AI automation for the manual work eating your week
We automate the processes rules engines never could: unstructured documents, messy exceptions, and the judgment calls buried in your team's inbox.
How it works
Your team already automated the easy parts. What's left is the work that broke every RPA bot you bought: a supplier who sends orders as a photo of a fax, a contract clause that changes the payment terms, an exception queue that only two people understand. AI handles that layer because it reads context instead of matching fixed fields. We map the process first, automate the 80 percent that's genuinely repeatable, and route the rest to a person with the analysis already done.
Process mapping before a line of code
We sit with the people doing the work and time the steps, so the automation targets the hours that actually exist rather than the ones on the org chart.
- Step-level time and volume baseline per workflow
- Exception rates measured, not estimated
- Automation candidates ranked by hours saved per week
- You get the map whether or not you build with us
Handles the messy middle
Scanned PDFs, email threads, handwritten notes, and inconsistent vendor formats all become structured, validated data.
- Document extraction tuned on your own historical files
- Validation rules that catch bad data before it hits the ERP
- Exception routing with the reasoning attached
- Accuracy tracked per field, not just per document
Measured in hours and dollars
Every workflow ships with a dashboard showing volume processed, touch rate, error rate, and time saved against the pre-automation baseline.
- Before-and-after baseline captured during discovery
- Live touch rate and exception trending
- Cost per transaction tracked against manual cost
- Quarterly tuning to push straight-through rate higher
Where teams use it
Built for real revenue work.
Order entry from email and PDF
Customer POs arrive in twelve different formats across email, EDI, and scanned attachments. AI extraction reads each one, matches SKUs and pricing against the ERP, and creates the order, flagging only genuine mismatches for a human.
Invoice matching and coding
Invoices get read, coded to the right GL account, matched against POs and receipts, and pushed into the finance system. Straight-through rates of 80 percent and up are common once the model has learned your vendor patterns.
Onboarding and compliance packets
New customer, vendor, or employee onboarding usually means chasing a dozen documents and checking each against policy. Automation assembles the packet, verifies what's present, and escalates only what's missing or non-compliant.
“Our AP team was three people opening PDFs eight hours a day. Now two of them work exceptions and analysis and the third moved to FP&A. We didn't cut anyone, we just stopped burning them out on data entry.”
FAQ
Questions, answered.
How do I automate my business with AI?
Start with one process you can measure, not a platform-wide program. Pick something with high volume, a clear definition of correct, and an obvious cost when it's done manually, then baseline how long it takes today. Automate that single workflow end to end, prove the hours saved, and use that result to fund the next one. Teams that try to automate eight processes at once usually finish none of them.
How can AI be used in business operations?
The highest-return uses in operations are reading documents, classifying and routing incoming work, checking data against policy, drafting routine communications, and forecasting demand or capacity. What ties them together is that each one replaces reading-and-deciding work rather than clicking work. That's the distinction between AI automation and the RPA generation that came before it.
Is this the same as RPA, Zapier, or Make?
They solve different halves of the problem. Zapier and Make move data between apps when the trigger and fields are predictable, and RPA clicks through screens on a fixed path. Both break when the input varies. AI automation handles the interpretation layer, the part where something has to read an unfamiliar document or decide which of six paths applies, and we frequently build it alongside tools you already own.
How long before we see the time savings?
A single scoped workflow is typically live in four weeks, and the savings show up in the first full month because the baseline was measured during discovery. Straight-through rates usually start around 60 percent and climb into the 80s over the following quarter as the model learns from the exceptions your team corrects. All of it runs on a fixed project cost, so the payback math doesn't move on you mid-build.
Keep exploring
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