Most of our projects start with executives saying “that process is highly manual” or “we have the data, but struggle to convert it into intelligence.” iNDustry Labs’ executive advisors can help you choose the right project and staff it for successful solution prototyping.
Give us your priority challenges. We will tell you whether AI or another technology can potentially solve it.
We write a statement of work with objectives, activities, timeline, and resources. You pick an executive champion and day-to-day support team.
Two to three fellows execute the project, with an iNDustry Labs project coach clearing blockers and our AI Engineers supporting the heaviest builds.
A live demo, a documentation package and a proof of concept working system, at a minimum. Your IT and Ops team take over and get training on how to use it.
Filtered by business function rather than industry, the same bottleneck tends to look similar across sectors.
A text-to-SQL agent over PLEX ERP and Paperless Parts data, with a benchmark framework built to catch hallucinated joins before a plant manager ever sees a wrong number.
Complete
Summer 2026
Government quoting demands ITAR, NSN, and ASTM compliance across hundreds of line items. One agent parses the RFQ, one sources qualified suppliers, one scores what comes back.
Complete
Summer 2026
Twelve thousand employees across a thousand job codes, each carrying SOC, EEO-1, FLSA, and Workers' Comp metadata that cannot be lost in a merge, plus an agent that maps new roles to the consolidated set.
Complete
Summer 2026
Auto-generating 2D drawings from 3D models inside Solid Edge, capturing the datum-handling rules that live in senior engineers' heads rather than in any documentation.
Complete
Summer 2026
A retrieval agent across SAE, IEEE, internal, and customer specs, built with strict citation rigor, because an engineer who cannot verify a clause reference will not use the tool twice.
Complete
Summer 2026
A pipeline that reads decades of engineering drawings and rebuilds them as parametric SOLIDWORKS feature trees, preserving the original design intent rather than just the geometry.
A prioritization agent wired into Microsoft Planner that surfaces critical-path tasks and path blockers, so leadership attention follows the schedule rather than whoever asked most recently.
An agent that validates webform leads against web research and prior interaction history, then routes qualified ones into Sugar CRM along with the NDA exchange workflow.
Complete
Summer 2026
Candidates decline offers because they don't know enough about the region to say yes. A cost-of-living comparator and benefits view built to be shared during active recruiting.
Complete
Summer 2026
Employees were going straight to HR for routine policy and benefit questions. A self-service agent in Copilot Studio answers from C&S’s own SharePoint documentation, deployed to Teams and SharePoint, with live escalation to a person when it cannot help.
Complete
Summer 2026
Beacon associates were hunting for HR information spread across SharePoint sites, PDFs and internal pages. Ask Rudy answers from Beacon’s own curated documentation, and routes anything sensitive or benefits related to the right team instead of guessing.
Complete
Summer 2026
Specs, fitment and warranty detail were missing from product pages, so dealers rang the office instead. The team rebuilt the site on a structured product catalog and added a chatbot that declines to answer rather than guess when it is not confident.
Complete
Summer 2026
Machinists were changing tools in and out between nearly every job. An optimiser reorders the schedule and picks which tools each job should use, so the machine can run for hours without a changeover.
Part of the workforce does not read English fluently, so written policy never reached everyone. A retrieval agent answers in English, Spanish, Pashto, Dari and Bosnian, cites the document and page for every answer, and sends anyone it cannot help to a person.
Trial balance exports, sign conventions and hundreds of cross-linked formulas made every month-end close a rebuild. The team pulled ERP data straight into Excel and packaged the mapping and variance logic as reusable skills the finance team can run themselves.
Seventy-six accessory SKUs carry millions in revenue and almost none behave smoothly. Rather than force a model onto everything, the team classified each item and reported plainly that roughly a third cannot be forecast without visibility into the OEM schedule.
Apprentice progress lived across six disconnected systems with no single source of truth and no real-time view. A dashboard now pulls field reports, training scores and hours together automatically, replicating the tracking logic that had been maintained by hand in Excel.
No projects in that function yet.
Not every good business problem makes a good student project. These are the questions we work through together, and they are meant to open up possibilities rather than rule things out.
What is the challenge, and what would success look like? The more useful question is usually why it has not been solved yet.
Fellows cannot build against a system they cannot see. What exists matters more than whether it is tidy.
The most valuable person understands the problem deeply. They do not need to know what should be built. That is the project.
Worth deciding early, because it shapes the scope.
Four roles on every engagement. The combination is what makes this different from an internship.
The client. A president, CEO, or VP who owns the engagement and can unblock data access in days rather than weeks.
The engagement manager from iNDustry Labs. Sets cadence, facilitates check-ins, owns scope conversations with the champion.
Two to three students who do the work, all trained by our professional staff around best problem solving and AI practices.
Technical experts from Notre Dame's Data, AI, and Computing initiative back the heaviest builds and ensure safe cyber practices.
Recaps and project news from the AI Leaders of Tomorrow fellowship and the iNDustry Labs team.
Five fellowship pilots wrapped up with regional partners, a working answer to where RPA ends and agents begin, and the industry news worth your time.
Why a bare model invents facts, why context beats clever prompting, and three use cases that do not need a deeply technical team.
Teams presenting their working software to the partner company and the cohort. Four recordings available so far.
Projects are currently free of charge per iNDustry Labs' mission of developing a more productive region and the University's objective of providing world class applied AI skill for students.
A standing weekly call with the champion plus a day-to-day contact. The heaviest lift is front-loaded: getting fellows access to the systems and data they need.
You get a live demo, a documentation package and a proof of concept working system, at a minimum. Our team will check in with you periodically after the project to ensure the solution has been sustained.
No. This is an educational fellowship inside a university. Projects are chosen partly for what they teach. The output is real, but the purpose is students.
We provide an NDA template that ensures your data is safe. No one outside of our team sees your data without your explicit permission.
Abstracted presentations, without any proprietary data, are shared with you prior to publishing on this website or any other media. Being part of the AILOT program automatically enrolls your company into the regional AI Executive Group where knowledge sharing among peers accelerates learning.
We work in your environment and the working solution stays yours. We retain the right to use the transferable skills we developed on the project across other engagements.
Repetitive knowledge work with a clear input and output: quoting, document conversion, lead qualification, or querying a system that currently requires a specialist. If someone does it the same or similar way fifty times a week, it is worth a conversation.
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