AI-Powered Analytics | Praxie

You can see the number moved. Nobody can tell you why.

Move beyond static BI dashboards and backward-looking reports. Praxie AI analytics connects your operational data, explains what is changing, predicts what may happen next, and recommends where your team should act first.

Send us one month of your real operating data See Praxie in Action Now Free pilot on your own data. You keep what we find.

AI-powered KPI managerfor executives and operational leaders

Praxie AI-powered analytics interface preview

Customer Success Stories

Dylan Hoback Accu-Tube

AI analytics

My Praxie analytics software transformed our operations, enabling data-driven decisions and streamlining our manufacturing process.

Jason Carpenter Environmental Pest Management

AI visibility

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

Digital transformation

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

Innovation management

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

Daily management

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

Project visibility

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

Strategic execution

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

Performance analytics

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

Custom innovation solution

Almost overnight, Praxie created a customized solution for our 100 member organizations across Canada to drive strategy and innovation.

Kate Merton Anthem

Custom applications

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 question turns into a data request.

Building the dashboard was the easy part. Explaining what it means, every week, by hand, is the job.

Your CEO

“Why did margin drop last month — and is it still dropping?”

Your VP of operations

“Which line is actually costing us, and since when?”

You

Four dashboards, three spreadsheets, two systems — and an answer by Thursday.

You do not need another dashboard. You need the data to explain itself — and say what to do about it.

If you own the numbers

Is this your reporting week?

Most operations leaders and CI managers recognise at least four of these. If you recognise six, the rest of this page is written for you.

Every meeting starts with someone questioning the numbers.
Your dashboards show what happened, never why it happened.
The monthly report takes days to assemble and is stale when it lands.
Two teams bring two different numbers for the same KPI.
The real analysis lives in one spreadsheet that one person fully understands.
By the time a trend reaches a report, the quarter is already gone.
You can see the metric move but not which driver moved it.
“Can you pull the numbers?” is most of your week.

None of this is a reporting problem. It is a time-to-answer problem.

Is this you?

Worked example — executive and management KPIs

The number is in the board deck. Finding the reason is in six systems.

Days↓Minutes to explain why a KPI moved

Revenue, gross margin, cost per unit, on-time delivery, working capital. Five systems, four owners, one month-end close.

Executive KPIs get assembled from ERP, finance, CRM, operations and a layer of spreadsheets. So when the board asks why margin moved, answering it means chasing the owner of each source and rebuilding the picture by hand — usually after the quarter has already closed.

With AI-powered analytics those sources stay connected, so the KPI and the drivers behind it arrive together. The variance comes with its explanation attached, while there is still a quarter left to act on it.

Monday, 8am

What you walk into the management meeting with

Today

A deck that explains last month.

Assembled by hand from four systems
Shows what happened, not why
Numbers argued over before they are acted on
Trends spotted a quarter too late

→

With Praxie

An answer that is current right now.

Connected data, refreshed continuously
Drivers and root causes surfaced automatically
One set of numbers everyone works from
Early warning while you can still act on it

ROI of Moving from Standard BI & Spreadsheets to AI-Powered Analytics

Key ROI Elements

50–80%

Faster Insight Discovery

Important trends and drivers surface earlier.

20–40%

Less Manual Analysis

Fewer repetitive reporting and spreadsheet tasks.

15–30%

Faster Decision Cycles

Teams move from insight to action more quickly.

10–25%

Better Forecast Accuracy

Forward planning improves with AI-driven signals.

20–40%

Fewer Escalations

Issues are identified before they spread.

10–25%

Stronger Alignment

Shared insights across teams and functions.

See Praxie in Action Now

What’s the biggest question you’d like to know about your business?

Why Your Dashboards Can’t Tell You Why

A simple view of the analytics landscape — and why AI-powered analytics goes beyond traditional business intelligence.

Spreadsheets &
Manual Analysis

Manual data compilation
Limited visibility
High risk of errors
Slow to gain insights
Hard to scale

Integrated ERP /
System Reporting

Connected to core systems
Often rigid workflows
Limited customization
Heavier implementation
Limited cross-system context

Traditional BI /
Dashboard Tools

Strong reporting capabilities
Good historical analysis
Requires dashboard setup
Often shows what happened, not why
Limited action automation

Point AI
Analytics Tools

Useful for narrow tasks
Adds isolated automation
May require tool stitching
Less end-to-end context
Narrow data coverage

★ BEST FIT

Praxie
AI-Powered Analytics

Flexible AI analytics workspace
Real-time insights
Connects across data, systems & documents
Dashboards, alerts & workflow automation
Faster deployment, lower complexity

Analytics flexibility

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Real-time insights

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AI-driven recommendations

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Cross-functional data support

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✓

Speed to value

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Ease of adapting to change

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– Limited / weak support ◐ Partial support ✓ Strong / full support

Why Praxie
Stands Out

More flexible than rigid reporting systems

Far more automated than spreadsheets

Broader than point AI tools

Faster to deploy than traditional analytics projects

AI-Powered Quality Analytics Case Study

Midwestern Yacht Manufacturer | Complex Custom Marine Production

A simplified example of how a mid-sized yacht manufacturer moved from traditional BI and spreadsheet-heavy quality reporting to AI-powered quality analytics—connecting inspections, NCRs, supplier data, warranty issues, drawings, photos, and production records to improve decisions and standard processes.

