AI ESTIMATING SOFTWARE FOR MACHINE SHOPS
AI-Powered Estimating That Actually Learns Your Shop.
Machine Research uses machine learning — trained on your real job data — to generate accurate estimates in seconds. Not a formula. Not rules. A model that gets smarter as your data grows.
Analyzes actual 3D part geometry — not just 2D drawings
Trains on your real actuals, not industry averages
Your data never leaves AWS GovCloud — never sent to an external AI system
If your estimating AI can't learn from your shop, it isn't AI.
Most estimating tools claim to use AI but rely on static rule sets — assigning fixed time values to individual features: so many seconds per hole, so many minutes per pocket. It sounds precise. The problem is, it isn't:
- Miss a feature the system can’t classify, and it never gets priced — your margin pays for it on the shop floor
- The estimates are built on generic assumptions, not your real job history — so they never reflect how your shop actually runs
- Once configured, they never update — so every time your processes, equipment, or team changes, the estimates drift further from reality
- Most shops spend months configuring them and still never get them right
You deserve software that actually learns — not one that calls static rules ‘AI.’
Machine Research was built from inside a machine shop by engineers who understood that real ML was finally mature enough to solve the quoting problem properly. The result is a system that trains on your actual recorded job data, analyzes 3D geometry the way an experienced machinist reads a part, and gets sharper as your data grows.
The proof: ML vs. linear regression on real shop data
Machine Research Machine Learning Model
trained on your real job actuals
Tight fit - few outliers
Standard rule-of-thumb formula
MRR × Volume + Surface Area × Finishing Rate
Poor fit - many outliers
This is what genuine machine learning looks like in a machine shop
Computational Geometry Engine
ML Model Trained on Your Actuals
Closed-Loop Job Feedback
Secure ML — No External AI
Unlike feature-based estimating tools, Machine Research actually learns.
Feature-Based Estimating Tools
Fixed time rules per feature — set up once, never updated
Only estimates geometry it can classify — misses everything else
Rules drift further from reality as your shop changes
Expensive to calibrate; most shops never configure them properly
Months of setup, multi-year contracts
Machine Research
Learns from your real job data and retrains over time
Analyzes full 3D geometry — nothing gets missed or lumped
Gets sharper as your data grows — reflects how your shop actually runs
Fast to deploy, intuitive to use — most shops quote in days
Up and running quickly, no long-term commitment
Everything your shop needs to quote faster — in one connected platform.
Upload CAD, PDFs, or drawing files individually or in batches — and see the actual 3D part in your browser, no CAD license required. Rotate, measure, section, and analyze geometry before your estimator writes a single number. Supports STEP, CATIA, X_T, SLDPRT, and 10 more file types.
Assembly Estimating
Estimators who’ve used both say the difference is real.
Give your shop the speed and consistency today’s buyers expect.
No steep learning curve. No heavy setup. No waiting around for the one person who ‘knows how to quote.’ Try Machine Research with your own parts before you commit to anything.
FAQS
Is Machine Research real AI or just rules and formulas?
It is machine learning. Most estimating tools use feature-based rules — assigning fixed time values to individual part features, like seconds per hole or minutes per pocket. Machine Research trains a ML model on your real recorded job actuals, so it learns the nuances of your specific machines, materials, and team. The model can be retrained as new job data comes in, getting more accurate as your data grows.
How does AI estimating actually work for a machine shop?
Machine Research reads the actual 3D CAD model — extracting removed volume, surface area, pocket depth, hole count, complexity, and other geometric features. These features feed into a machine learning model trained on your historical job actuals to predict programming, setup, and run times. The result is an estimate in seconds that reflects how your shop actually performs, not a static rule set built around generic feature types.
Why might my initial trial estimates be off for my shop?
Machine learning requires data to become accurate for a specific shop. When you start your trial, the model begins with a foundation built from real shop actuals across a range of CNC work — which is already far more nuanced than a feature-based rule set. But shops with highly specialized processes, unique materials, or proprietary machining methods will see accuracy improve significantly once the model has been trained on their own historical job data. We make this easy: send us your actuals and we’ll calibrate your instance before you go live.
Can I turn off the AI estimates and enter my own times?
Yes. Machine Research is designed to give your estimators full control. You can use the AI estimate as a starting point, adjust individual time values, or — for shops that prefer to begin without AI estimates — disable the AI layer entirely and enter your own times. The platform’s other capabilities (3D Part Viewer, ShapeSearch, Part Library, professional quoting) are fully available regardless of whether you use the AI estimating layer.
Why is Machine Research better than using a general AI tool like ChatGPT for quoting?
General AI tools like ChatGPT are not built for machine shop quoting — they have no understanding of your part geometry, your machines, your pricing logic, or your historical job data. Machine Research is purpose-built: it reads actual 3D CAD files, applies ML trained on real shop actuals, stores all job history in a centralized part library your whole team can access, and keeps every file and pricing detail within your secure AWS GovCloud environment. A general AI tool cannot open a STEP file, cannot quote a part, cannot remember your last job, and cannot protect your customers’ IP from being used to train external models. Machine Research does all of this — and gets sharper as your data grows.
Does the AI replace my estimator?
Yes — when you feed it your data. As you record actual programming, setup, and run times for completed jobs and submit them for retraining, the model becomes more accurate for your specific shop. The more real job data you provide, the more closely the model reflects how your shop actually runs.
Is my shop’s data sent to an external AI system?
No. Machine Research uses machine learning, not a large external model like ChatGPT. Models are built in-house using your data. Your proprietary part files, pricing logic, and job actuals never leave our secure AWS GovCloud environment.
What are the limitations of AI estimating?
AI performs best on parts similar to those it has been trained on. Highly unique features, extreme tolerances, or uncommon processes still benefit from human review — which is why Machine Research is designed to support experienced estimators, not replace them. For shops with very specialized work, sharing historical job data early allows the model to calibrate faster and deliver more useful estimates from the start.