Dylan Hoback
Accu-Tube
My Praxie analytics software transformed our operations, enabling data-driven decisions and streamlining our manufacturing process.
The design was approved. The floor found the problem.
Replace late-stage design reviews with AI that checks manufacturability while the part is still on the screen — flagging tolerances, processes and cost drivers before tooling is cut.
$0
Send us one part you expect trouble with
See Praxie in Action Now
Free pilot on your own designs. You keep what we find.
AI design for manufacturabilityfor engineering and manufacturing 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 change costs ten times more than the last one.
Designing the part was the easy part. Finding out whether you can actually make it, before tooling, is the job.
“We cannot hold that tolerance on this process.”
“Nobody raised it during the design review.”
A review that depends on which experienced person happened to attend.
You do not need another gate. You need the design to flag itself — while changing it is still cheap.
If you own the design
Is this your release?
Most engineering and manufacturing leaders recognise at least four of these. If you recognise six, the rest of this page is written for you.
- Manufacturability is reviewed once the design is basically finished.
- The catch depends on who attended the review.
- The same issue recurs across parts with no shared memory.
- Cost drivers surface after tooling is quoted.
- The floor finds problems the drawing never showed.
- Design standards live in a document nobody opens.
- Changes late in the cycle cost an order of magnitude more.
- “Can we actually make this?” is answered by experience, not evidence.
None of this is a design problem. It is a timing problem.
Is this you?
Worked example — custom part manufacturerManufacturability used to be found at first article.
Late↓Earlywhen manufacturability issues surface
Hundreds of parts, several processes, and design rules that lived mostly in experienced heads.
Because the manufacturability check happened near the end of design, problems surfaced when changes were most expensive — and whether one was caught at all depended on who was in the room.
With AI checking the design against your own process rules as it develops, the same knowledge is applied to every part consistently, and the issues appear while they are still cheap to fix.
Design review, 9am
What you walk into the design review with
Today
A finished design and a hope.
- Manufacturability checked at the end
- Catches depend on who attended
- Cost drivers found after quoting
- Standards in a document nobody opens
→
With Praxie
A design already checked against your processes.
- Rules applied consistently to every part
- Tolerance and process issues flagged early
- Cost drivers visible before tooling
- The same knowledge applied every time
See Praxie in Action Now
Which part keeps causing problems once it reaches the floor?
Now select a day and time below.
Questions Engineering Leaders Ask Us First
The objections we hear in the first 20 minutes — answered plainly.
1
How does AI improve design for manufacturability — and can engineers trust the recommendations?
Answer: AI evaluates geometry, materials, tolerances, process requirements, supplier capabilities, and historical manufacturing data to identify likely production issues. Recommendations are explainable and traceable, so engineers can understand the rationale, review the evidence, and remain in control of every design decision.
2
What types of manufacturability risks can the AI identify?
Answer: The system can flag issues such as unnecessarily tight tolerances, difficult-to-machine features, thin walls, deep pockets, inaccessible tooling areas, excessive part complexity, unsuitable material choices, high scrap risk, difficult assembly steps, and designs that exceed available process or supplier capabilities.
3
Can it estimate the cost impact of a design decision?
Answer: Yes. AI can compare design alternatives and estimate how changes may affect material usage, cycle time, tooling, setup time, inspection effort, scrap, rework, assembly labor, and supplier cost. This helps teams make better cost-versus-performance tradeoffs earlier in the design process.
4
Does AI-powered DFM replace engineering reviews or supplier feedback?
Answer: No. It strengthens them. AI performs an initial, consistent review across a large number of design variables, surfaces the highest-risk areas, and gives engineers and suppliers a better starting point for collaboration. Final design approval remains with the appropriate engineering, quality, and manufacturing experts.
5
Can the system use our own manufacturing standards, equipment, and supplier constraints?
Answer: Yes. The AI can be configured around your approved materials, machines, tooling, process capabilities, tolerance standards, supplier requirements, quality rules, and lessons learned. This allows recommendations to reflect how your organization actually manufactures products rather than relying only on generic DFM rules.
6
How early in the product development process should AI-powered DFM be used?
Answer: As early as possible. The greatest value comes during concept development and detailed design, when changes are still relatively inexpensive. The system can also be used during design reviews, engineering change requests, supplier handoffs, and pre-production validation to catch new risks before they reach the shop floor.
7
Will it work with our existing CAD, PLM, ERP, and quality systems?
Answer: Yes. AI-powered DFM can connect design data with CAD, PLM, ERP, MES, quality, and supplier systems so recommendations reflect both engineering intent and real manufacturing conditions. This creates a connected review process instead of another isolated analysis tool.
Bottom line: AI-powered design for manufacturability helps engineering teams identify production risks earlier, reduce avoidable cost, improve supplier collaboration, and release designs that are easier to manufacture at scale.
Which part would you check first, if the answer came in minutes?
Send us one part you expect trouble with. We’ll show you what Praxie would flag — your tolerances, your processes, your cost drivers.
1Send us a part230-minute review3Free pilot on your data
See Praxie in Action
No cost, no obligation — and you keep what we find either way.