Key Quality Analytics Metrics & Results

Quality Visibility

Quality reviews used to require manual dashboard pulls, spreadsheets, and tribal knowledge; now the team sees issues, trends, and drivers in

near real time.

Data Complexity

Each yacht contains thousands of custom requirements, inspection points, supplier parts, build steps, photos, and customer-specific quality expectations.

High-mix custom quality data

Operational Context

Quality decisions must connect design intent, production process, supplier performance, in-process inspections, final commissioning, and warranty feedback.

Closed-loop quality management

Before Praxie

Quality leaders relied on traditional BI dashboards that showed defect counts but did not explain why issues were happening.
Inspection data, NCRs, rework, supplier quality, warranty feedback, photos, and drawings were difficult to connect.
Root-cause analysis and CAPA follow-up depended on manual spreadsheet reconciliation and meetings.
Standard processes varied by department, customer program, and product line, making continuous improvement harder to scale.

✓

After Praxie

AI-powered analytics connects quality signals across inspections, NCRs, supplier data, photos, warranty claims, drawings, and production records.
Teams identify recurring issue patterns, likely root causes, and priority actions faster.
Standard quality processes become easier to monitor, compare, and improve across departments and yacht programs.
Leaders receive narrative summaries, risk alerts, and recommended actions instead of static dashboard interpretation.

Faster Quality Insight Cycles

Quality reviews move from manual reporting to faster AI-assisted insight discovery.

Lower Cost of Poor Quality

Recurring defects, scrap, rework, and escapes are surfaced earlier and prioritized.

Stronger Standard Processes

Inspection, NCR, CAPA, supplier, and warranty workflows become more consistent.

Standard Processes Improved

Inspection & NCR Review

Inspection results, nonconformances, photos, and comments are analyzed together.

40–65% faster review cycles

Root Cause & CAPA

AI highlights recurring failure modes, likely drivers, and corrective-action priorities.

30–60% faster follow-up

Supplier Quality

Supplier defects, late certs, material issues, and incoming inspection trends are connected.

10–25% fewer supplier-related defects

“

We used to know where quality issues showed up. Now we understand why they happen and what to fix next.

VP of Operations
Project Sponsor

50–75%faster quality insight cycles

25–45%less manual reporting effort

15–30%fewer recurring quality issues

30–60%faster CAPA follow-up

AI-Powered Analytics FAQ

Questions Operations Leaders Ask Us First

The objections we hear in the first 20 minutes — answered plainly.

How is AI-powered analytics different from traditional BI?

Answer: Traditional BI is usually focused on dashboards and reports that show what happened. AI-powered analytics goes further by connecting data across systems, explaining why results changed, predicting what may happen next, and recommending actions teams can take.

What kinds of data can AI-powered analytics use?

Answer: It can use structured and unstructured data from ERP, CRM, MES, QMS, finance, operations, workflows, tickets, documents, notes, spreadsheets, emails, and external signals. The value comes from connecting fragmented data into a shared business context.

Can the AI explain why a metric changed?

Answer: Yes. AI-powered analytics can analyze patterns, correlations, segments, process changes, exceptions, and supporting context to identify likely drivers behind performance changes. Instead of only seeing the metric, users get a plain-language explanation of the factors that contributed to it.

Does this replace analysts or business leaders?

Answer: No. It supports them. Analysts and leaders stay in control while AI reduces manual reporting, surfaces hidden patterns, summarizes findings, and recommends next steps. The goal is to help teams move faster from data gathering to decision-making.

How does AI-powered analytics help teams take action?

Answer: The system can prioritize insights, flag risks, recommend next-best actions, assign follow-up tasks, and generate summaries for teams or executives. This helps analytics become part of daily execution instead of ending with a static dashboard.

Is AI-powered analytics only for manufacturing?

Answer: No. It is useful anywhere teams need to connect fragmented data and make better decisions, including operations, finance, supply chain, quality, sales, customer service, product management, and executive reporting.

Bottom line: AI-powered analytics helps teams move from static reporting to connected data, faster root-cause insight, earlier risk detection, better recommendations, and stronger decision execution.

What would you ask your data right now, if the answer took minutes?

Send us one month of your real operating data. We’ll show you what Praxie would surface from it — your KPIs, your systems, your plant.

1Send us a month of data 230-minute review 3Free pilot on your data

See Praxie in Action

No cost, no obligation — and you keep what we find either way.