Manufacturing & Industrial Automation Consulting

Vision, learning, and modeling systems for lines that can't afford to guess.

Protocess Technologies builds machine-vision, machine-learning, and process-modeling solutions for large industrial manufacturers, from part-level inspection at production speed to plant-wide predictive models. We get paid when the automation works.

Book a Process Audit How We Structure Engagements
0% Part ID accuracy achieved
in fielded vision systems
0ms Typical inference latency
at line speed
0$ upfront On qualifying ROI-based
profit-share engagements
Deployed on industry-standard machine vision platforms, including Keyence and Cognex systems, trusted across automotive, packaging, and precision-parts manufacturing
2D/3D Machine Vision Deep-Learning Inspection Multi-Camera Profiling Edge Inference PLC / MES Integration

What We Do

We match the method to the process, not the other way around

Protocess exists to make industrial processes measurable and controllable. Machine learning, computer vision, and digital twins are common examples of how we get there, but they aren't a fixed menu. We start from the decision that needs to get better, then apply whatever actually fits: a deep-learning defect classifier, a physics-based model, or a straightforward regression across a handful of process variables.

Machine Vision & Part Differentiation

High-speed 2D/3D inspection built primarily on industry-standard platforms like Keyence's IV series and VS line, or Cognex's In-Sight systems. In most engagements we're integrating and tuning proven hardware rather than building our own, then configuring it to identify part variants, verify features, and catch defects at full line rate for your specific part mix.

  • Part ID & automatic recipe selection
  • Surface, dimensional & assembly defect detection
  • 3D profilometry for form & flush/gap checks

How our defect inspection works →

Machine Learning & Predictive Models

Models trained on your process data to predict yield, flag drift before it produces scrap, and recommend set-points, deployed at the edge or integrated into existing historian and MES infrastructure.

  • Predictive quality & anomaly detection
  • Process parameter optimization
  • Failure-mode & remaining-useful-life models

Modernized Process Modeling

Digital-twin and simulation models that replace static spreadsheets and tribal knowledge with a live, testable representation of your process, used to plan changeovers, capacity, and automation ROI before capital is committed.

  • Digital twins of lines & work cells
  • Throughput & bottleneck simulation
  • What-if modeling for capex decisions

Data Acquisition & Sensor Integration

Instrumentation and data pipelines for plants that don't yet have reliable ground-truth data, so you can finally see what's happening on the floor: how parts are actually flowing, where productivity is being lost, and where quality is slipping.

  • Part flow & routing visibility across the line
  • Productivity & throughput metrics (OEE, cycle time, downtime)
  • Quality & yield metrics at the station level

Replacing the day-by-hour sheet →

Illustrative Scenario

Statistical Modeling & Root-Cause Analysis

Not every problem is a vision or deep-learning problem. Consider a hypothetical chemical process with an intermittent defect and dozens of candidate causes: reactor temperature, flow rates, residence time, catalyst concentration, feedstock lot. Cataloging those variables and fitting a Bayesian regression against measured defect rates would produce a ranked, uncertainty-aware picture of which ones genuinely move the outcome and which only appear to, pointing engineering effort at the changes most likely to pay off.

  • Process variable cataloging across disconnected systems
  • Bayesian regression relating variables to defect & yield outcomes
  • Credible intervals that separate real drivers from noise
  • Prioritized, testable changes rather than a black-box score

Featured Application

Defect inspection with tolerance you control, not us

Every feature we inspect, surface spotting, flush/gap, bore diameter, hole position, gets measured at full line rate. You set how tight the pass/fail threshold is, per feature, per part variant, per recipe.

Full defect inspection capability →
LIVE INSPECTION PREVIEW FEATURE: SURFACE SPOT COVERAGE
Spot coverage --% READY
2%Spot coverage threshold35%
0Accepted
0Rejected
0%Yield at this threshold

Set the pass/fail line yourself: per feature, per part, per recipe

Out of the box, platforms like Keyence and Cognex ship with a single global sensitivity setting. We configure and layer control logic on top so the threshold becomes a first-class, per-feature parameter, because a cosmetic surface blemish and a safety-critical bore diameter don't belong on the same setting.

