Protect output and customer trust.
Faster quality and maintenance reviews help keep orders, launches, and service commitments moving.
OpenNash
Prepared for
3M · July 2026
A working hypothesis for 3M plant, quality, and technical-sales teams
Help 3M's plant, lab, and quality teams clear the exceptions that quietly slow production and shipments.
3M runs thousands of products through plants, labs, and quality reviews across dozens of divisions, and every launch, order, and shipment turns on evidence-heavy decisions. The open roles cluster in manufacturing, quality, lab work, technical sales, and supply chain, where a missing document or unclear next step stalls the line. The first useful OpenNash workflow would take one recurring exception, assemble the context from the systems teams already use, and let an operator approve the next step faster.
OpenNash builds custom 24/7 AI agents for customer support, back-office, and operational work. We automate workflows end to end inside the systems your team already uses: secure, auditable, and human-reviewed where it matters.
Engineers who build AI agents that work.
Start with the Zero to Agent guide, then bring one real 3M workflow we can map in plain English.
Read Zero to Agent
Built by engineers from GoogleMetaSnowflakeDatabricks
Business thesis
3M makes money when plants, products, and customer commitments stay reliable.
3M's operating model depends on many small decisions across manufacturing, quality, maintenance, and customer support. OpenNash helps teams clear evidence-heavy exceptions before they slow production or service.
3M SEC filingsReduce rework in plant operations.
When teams spend less time chasing missing context, they can avoid repeated checks, delays, and handoffs.
Make every operator review faster.
Source-linked packets let teams approve, edit, or reroute more exceptions without changing the systems they already use.
What OpenNash is
Reliable, auditable AI workflows for the work that actually runs the business.
We study how your best humans solve hard work, replicate the skill, and build AI agents that automate the repetitive parts while keeping people in control of exceptions, approvals, and judgment calls.
M.01
Time to production: 4-8 weeks
We do the workflow audit, build the agent, connect the tools, write evals, and launch against real operating cases.
M.02
14-day no-charge pilot
Forward-deployed engineers embed with your team, watch the best operators work, and prove one workflow before you commit.
M.03
Built on your software
APIs, CRMs, data warehouses, dashboards, spreadsheets, inboxes, browser-only portals, and legacy systems.
M.04
U.S.-based, on site if useful
We will fly to you, work with the people doing the work, and price the pilot risk so you do not have to.
Zero to Agent
We teach the basics, then build inside your real work.
Step 01
Learn
We explain the pieces in plain English: models, tools, context, approvals, evals, and why reliable agents need more than a prompt.
Step 02
Build
We connect to the tools that finish the work today and replicate the process against real test cases before automation.
Step 03
Launch
Human-in-the-loop review, monitoring, audit logs, recovery paths, and automated tests keep the agent reliable in production.
Evaluations are the difference between a demo and a production workflow. We write test cases for incomplete requests, unusual documents, portal errors, approval paths, and edge cases so the agent can fail safely, ask for help, and improve from real reviewer feedback.
Research snapshot
Where 3M appears to be adding people
3M's public postings cluster around plant and lab operations, technical sales, and supply chain, the teams that work through evidence-heavy exceptions every day. This is a read from public postings, not an internal org chart, so treat it as a starting hypothesis until an operator confirms the real workflow.
Open roles reviewed
694
From 3m.wd1.myworkdayjobs.com and related public postings.
Largest work pattern
450
General & other roles
To a working pilot workflow
14 days
No charge. On-site if useful. Staff approve everything.
694 open roles pulled from 3m.wd1.myworkdayjobs.com · July 6, 2026
Three problems worth solving
Three problems worth solving.
MANUFACTURING, LAB & QUALITY WORK
Repeated review work should become a measured workflow.
3M is hiring across manufacturing, labs, and cross-functional roles, including Laboratory Technologist, Manufacturing Supervisor, and Specialist / Manager - Activation Marketer. These roles turn scattered records, specs, and approvals into next steps someone has to assemble by hand.
Our point of view
OpenNash can gather context from existing systems, draft the next step, and show staff exactly why the recommendation was made.
Less manual coordination and a clearer view of where work gets stuck.
“Laboratory Technologist”
SALES, ORDERS, FIELD SERVICE
Sales and field teams lose time turning messy notes into next steps.
3M's field organization spans sales, technical sales, and account management, including Area Sales Manager, National Sales Manager - Trade, and Senior Sales Engineer. Every deal turns notes, quotes, and specs into follow-up work someone tracks by hand.
Our point of view
OpenNash can convert orders, quotes, visit notes, warranty details, and customer updates into reviewed next-step packets.
More time with customers and fewer dropped follow-ups.
“Area Sales Manager”
OPERATIONS, DISPATCH, SUPPLY CHAIN
Operational exceptions should not wait for someone to rebuild context by hand.
3M has 65 visible open roles in this pattern, including Sr. Specialist - Global Molding Procurement, Regulatory Affairs Manager - Transportation Safety business, and Demand Planner, Brazil. That points to repeated work where context has to move cleanly between people and systems.
Our point of view
OpenNash can watch the workflow, gather route, order, inventory, or shipment context, draft the next step, and keep operators in control.
Faster handoffs and fewer unresolved exceptions at shift change.
“Sr. Specialist - Global Molding Procurement”
How OpenNash would help
Turn one recurring 3M exception into a reviewed workflow.
The first pilot should make the messy handoff visible, reviewable, and measurable without replacing the systems staff already use.
- The workflow stays inside the operating workflow.
- Every recommendation links back to source context.
- The pilot measures whether the workflow is worth expanding.
How the first 14 days run
One workflow, live in two weeks, measured honestly.
First workflow we would test
3M reviewed operating exception workflow
Day 1
Watch the work
Sit with the team that owns the workflow and record the decision points, source systems, exceptions, and approval rules.
Day 3
Map the packet
Define what context the reviewer needs, what OpenNash drafts, and what must stay human-approved.
Day 8
Run live examples
Turn real requests into source-linked packets inside a small review workflow.
Day 14
Measure honestly
Review cycle time, approval rate, edits, rework, and the exceptions that should stay manual.
No charge for the pilot. U.S.-based team — we fly to you. OpenNash connects to the systems your teams already use; nothing is replaced. Every draft, summary, and routing decision lands in a simple review flow where your staff approve, edit, or reject it, with a link back to the source and an audit trail of every action.
Structured role evidence
All 694 3M roles on this page, searchable.
Search by title, location, work pattern, or how OpenNash would help. This is the full role list behind the hypothesis above, not a curated sample.
694 of 694 roles shown
| Role | Work Pattern | Location | OpenNash Fit | Source |
|---|
No roles match that search.
Pulled from 3M public postings on July 6, 2026 · every source link goes to the original posting where available.
The ask
Show us one real workflow from this week.
We will map where an AI agent can help, what should stay human-approved, and what test cases would prove it works.