Keep production and service moving.
Faster exception handling protects customer commitments when an order, asset, shipment, or schedule needs attention.
A working hypothesis for Georgia-Pacific customer-experience and operations teams
Georgia-Pacific's visible hiring pattern points to operations and dispatch exception workflow. We would validate one workflow with operators, build a reviewed packet workflow, and measure whether cycle time and rework go down in 14 days.
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.
Business thesis
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 overviewFaster exception handling protects customer commitments when an order, asset, shipment, or schedule needs attention.
AI agents can assemble maintenance, inventory, production, and service context before supervisors spend time chasing answers.
The goal is a clear review packet that lets teams resolve more issues with the same people and better visibility.
What OpenNash is
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.
We do the workflow audit, build the agent, connect the tools, write evals, and launch against real operating cases.
Forward-deployed engineers embed with your team, watch the best operators work, and prove one workflow before you commit.
APIs, CRMs, data warehouses, dashboards, spreadsheets, inboxes, browser-only portals, and legacy systems.
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 explain the pieces in plain English: models, tools, context, approvals, evals, and why reliable agents need more than a prompt.
We connect to the tools that finish the work today and replicate the process against real test cases before automation.
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
The visible role mix points to operations, dispatch, supply chain, customer, member, patient, or agent support, and ai, data, digital, analytics, cloud. The page should be treated as a hypothesis until an operator confirms the real workflow.
437 open roles pulled from koch.avature.net · July 6, 2026
Three problems worth solving
Georgia-Pacific has 433 visible open roles in this pattern, including Maintenance Technician (Millwright), Printing Technician, and Corrigan Production Operator. That points to repeated work where context has to move cleanly between people and systems.
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)”
Georgia-Pacific has 2 visible open roles in this pattern, including Regional IT Support Specialist and Product Support Specialist. That points to repeated work where context has to move cleanly between people and systems.
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”
Georgia-Pacific has 1 visible open roles in this pattern, including Digital Press Operator. That points to repeated work where context has to move cleanly between people and systems.
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
The first pilot should make the messy handoff visible, reviewable, and measurable without replacing the systems staff already use.
How the first 14 days run
Georgia-Pacific operations and dispatch exception workflow
Sit with the team that owns the workflow and record the decision points, source systems, exceptions, and approval rules.
Define what context the reviewer needs, what OpenNash drafts, and what must stay human-approved.
Turn real requests into source-linked packets inside a small review workflow.
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
Search by title, location, work pattern, source evidence, or how OpenNash would help. This is the full role list behind the hypothesis above, not a curated sample.
| Role | Work Pattern | Location | Public Evidence | OpenNash Fit | Source |
|---|
Pulled from Georgia-Pacific public postings on July 6, 2026 · every source link goes to the original posting where available.
The ask
We will map where an AI agent can help, what should stay human-approved, and what test cases would prove it works.