AI automation results: what it looks like in practice

Three walkthroughs of the kind of work we do — the problem, the build, and the numbers that come out the other side.

Example project · Finance

Invoice inbox → books, hands-free

The problem: a 12-person accounting firm was re-typing every supplier invoice from email into QuickBooks — about 25 hours of manual entry a month, with errors slipping through.

The build: AI reads each incoming invoice (including PDFs), extracts vendor, amount, and due date, creates the QuickBooks entry, and pings the approver on Slack.

~22 hrs/month backerrors ↓ 90%live in 2 weeks
Example project · Retail

Support that answers overnight

The problem: an online retailer's two-person support team faced a growing ticket queue and slow replies outside business hours.

The build: an AI agent trained on their help docs and order system answers common questions instantly in Zendesk and hands complex cases to a human with full context.

70% auto-resolvedreply time <1 minCSAT up
Example project · Healthcare

Patient intake, summarized before the visit

The problem: a dental clinic's front desk spent 10+ minutes per patient transcribing intake forms and chasing missing details.

The build: web intake forms flow to AI, which flags gaps, drafts the clinical summary, and files it in the practice system before the patient arrives — with reminder texts that cut no-shows.

~10 min/patient savedno-shows ↓ 40%zero re-typing

What would yours look like?

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