Manufacturing & Industrial
AI consulting for manufacturing shops that need faster quotes and fewer surprises
AI consulting for manufacturing starts with bottlenecks you already know: the RFQ pile on the estimator's desk, ERP data nobody trusts, the machine that fails during a rush order, and the know-how that retires with your best machinist. NextGen Code works with job shops, machine shops, fabricators, and small-to-mid manufacturers to put AI on those problems first.
KPIs we baseline and move
- 01Quote turnaround time
- 02Quote win rate
- 03On-time delivery
- 04OEE (overall equipment effectiveness)
- 05Scrap and rework rate
- 06Unplanned downtime hours
The opportunity
Faster quotes, cleaner ERP data, fewer surprises on the floor, and customer drawings kept secure.
We don't open with sensors and a data lake. We open your quote log, routings, scrap reports, and maintenance history, and the first wins usually come from data you already collect. When a project does need new data, such as spindle load and cycle times captured over MTConnect or OPC UA (the common machine-data standards), we prove it on one machine or cell before scaling.
Security comes first for shops that hold customer drawings or sell into the defense supply chain. Controlled technical data, CUI (controlled unclassified information), and customer IP never go into public AI tools, and every workflow we design is scoped to your ITAR, export control, and CMMC obligations.
What gets in the way
The problems we solve for manufacturing businesses.
Quoting is the bottleneck
Estimators read each drawing, check material and tolerances, work out routings and cycle times, and chase outside processing prices, one RFQ at a time. Slow quotes lose work, and rushed quotes lose money.
The ERP doesn't match the floor
Routings carry outdated times, part numbers are duplicated, inventory counts drift, and BOMs don't reflect how parts are really made. Every report built on that data is wrong in ways people quietly work around.
Unplanned downtime
Spindles, hydraulics, compressors, and chillers fail on their own schedule. Maintenance runs on calendars and instinct, and one breakdown can make every order behind it late.
Inspection is slow and inconsistent
Manual visual inspection is tiring and varies by shift and inspector. A defect caught at the customer costs far more than one caught at the machine.
Scheduling by spreadsheet
Hot jobs, late material, and machine conflicts reshuffle the schedule daily, usually in a spreadsheet only one planner understands. When sales asks about capacity, the answer is a guess.
Knowledge walks out the door
Setup tricks, fixture locations, and the reasons behind process changes live in senior people's heads. New hires take months to get productive, and the SOP binder hasn't been opened since the last audit.
AI use cases
Where AI pays off in manufacturing.
- 01
RFQ intake and quote drafting from drawings
Before: an estimator opens each PDF drawing and STEP file, reads the title block and notes, and builds the quote from scratch. After: AI extracts material, finish, tolerances, quantities, and special requirements, finds similar past jobs with their actual costs, and drafts routings and a price for the estimator to adjust. Example: cutting quote prep from 90 minutes to 30 across 60 RFQs a month frees 60 estimator hours.
- 02
ERP and MRP data cleanup
AI compares routings with actual labor tickets to flag outdated cycle times, finds duplicate and near-duplicate part numbers, and spots inventory records that don't match usage. A person approves every change, and the cleaner data makes scheduling, costing, and every later AI project more accurate.
- 03
Predictive maintenance on the assets that stop the plant
Start with the two or three machines whose failure halts production. Vibration, temperature, current, and alarm data feed a model that flags abnormal patterns early enough to plan the repair, so maintenance gets a work order instead of a surprise. Example: if one unplanned spindle failure costs $25,000 in repairs, overtime, and expedited freight, avoiding one a year pays for a lot of monitoring.
- 04
Computer-vision inspection at the station
A camera and a trained vision model check parts for surface defects, missing features, or assembly errors at production speed and route questionable parts to a human inspector. Results are logged by part, lot, and shift, which speeds up root-cause analysis and customer quality reports.
- 05
Scheduling and capacity planning
AI proposes a schedule that respects machine capabilities, setups, material availability, and due dates, then re-plans when a machine goes down or a hot job lands. Sales gets realistic ship dates, and the planner works the exceptions instead of rebuilding the whole board.
- 06
A shop-floor assistant for SOPs and setups
Operators ask a tablet at the machine for a torque spec or the setup steps for a part on the Haas in cell 3, and get an answer from your controlled work instructions, setup sheets, and manuals, with the source document cited. Safety-critical procedures such as lockout/tagout appear verbatim from the approved document, never paraphrased.
- 07
Supplier and inventory analytics
AI tracks supplier lead times, on-time performance, and price changes from purchase orders and receipts, and flags reorder points that no longer match reality. Buyers see which parts are at risk of shortage before a job is released to the floor.
- 08
Quality documentation and customer reporting
First article inspection reports, certificates of conformance, and corrective action responses are drafted from inspection data and nonconformance records for the quality manager to review. In AS9100 or IATF 16949 shops, the drafts go through your document control process, not around it.
Compliance, built in
General guidance, not legal advice. We work alongside your counsel and compliance team.
