwide.
Customer Profile
- Industry: Luxury goods manufacturing
- Business: Highly customized luxury products with complex materials, components, finishing, supplier, packaging, delivery, and service requirements
- Challenge: Quality issues appear across the full product lifecycle — design, sourcing, production, inspection, delivery, warranty, and global field service
- Focus: AI-powered quality analytics and cost-of-poor-quality visibility from production through worldwide service and maintenance
Key Metrics & Results
COPQ Visibility
Quality, rework, warranty, supplier, and field-service costs consolidated into
one executive view.Root Cause Complexity
Quality teams can connect defects to build phase, supplier lots, processes, operators, parts, and service context.
End-to-end issue traceabilityGlobal Service Context
Delivered luxury products are maintained and supported around the world, requiring analytics across warranty claims, service notes, parts, and geography.
Worldwide lifecycle analytics!
Before Praxie
- Quality data was scattered across inspections, NCRs, sea trials, supplier issues, service tickets, spreadsheets, and warranty records.
- True COPQ was difficult to quantify because rework, scrap, travel, warranty labor, parts, and customer-impact costs were not connected.
- Root cause analysis was slowed by highly customized builds, many suppliers, many systems, and long product lifecycles.
- Leadership had limited visibility into which recurring quality issues were creating the largest financial and customer-experience impact.
✓
After Praxie
- AI-powered analytics connects inspection results, defect reports, process data, supplier history, warranty claims, and service records.
- Quality teams prioritize issues by cost impact, frequency, severity, customer risk, and downstream service burden.
- Root cause analysis highlights patterns across product lines, production phases, suppliers, materials, components, service regions, and operating environments.
- Executives gain a live COPQ dashboard for build quality, field quality, warranty exposure, and corrective-action effectiveness.
Lower COPQ
Reduced rework, warranty burden, premium freight, service travel, and avoidable part replacement.
Better Delivered Quality
Recurring defects are found earlier, prioritized faster, and prevented before delivery.
Smarter Corrective Actions
CAPA priorities are driven by cost, recurrence, field exposure, customer impact, and process risk.
“
We finally connected build quality, warranty costs, and global service data into one view of what quality is really costing us.
VP of Quality
Global Luxury Goods Manufacturer
Global Luxury Goods Manufacturer
15–30%potential COPQ reduction
2–4xfaster root cause analysis
25%+reduction in recurring defects
Globalservice and warranty visibility
Questions Quality Leaders Ask Us First
The objections we hear in the first 20 minutes — answered plainly.
1
How does AI help improve quality and reduce cost of poor quality?
Answer: AI connects quality events, production data, supplier issues, customer complaints, warranty claims, scrap, rework, and financial impact into one analytical view. Instead of only reporting defects after the fact, teams can see where quality losses are occurring, what is driving them, and which actions are likely to reduce COPQ fastest.
2
What types of quality data can the system analyze?
Answer: It can analyze structured and unstructured data from ERP, MES, QMS, inspection systems, NCRs, CAPAs, complaints, warranty records, supplier scorecards, spreadsheets, documents, images, and operator notes. The goal is to turn fragmented quality information into a unified source of insight.
3
Can it calculate the true cost of poor quality?
Answer: Yes. AI-powered COPQ analytics can combine visible costs like scrap, rework, returns, concessions, warranty, service, and containment with hidden costs such as engineering time, expediting, lost capacity, customer dissatisfaction, and recurring failure modes. This creates a more complete view of the financial impact of quality problems.
4
Will this help us find root causes faster?
Answer: Yes. The AI can identify patterns across defects, processes, suppliers, products, shifts, materials, machines, and locations. It helps teams prioritize likely root causes, summarize related incidents, and connect recurring quality issues that may be missed in separate systems or spreadsheets.
5
How is this different from a traditional quality dashboard?
Answer: Traditional dashboards usually show what happened. AI-powered quality analytics explains why it happened, where it is likely to happen again, and what actions could reduce risk or cost. It adds recommendations, anomaly detection, natural-language summaries, and workflow automation on top of KPI reporting.
6
Can teams use it for customer complaints, warranty, and field quality?
Answer: Yes. The system can connect field issues back to product configuration, production history, inspection records, suppliers, service notes, and cost data. This helps teams understand which issues are driving the greatest customer impact and lifecycle cost after products are delivered.
7
Does AI replace quality engineers or quality managers?
Answer: No. It supports their judgment by reducing manual analysis, organizing evidence, highlighting patterns, and recommending next steps. Quality leaders stay in control while AI helps them move faster, focus on the highest-value problems, and make better decisions with clearer data.
Bottom line: AI-powered quality and COPQ analytics helps teams find the biggest sources of quality loss, act faster on root causes, and reduce the financial impact of poor quality across the full product lifecycle.
Which quality trend would you explain first, if it took minutes?
Send us one month of your real quality data. We’ll show you what Praxie would surface — your defects, your lines, your drivers.
1Send us a month of data230-minute review3Free pilot on your data
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