Revenue and customer operations
Inquiries sit, quotes take too long, follow-up gets missed, or customer handoffs lose momentum.
AI workflow implementation
Turn one operational bottleneck into a working AI workflow
We map one workflow against its baseline, build the automation inside the systems your team already runs, and stay through live operation until the change is measured. One workflow at a time, not an open-ended AI program.
Engagement record
- Scope
- One operational workflow, named and bounded
- Owner
- A named decision-maker on your side
- Evidence
- Baseline, operating record, accepted result
- Review
- Human approval on every exception
Map · Build · Operate · Measure
Four stages, each closed with an artifact of record
The work moves in four controlled stages. Each one ends with a deliverable your team keeps: the opportunity map, the working implementation, the operating record, and the accepted result.
How the engagement runsPlate I
01 · Map
Choose the right work
Trace volume, delays, exceptions, systems, owners, and approval points.
02 · Build
Fit your systems
Configure the workflow around the tools and decision points already in use.
03 · Operate
Stay through real use
Handle exceptions, tune the work, and keep human authority visible.
04 · Measure
Decide what comes next
Compare the result to the accepted baseline and make the next call.
One record runs from the first baseline to the accepted result. The decision to continue, reshape, or stop is read from it — nowhere else.
Start with the friction
Where does the work slow down?
The strongest first project has a visible problem, a named owner, and a decision worth improving.
Finance and reconciliation
Records do not match, exceptions pile up, and people spend hours checking two systems by hand.
Documents and approvals
Invoices, contracts, forms, and email attachments have to become structured work with a human decision.
Sensitive-data workflows
The work matters, but data handling, approvals, and retained records need a stronger boundary.
One market, two buyer contexts
Established team or founder-led operator? The work determines the fit.
Established teams often bring more systems and controls. Founder-led businesses often start with revenue, customer, or operating work consuming attention. In both cases, a named owner and usable data matter more than a public revenue cutoff.
The Workflow Opportunity Map
Know what to change before you fund the build
A fixed-scope engagement for one operational workflow, typically completed in two to four weeks; the schedule is confirmed before work begins. You keep all five deliverables whether we build the next stage or not.
Every engagement is quoted case by case. Price follows workflow complexity, system access, data boundaries, decision authority, acceptance criteria, and operating support.
- 01
- Current-state workflow mapSystems, handoffs, exceptions, owners, and decisions.
- 02
- Measured baselineVolume, cycle time, manual touches, and starting point.
- 03
- Automation and control designModel, code, and human responsibilities separated.
- 04
- Implementation briefBuild path, acceptance criteria, dependencies, and stop conditions.
- 05
- Go, reshape, or stop decisionA direct recommendation, including when not to automate.
Evidence, with the boundary visible
What is real. What is demonstrated.
A running workflow and a runnable sample are different facts. We label both.
Owner-attested runtime
A $10 million invoicing backlog, taken apart
A field-services operator had $10 million of completed work it could not bill — field sales tickets weren’t matching the purchase orders that authorize an invoice. A workflow on the operator’s infrastructure now matches tickets to purchase orders, feeds QuickBooks invoicing, and stages submission into the customer’s SAP Ariba portal, with a person reviewing every exception.
Establishes: the $10M backlog, the matching workflow, and a second signed engagement to automate ticket matching, QuickBooks invoicing, and SAP Ariba submission end to end. Does not establish: a named customer, a quantified outcome, or VeilEngine usage.
Runnable sample
Check a signed receipt offline
Download a synthetic receipt, its signer key, and the MIT-licensed verifier. Run the check without calling Vertical Edge AI.
Establishes: the sample signature and hashes verify offline. Does not establish: customer deployment, provider behavior, business outcomes, or production maturity.
When the work needs a stronger boundary
VeilEngine is built to keep sensitive data from reaching the model — and to make every execution inspectable.
Two protections in one layer. Before your data leaves your boundary, VeilEngine is designed so the model receives only protected data — a safeguard that does not depend on your provider’s terms. After it runs, the execution record is signed and can be checked offline, by you or by an auditor, without trusting us. VeilEngine is a supporting part of the architecture, not a requirement for every engagement.
Synthetic sample
- sensitive-data handling
- protected before egress (designed so)
- receipt signature
- offline-checkable
- artifact hashes
- offline-checkable
- sample payload
- synthetic
- business outcome
- not established
status labels travel with the claim
Next step
Bring the workflow whose delay you can already measure
The Opportunity Map ends with a go, reshape, or stop recommendation and an implementation brief you keep.