Outcomes you can put on a board slide
We don't sell pilots that never graduate. We ship agents into production, measure them against a real baseline, and report the deltas that move cost, speed, and quality.
- Baseline-measured deltas
- Production, not demos
- Honest attribution
- Monthly outcome reporting
Four kinds of results we deliver
Every engagement targets a specific, measurable lever — not generic productivity.
Speed
Multi-day back-office processes collapse to minutes when an agent owns the handoffs instead of a queue of people.
Capacity
Teams clear three to six times the volume per head without adding headcount or burning out on rote work.
Accuracy
Structured checks and decision lineage drop exception and rework rates below what tired humans sustain at scale.
Cost
Recovered hours, fewer errors, and lower vendor spend show up as a defensible cost-per-task line, not a hunch.
From baseline to attributed delta
The same measurement discipline runs on every engagement, so the number you see is the number that held.
Baseline
Before code, we capture current cycle time, error rate, volume mix, and fully-loaded cost per task.
Instrument
The agent logs every action, decision, and exception so outcomes are computed from data, not anecdotes.
Compare
We measure like-for-like over the same workload and isolate the agent's contribution from other changes.
Report
A monthly outcome review shows the deltas, what drove them, and where the next gain is hiding.
We report the misses too
Most AI case studies are marketing. Ours are accounting. If a workflow underperformed its baseline, you'll see it in the same monthly review as the wins — alongside what we're changing.
About one in five workflows we assess isn't worth automating yet. We flag those before you spend a dollar building them, because a result that doesn't survive scrutiny isn't a result.
- Pre-build feasibility and ROI scoring
- Misses reported beside the wins
- Cost-per-task tracked end to end
A pilot vs. a result
Why most AI experiments never make it onto a P&L — and how ours do.
| A typical AI pilot | An Automatic.co result | |
|---|---|---|
| Lives in | A demo environment | Your production systems |
| Measured by | Looked impressive | Delta against a baseline |
| Owns the work | Suggests, human redoes | Completes it, with approvals |
| Reported as | A slide, once | A monthly outcome review |
| When it fails | Quietly shelved | Flagged, fixed, or killed |
Frequently asked questions
How do you prove a result was the agent and not something else?
We measure a baseline before anything ships — current cycle time, error rate, and cost per task. Then we compare like-for-like over the same volume mix. If a number moved for an unrelated reason, we say so. No vanity math.
How fast do results show up?
A scoped first workflow usually reaches production in four to eight weeks, and the first clean month of data follows. Early wins are typically cycle time and throughput; cost and quality gains compound as the agent handles more of the volume.
What if the numbers don't move?
Then we tell you and fix the design or kill the workflow. Roughly one in five workflows we assess isn't worth automating yet — we'd rather flag that early than ship a costly agent that looks busy and changes nothing.
Can we see references in our industry?
Yes. On a call we'll walk through anonymized case studies and, where clients allow, connect you with a reference in a comparable regulated or operations-heavy environment.
Bring a number you want to move.
Tell us the cost, cycle time, or error rate that's bothering you. We'll show you what an agent can realistically do to it — and how we'd prove it.