Keep production and service moving.
Faster exception handling protects customer commitments when an order, asset, shipment, or schedule needs attention.
OpenNash
Prepared for
Georgia-Pacific · July 2026
A working hypothesis for Georgia-Pacific customer-experience and operations teams
Help Georgia-Pacific's mill and plant teams resolve the maintenance and production exceptions that quietly cost machine uptime.
Georgia-Pacific runs pulp, paper, tissue, and building-products mills where a stopped machine costs real money by the hour. Of the 437 open roles we read, the clear majority are maintenance, reliability, and production jobs — millwrights, industrial electricians, mechanics, and planners who keep lines moving. The first useful OpenNash workflow should help a shift team pull the maintenance, parts, and production context behind a stoppage into one place, so the next step gets decided faster instead of rebuilt by hand.
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 Georgia-Pacific workflow we can map in plain English.
Read Zero to Agent
Built by engineers from GoogleMetaSnowflakeDatabricks
Business thesis
Georgia-Pacific makes money when mills, plants, and customers run without interruption.
Georgia-Pacific depends on dependable production, asset uptime, and consistent service to customers. OpenNash helps operations teams turn messy plant, maintenance, and customer exceptions into reviewed next steps.
Georgia-Pacific company overviewCut downtime and manual coordination.
AI agents can assemble maintenance, inventory, production, and service context before supervisors spend time chasing answers.
Help plant leaders act faster.
The goal is a clear review packet that lets teams resolve more issues with the same people and better visibility.
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 Georgia-Pacific appears to be adding people
Almost every open role we found sits in plant and mill operations — maintenance, reliability, production, and the supervisors who run the floor — with only a handful of support and systems roles alongside. That is a read of public postings, so treat it as a starting hypothesis until an operator confirms where the real exceptions pile up.
Open roles reviewed
437
From koch.avature.net and related public postings.
Largest work pattern
433
Maintenance, production, and plant operations
To a working pilot workflow
14 days
No charge. On-site if useful. Staff approve everything.
437 open roles pulled from koch.avature.net · July 6, 2026
Three problems worth solving
Three problems worth solving.
MAINTENANCE, PRODUCTION, AND PLANT OPERATIONS
When a line goes down, the fix should not wait on someone rebuilding the story by hand.
Georgia-Pacific has 433 visible open roles across maintenance, reliability, and production, including Maintenance Technician (Millwright), Printing Technician, and Corrigan Production Operator. Those are the people who work the moment a machine stops, when parts history, work orders, and shift notes all have to line up fast.
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.
“Maintenance Technician (Millwright)”
IT AND PRODUCT SUPPORT
Support teams need the next answer without hunting across systems.
A smaller set of roles, including Regional IT Support Specialist and Product Support Specialist, sits between plant users and the systems they depend on. That work repeats: find the history, check the account, decide the next step, and route it to the right owner.
Our point of view
OpenNash can gather history, policy, account, and prior-case context, then draft a response or route for staff approval.
Shorter waits, more consistent answers, and fewer manager interruptions.
“Regional IT Support Specialist”
DATA AND SYSTEMS WORK
Reporting and systems work should turn into operating decisions faster.
Roles like Business Analyst and Digital Press Operator show data and production systems sitting close together. The recurring gap is turning what the systems already know into a next step an operator can act on.
Our point of view
OpenNash can turn recurring analysis, monitoring, and systems questions into source-linked review notes that connect back to the workflow operators already use.
Fewer status meetings and faster decisions from the data already available.
“Digital Press Operator”
How OpenNash would help
Turn a stalled maintenance or production 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
Georgia-Pacific operations and dispatch 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 437 Georgia-Pacific 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.
437 of 437 roles shown
| Role | Work Pattern | Location | OpenNash Fit | Source |
|---|
No roles match that search.
Pulled from Georgia-Pacific 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.