Protect shelf availability.
Faster answers around supply, inventory, quality, and customer commitments help keep products moving and protect demand when plans change.
OpenNash
Prepared for
General Mills · July 2026
A working hypothesis for General Mills
Keep every General Mills SKU on the shelf when the plan changes.
General Mills runs on trusted brands and clean execution across manufacturing, supply planning, and food safety. Its open roles skew to supply-chain operations, data and technology, and quality. The first useful OpenNash workflow would turn the scramble around a plan change — a supply hiccup, a spec question, a quality hold — into a source-linked packet a planner or reviewer can act on fast.
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 General Mills workflow we can map in plain English.
Read Zero to Agent
Built by engineers from GoogleMetaSnowflakeDatabricks
Business thesis
General Mills makes money when products move reliably from plant to shelf.
General Mills runs on trusted brands, steady supply, and clean execution across manufacturing, quality, planning, and retail channels. OpenNash helps teams resolve the small operating exceptions that slow down production, service, and replenishment.
General Mills SEC filingsReduce rework across operations.
AI agents can gather the right order, plant, supplier, and policy context before a team member decides what to do next.
Make every planner and operator faster.
Source-linked review packets let teams handle more exceptions without adding more meetings, searches, or manual follow-up.
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 General Mills appears to be adding people
The visible role mix clusters around data and technology, supply-chain operations, and food safety and quality. That does not prove the exact workflow, but it points to recurring exceptions where context has to move between plants, planners, and systems — a hypothesis worth confirming with an operator.
Open roles reviewed
316
From careers.generalmills.com and related public postings.
Largest work pattern
102
AI, data, digital, analytics, cloud
To a working pilot workflow
14 days
No charge. On-site if useful. Staff approve everything.
316 open roles pulled from careers.generalmills.com · July 6, 2026
Three problems worth solving
Three problems worth solving.
AI, DATA, DIGITAL, ANALYTICS, CLOUD
Reporting and systems work should turn into operating decisions faster.
General Mills has 102 visible open roles in this pattern, including D&T Analyst I – Master Data Operations, Sr. D&T Engineer – Intelligent Automation, and Analyst - Supply Planning - PLPM - Global Planning Hub. That points to repeated work where context has to move cleanly between people and systems.
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.
“D&T Analyst I – Master Data Operations”
OPERATIONS, DISPATCH, SUPPLY CHAIN
Operational exceptions should not wait for someone to rebuild context by hand.
General Mills has 77 visible open roles in this pattern, including Facilities Maintenance Technician, Maintenance Technician, and Electrical Controls Technician. 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.
“Facilities Maintenance Technician”
QUALITY, SAFETY, COMPLIANCE
Evidence work should be assembled before it reaches the reviewer.
General Mills has 39 visible open roles in this pattern, including Food Safety & Quality Engineer II, Health, Safety & Environmental Specialist, and Sanitation Technician. That points to repeated evidence work where records have to be gathered and checked before a reviewer signs off.
Our point of view
OpenNash can collect source records, compare them to standard work, and prepare an exception packet with an audit trail.
Cleaner reviews, fewer missing fields, and a better record of why decisions were made.
“Food Safety & Quality Engineer II”
How OpenNash would help
Turn analytics and systems review packets 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
General Mills analytics and systems review packets
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 316 General Mills 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.
316 of 316 roles shown
| Role | Work Pattern | Location | OpenNash Fit | Source |
|---|
No roles match that search.
Pulled from General Mills 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.