Keep more trucks ready to move.
Faster driver and compliance reviews help protect available capacity and service reliability.
OpenNash
Prepared for
CRST International · July 2026
A working hypothesis for CRST International
Help CRST clear the dispatch and compliance work behind each load so drivers spend more time on the road.
CRST's open postings are almost entirely CDL-A drivers - over-the-road team lanes, owner-operator and lease-purchase seats, and a few regional and local runs. Behind each of those drivers is a steady stream of onboarding documents, compliance checks, settlements, and load-status questions that operators still handle by hand. The first useful OpenNash workflow would take one of those recurring exceptions and turn it into a reviewed, source-linked packet that an operator approves.
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 CRST International workflow we can map in plain English.
Read Zero to Agent
Built by engineers from GoogleMetaSnowflakeDatabricks
Business thesis
CRST makes money when drivers, loads, and compliance move without delay.
CRST's business depends on getting drivers ready, compliant, and matched to freight quickly. OpenNash helps teams clear onboarding and operating exceptions before they slow capacity.
CRST company overviewReduce manual follow-up.
Cleaner packets can cut repeated document checks, missing-information loops, and recruiter or operations rework.
Make every driver operations team faster.
OpenNash can prepare reviewable packets so staff can approve, edit, or escalate more cases in less time.
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 CRST International appears to be adding people
Every visible CRST posting is a CDL-A driver role, weighted heavily toward over-the-road team lanes with a handful of owner-operator, regional, and local seats. That mix suggests the busiest back-office work sits around dispatch, driver onboarding, and settlements - a hypothesis worth confirming with the operators who live it.
Open roles reviewed
12
From crstrecruiter.crst.com and related public postings.
What CRST is hiring
CDL-A lanes
Every open seat is a driving lane — team, regional, local, and owner-operator.
To a working pilot workflow
14 days
No charge. On-site if useful. Staff approve everything.
12 open roles pulled from crstrecruiter.crst.com · July 6, 2026
Three problems worth solving
Three problems worth solving.
DISPATCH AND LOAD STATUS
Dispatch exceptions should not wait while someone rebuilds a load's story by hand.
Most open roles are over-the-road and team lanes, where a single delayed load ripples across appointments, driver hours, and customer updates. When a load slips, an operator often rebuilds the full picture from several systems before deciding the next move.
Our point of view
OpenNash would assemble load, route, appointment, and driver-hours context into one reviewed packet, then keep the dispatcher in control of the next step.
Faster handoffs at shift change and fewer loads left waiting on a status answer.
“CDL A Team Truck Driver”
DRIVER ONBOARDING AND COMPLIANCE
New drivers should be road-ready without a week of chasing documents.
Nearly every open role is a CDL-A seat, so onboarding and qualification never stop. Each new driver brings a DOT qualification file - CDL, medical card, MVR, and prior employment - that staff assemble and re-check by hand before dispatch.
Our point of view
OpenNash would gather each driver's qualification documents, flag what is missing or expiring, and hand recruiting or safety a reviewed packet to approve - no autonomous decisions.
Drivers cleared to run sooner and fewer compliance gaps found late.
“CDL-A Truck Driver”
SETTLEMENTS AND OWNER-OPERATOR PAY
Pay and settlement questions should not pull operators off the next load.
CRST runs owner-operator and lease-purchase lanes alongside company drivers, each with its own settlement math, deductions, and paperwork. BOL, detention, and pay questions arrive mid-shift and send staff digging through documents to answer one driver.
Our point of view
OpenNash would pull the trip, BOL, and settlement detail behind a pay question into one source-linked packet, so staff can confirm the answer and keep moving.
Quicker, better-documented answers for drivers and less rework in settlements.
“CDL A Owner Operator/ Lease Purchase Team Truck Driver”
How OpenNash would help
Turn dispatch and compliance exceptions into a reviewed, source-linked 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
CRST International 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 12 CRST International 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.
12 of 12 roles shown
| Role | Work Pattern | Location | OpenNash Fit | Source |
|---|
No roles match that search.
Pulled from CRST International 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.