Agents that ship measurable work
Not demos or pilots that die in a sandbox. These are autonomous agents in production — governed, integrated, and tracked against a hard baseline. Here's what they actually moved.
- Production deployments, not POCs
- Outcomes tracked vs. baseline
- Across regulated industries
- References on request
AI agents that run accounts payable
Diedrich Roasters builds commercial coffee roasting equipment and runs Odoo Online for accounting and Ramp for company cards, with roughly 250 vendor bills a month. The team was retyping what Odoo had already half-read.
Two AI agents now review every vendor bill and card charge field by field, write back only what needs fixing, and route the exceptions to a person with the reason attached. The agents judge Odoo's draft instead of replacing it, and agreement between two independent readers is the gate for anything that posts without a person.
- 96–99%
- right on vendor, invoice number, bill date and total
- 456
- bills graded field by field against what staff actually posted
- 176
- card expenses created in Odoo with receipts attached
Three agents, three outcomes
Different industries, the same pattern: map a high-leverage workflow, govern it, ship it, then let the numbers compound.
Invoice reconciliation, fintech
An agent matches invoices to POs and bank feeds across three systems, clearing 94% straight-through and routing only true exceptions to AP staff.
Tier-1 support triage, SaaS
A support agent reads, classifies, and resolves or routes inbound tickets — cutting first-response time from hours to under a minute on 60% of volume.
Contract review, legal ops
A retrieval-grounded agent extracts clauses, flags deviations from playbook, and drafts redlines — turning a two-day review into a same-hour first pass.
Order-to-cash, distribution
An agent orchestrates quote, credit check, and order entry across an ERP and CRM, with approval gates on anything above a risk threshold.
Compliance monitoring, banking
An agent reviews transactions against policy, files structured findings with full lineage, and hands edge cases to a compliance officer.
Underwriting assist, insurance
A decision-support agent assembles the file, scores risk, and recommends — leaving the bind/decline call to a licensed underwriter.
How the fintech reconciliation agent runs
The client's AP team was matching thousands of invoices a month by hand across an ERP, a bank feed, and a procurement system. The agent ingests each invoice, retrieves the matching PO and payment record, and reconciles them through a governed action layer that writes back to the ERP.
Anything ambiguous — a price mismatch, a missing PO, a duplicate — is held and escalated with the full reasoning trail attached, so a human resolves it in seconds instead of investigating from scratch. Ninety-four percent now clears without a person touching it.
- 94% straight-through reconciliation
- Write-back through an approval-gated action layer
- Full decision lineage on every escalation
From baseline to compounding result
Every case study below followed the same measured arc.
- Step 01
Baseline
We instrument the existing workflow — cost, cycle time, error rate — so the result is a real delta, not a guess.
- Step 02
Govern & build
We design the action layer, guardrails, and approval gates, then ship the agent into the client's environment.
- Step 03
Tune
We watch live behavior, drive the exception rate down, and widen the agent's autonomy as trust earns it.
- Step 04
Compound
We expand into adjacent workflows, so the fleet — and the ROI — grows past the first win.
A pilot vs. a deployed agent
Why most flashy AI demos never show up in a case study like these.
| Criterion | A typical AI pilot | An Automatic.co deployment |
|---|---|---|
| Environment | Vendor sandbox | Your VPC, on-prem, or air-gapped |
| Outcome | A convincing demo | Tracked delta vs. baseline |
| Integration | Mocked data | Live ERP, CRM, and databases |
| Oversight | None | Approval gates, thresholds, lineage |
| Longevity | Shelved after the POC | Operated and expanded over time |
Explore further
Dig into the numbers, the approach, or start scoping your own workflow.
Frequently asked questions
Are these real deployments?
Yes. Each case study is a production agent running in a client's environment. Some names are anonymized under NDA, but the workflows, architectures, and outcome metrics are real and verifiable on a reference call.
How do you measure outcomes?
We instrument cost-per-task, cycle time, exception rate, and quality against a pre-engagement baseline. Every number you see below is a delta we tracked over a defined window — not a projection.
Can I talk to a reference client?
For serious engagements, yes. After scoping, we'll connect you with a client in a comparable industry who ran a similar workflow, so you can hear how the build and operations actually went.
How long until a case study like these?
Most teams see a measurable first result inside a single workflow within 6 to 10 weeks of kickoff, then compound from there as the fleet expands. Timelines depend on integration depth and approval requirements.
Picture your workflow on this page
Bring us the process that's eating your team's hours. We'll baseline it, scope the agent, and tell you honestly what it would move.