Dylan Hoback
Accu-Tube
My Praxie analytics software transformed our operations, enabling data-driven decisions and streamlining our manufacturing process.
The asset failed again. The work order says it was fixed.
Replace reactive repairs and paper work orders with AI that connects downtime, history and condition data — so you can see what is really failing, how often, and what it costs.
$0
Send us one month of your maintenance history
See Praxie in Action Now
Free pilot on your own asset data. You keep what we find.
AI maintenance managerfor maintenance and reliability 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.
You are not short of work orders. You are short of causes.
Fixing the failure was the easy part. Stopping it coming back, every time, is the job.
“Why is that machine down again?”
“We have replaced that part three times this year.”
A history in the CMMS, a reason in somebody's memory, and no link between them.
You do not need more preventive tasks. You need the failures to explain themselves — before the next one.
If you own uptime
Is this your week?
Most maintenance and reliability leaders recognise at least four of these. If you recognise six, the rest of this page is written for you.
- Work orders record what was done, never why it failed.
- The same asset fails repeatedly and nobody connects the events.
- Preventive schedules are based on the calendar, not condition.
- Failure codes get picked to close the ticket quickly.
- Spares are held because nobody trusts the lead time.
- Downtime cost is estimated, not known.
- Reliability improvement competes with today's breakdowns.
- “What is actually failing most?” takes a month to answer.
None of this is an effort problem. It is a root-cause problem.
Is this you?
Worked example — multi-asset production siteRepeat failures used to look like separate events.
Months↓Minutesto see what is really driving downtime
Hundreds of assets, years of work orders, and failure codes chosen to close the ticket.
Because each repair was logged in isolation, a recurring failure appeared as a series of unrelated jobs — so the fix kept being a replacement rather than a cause.
With AI connecting downtime, work order history and condition data, repeat patterns and their drivers surface together, and preventive effort can go where the losses actually are.
Monday, 6am
What you walk onto the floor with
Today
A backlog and a breakdown.
- Work orders that record the fix, not the cause
- Repeat failures logged as separate events
- Preventive work set by calendar
- Downtime cost estimated
→
With Praxie
A view of what is actually failing.
- Downtime, history and condition connected
- Repeat patterns surfaced automatically
- Preventive effort aimed at real losses
- Downtime cost quantified
Key ROI Elements
30%
Less Unplanned Downtime
Predict failures earlier and reduce emergency repairs.
40%
Faster Work Order Cycle Time
Cut delays from request to assignment to closeout.
25%
Higher PM Compliance
Keep preventive maintenance on schedule.
15%
Lower Maintenance Cost
Reduce overtime, expediting, and repeat failures.
Fewer
Parts Stockouts
Connect maintenance plans with spare parts needs.
Higher
Equipment Availability
Improve uptime and output from existing assets.
See Praxie in Action Now
Which asset keeps failing for reasons nobody can pin down?
Now select a day and time below.
Why Reactive Maintenance Never Catches Up
Spreadsheets &
Manual Logs
- Reactive tracking
- Paper-based work requests
- Limited asset history
- Hard to prioritize work
- Higher downtime risk
ERP / EAM
Maintenance Modules
- Connected to enterprise data
- Often rigid workflows
- Slow field adoption
- Heavy configuration
- Limited AI guidance
Traditional
CMMS Tools
- Good work order control
- Preventive maintenance support
- Can become data-entry heavy
- Limited operational context
- Often weak analytics
Point
Predictive AI Tools
- Useful for narrow assets
- Detects failure patterns
- May require sensor projects
- Disconnected from execution
- Limited maintenance workflows
★ BEST FIT
Praxie AI-Powered
Maintenance & Downtime Analysis
- AI work orders, inspections & PMs
- Predictive alerts and risk prioritization
- Connects ERP, MES, IoT, inventory & quality
- Dashboards, workflows & technician guidance
- Faster deployment, lower complexity
Maintenance workflow flexibility
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✓
Real-time downtime response
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✓
AI failure prediction & recommendations
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✓
✓
Work orders, PMs & inspections
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✓
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✓
Connected asset, inventory & operations data
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✓
Speed to deploy and adapt
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✓
Why Praxie
Stands Out
Stands Out
More flexible than rigid EAM and ERP modules
Far more automated than spreadsheets and manual logs
Broader than point predictive maintenance tools
Faster to deploy than heavy CMMS replacement projects
Questions Maintenance Leaders Ask Us First
The objections we hear in the first 20 minutes — answered plainly.
1
How does AI improve equipment maintenance — and can my team trust it?
Answer: The AI helps maintenance teams make better decisions by analyzing work orders, asset history, downtime, inspections, sensor data, and technician notes. Recommendations are explainable, so teams can see why an asset needs attention and stay in control of every maintenance decision.
2
Can this reduce unplanned downtime and emergency repairs?
Answer: Yes. AI-powered maintenance management identifies early warning signs, recurring failures, overdue tasks, and high-risk assets before breakdowns disrupt production. This helps teams shift from reactive firefighting to planned, preventive, and predictive maintenance.
3
How does the system help manage work orders and technician priorities?
Answer: The AI can help create, categorize, prioritize, and route work orders based on asset criticality, failure risk, production impact, required skills, parts availability, and maintenance history. Teams get a clearer view of what needs to be done first and why.
4
Will this help us find root causes and recurring maintenance issues?
Answer: Yes. The system analyzes patterns across failures, downtime events, corrective actions, inspections, and technician comments to identify repeat problems, likely root causes, and improvement opportunities across equipment, lines, plants, or vendors.
5
Is this better than spreadsheets, a basic CMMS, or manual maintenance tracking?
Answer: Yes. Traditional tools often store maintenance data but do not actively interpret it. AI-powered maintenance management connects asset data, work orders, KPIs, and operating conditions so teams can predict issues, optimize maintenance plans, and continuously improve reliability.
Bottom line: AI-powered equipment maintenance management helps teams reduce downtime, prioritize the right work, improve asset reliability, and move from reactive repairs to proactive maintenance execution.
Which asset would you investigate first, if the answer took minutes?
Send us one month of your maintenance history. We’ll show you what Praxie would surface — your assets, your failures, your losses.
1Send us your history230-minute review3Free pilot on your data
See Praxie in Action
No cost, no obligation — and you keep what we find either way.
Frequently asked questions
Answered
What is a CMMS and do I need one?+
A CMMS (computerized maintenance management system) manages work orders, PMs and asset history. If you’re running maintenance on spreadsheets or paper, a CMMS is the baseline — and modern AI tools add downtime analysis and prediction on top.
How does AI reduce unplanned downtime?+
AI finds the recurring causes worth fixing and flags developing failures early from history and sensor data — so breakdowns are prevented or planned, not suffered.
How do you prioritize maintenance work orders?+
Rank by risk and impact — asset criticality, downtime cost and failure likelihood — so the team works the jobs that protect throughput first, not just the newest ticket.
How much does maintenance software cost?+
It’s a subscription that scales with assets, users and sites; predictive features may add sensor/integration cost. Praxie starts with a free micro-pilot so you prove uptime gains before you pay.
Can AI use machine and sensor data for predictive maintenance?+
Yes — AI learns each asset’s normal behavior from sensor and history data and flags the early signals of failure, enabling condition-based, predictive maintenance on your critical equipment.
Go deeper
- What’s the best software to track and reduce equipment downtime?
- Preventive vs. predictive maintenance — which does my plant need?
- How does AI predict maintenance needs and prevent breakdowns?
- How do you do downtime analysis and find the real bottleneck?
- How do you calculate the cost of unplanned downtime?