- ITAR technical data can't be released to foreign persons without authorization, and uploading it to a public AI service may count as an export. Use environments built for controlled data, or rely on ITAR's end-to-end encryption provision only when every condition is met; EAR-controlled technology has comparable limits.
- DoD began adding CMMC requirements to contracts on November 10, 2025, and third-party Level 2 assessments become a condition of award for applicable contracts starting November 10, 2026. Any AI tool that stores, processes, or transmits CUI falls inside your assessment boundary.
- Under DFARS 252.204-7012, cloud services that store, process, or transmit covered defense information must meet security equivalent to the FedRAMP Moderate baseline, and cyber incidents must be reported to DoD within 72 hours. That includes cloud-based AI services.
- Purchase order terms and NDAs often restrict sharing customer drawings with third parties. Choose AI services that contractually rule out training on your data and keep it in a defined region, and read customer terms before connecting anything.
- Under ISO 9001, AS9100, or IATF 16949, AI-generated work instructions and reports are controlled documents that need review and approval like any other. This is an overview, not legal or export-control advice; have your export compliance lead or counsel classify your data.
How we help
Services that fit.
AI Opportunity Assessment
A fixed-scope, 2–3 week deep dive into how your business runs, ending in an ROI-ranked AI roadmap.
Learn moreCustom AI Development
LLM apps, RAG, copilots, document AI, and computer vision, built and tested like production software.
Learn moreBusiness Intelligence & Dashboards
KPIs, pipelines, and dashboards that tie back to your books, so every meeting starts from the same numbers.
Learn moreBusiness Analysis & Process Optimization
A top-down review of how your business runs, from first call to final invoice, with a ranked plan to fix it.
Learn moreCustom & Enterprise Software
Internal tools, ERPs, portals, and integrations built around how your business runs, with AI where it pays.
Learn moreFractional CTO & Technology Advisory
Part-time technology leadership for strategy, AI, security, vendors, and hiring, explained in plain English.
Learn moreNext step
Let's find the AI wins in your manufacturing business.
A 30-minute call with a consultant who knows your industry's workflows. You'll leave with two or three concrete ideas, whether or not we work together.
FAQ
AI for manufacturing: FAQ
Still have a question? Ask us directly.
Can AI quote parts from a drawing?
AI can read a drawing and draft most of a quote, and an estimator should still own the price. Current models can extract material, dimensions, tolerances, finishes, and notes from clean PDF drawings, and they're good at finding similar past jobs and their actual costs. They're weaker on unusual geometry, tight-tolerance judgment calls, and outside processing. The practical design is simple: AI drafts, the estimator decides, and actual job costs feed back to sharpen the next quote.
Is it safe to put customer drawings into AI tools?
Only into tools whose contracts, data handling, and hosting match the drawing's sensitivity. Commercial drawings under NDA need an AI service that doesn't train on your data, keeps it in a defined region, and limits access to your team. ITAR, EAR-controlled, or CUI drawings need an environment approved for that data, often a government cloud offering, and some shouldn't go to an AI service at all. We classify your data first and design around the strictest category involved.
Do we need sensors and new equipment to start?
Usually not. Many shops get their first AI results from data they already have: quote history, ERP routings, labor tickets, inspection records, and maintenance logs. Newer CNC machines often expose data through MTConnect or OPC UA, and older machines can be fitted with inexpensive current or vibration sensors once a predictive maintenance pilot justifies it. We pilot on one machine or cell, prove the value, and then scale.
Which ERP systems do you work with?
In most cases, the one you already run, whether that's JobBOSS², E2 Shop System, Epicor Kinetic, ProShop, Global Shop Solutions, NetSuite, SAP Business One, or Microsoft Dynamics 365 Business Central. Integration depth depends on the system's APIs and how it's hosted, and some on-premise installs need a database connector or scheduled export. Before recommending any change, we'll tell you whether the ERP is the real problem or just the data in it.
What does a first AI project look like for a 50-person manufacturer?
Usually one focused project with a clear payback, scoped to show results in weeks rather than a year. Common starting points are RFQ intake and quote drafting, an SOP assistant for one department, or a scrap and downtime dashboard that finally shows where the money goes. We begin with an AI Opportunity Assessment that ranks options by impact, feasibility, and risk using your own numbers, then build the top item in short cycles.
From the Journal
Further reading
AI Readiness Assessment: A 5-Pillar Framework and Scorecard
Run an AI readiness assessment across five pillars: 15 questions, a 0–100 scoring rubric, and next steps for each tier, plus a free interactive tool.
Business Intelligence for Small Business: Tools, KPIs and a 30-Day Plan
Business intelligence for small business in plain English: when to leave spreadsheets, BI tools compared, KPIs by department and a 30-day dashboard plan.
Custom Software vs. SaaS: How to Decide in the Age of AI
Custom software vs SaaS: when each wins, a 3-year cost comparison with every assumption shown, how AI changes build vs buy, and what to ask a developer.