  • Independent thresholds per feature, part variant, and recipe, no shared setting across dissimilar parts
  • Adjusted in real engineering units (percent coverage, mm, degrees, pixel count) without touching model code
  • Tighten for safety-critical features, relax for cosmetic ones, and see the yield trade-off before you commit
  • Every accept/reject decision is logged against the threshold active at that moment, for full traceability

Start the line: each part arrives with a randomized percentage of surface spotting, gets scanned, and is either passed down the line or dropped through the reject hatch, with a new part indexed every four seconds once it's running. Move the slider and the yield across every part scanned so far recalculates live.

How We Work

We price to the value we create, not the hours we bill

Automation consulting is full of misaligned incentives: firms that get paid the same whether the system works or not. We split our engagement model by project type so incentives stay aligned with what's actually deliverable.

How the profit share is measured →
ROI-Based Projects

Profit-Sharing Model

When a project has a measurable financial outcome (reduced scrap, higher throughput, avoided downtime, labor reallocation) we take a share of the value we generate instead of a large upfront fee.

  • Reduced capital risk on your side: we're funded by results, not a line item
  • Jointly defined baseline & measurement methodology before kickoff
  • Our team stays engaged post-deployment because our return depends on it
Data Acquisition Projects

Fixed-Rate Model

Instrumentation, sensor integration, and data-infrastructure work rarely has a direct, attributable profit number on its own; it's the foundation later projects are built on. For this category, we scope and bill at a fixed, predictable rate.

  • Transparent scope and cost, agreed before work begins
  • No ambiguity when there's no direct revenue line to share against
  • Often the phase-one step that qualifies you for a later profit-share phase

Part Identification & Recipe Selection

Vision-driven part differentiation with automatic recipe selection

We have built a computer vision part identification system that modulated process recipes for a client. What follows is how that class of system works, described generally.

Full part identification write-up →

The problem it addresses

On a line running mixed part variants, the process has to change per part, and the thing that knows which part is coming is usually a person. That leaves room for mis-selection, time lost to manual changeover, and a process that quietly depends on who is running it.

How a system like this works

A vision-based control system, running on Keyence/Cognex-class camera hardware, would capture each part in-line, classify its variant against a trained model, and push the corresponding recipe (process parameters, fixture settings, torque and pressure profiles) to the controlling PLC before the part reaches the next station.

The same pattern extends to part ID & sorting, recipe/fixture selection, kitting verification, and assembly-sequence gating: anywhere a process needs to know what is in front of it before deciding what to do.

How the system handles low-confidence parts →

Industries

Built for high-mix, high-consequence production

Automotive & Tier Suppliers

Part traceability, assembly verification, torque/fixture selection across mixed platforms.

Precision & Machined Parts

Dimensional inspection, form/flush-gap checks, 3D profiling for tight-tolerance components.

Packaging & Consumer Goods

Code reading, fill/seal verification, high-speed defect sorting at line rate.

Heavy & Industrial Equipment

Weld inspection, surface defect detection, predictive maintenance modeling.

About Protocess

Automation consultants who stay accountable to the outcome

Protocess Technologies LLC was founded to close the gap between industrial automation vendors selling hardware and consultants selling hours, neither of whom carries the outcome with you. We design, build, and stand behind the control logic and integration work around the vision, ML, and modeling systems we deploy, and our profit-sharing model on ROI work means we only win when your line does.

Vendor-agnostic engineering

We select and integrate the vision, sensing, and compute hardware that fits your process, typically platforms like Keyence and Cognex, rather than pushing a fixed product catalog.

Built to integrate, not replace

Solutions are engineered to sit alongside your existing PLC, MES, and historian infrastructure.

Incentives that match the work

Profit-share where there's a measurable return; fixed-rate where the value is foundational, not direct.

Start a Project

Tell us about your line

Share a bit about your process and where it's breaking down: manual part sorting, scrap you can't explain, a modeling gap. We'll follow up to scope whether it's a profit-share or fixed-rate engagement.

Protocess Technologies LLC Manufacturing & Industrial Automation Consulting