Measured results
Production systems, described without client names. Numbers are rounded and come from the systems themselves. The walkthrough videos use fictional companies and fictional data so we can show the mechanics without showing anyone's customers.
Case studies
Problem, what we built, result. Each links to the service it belongs to.
Hundreds of seats, zero hand-built phones
Problem. A UCaaS provider's onboarding team got seat lists as spreadsheets and set up every user, phone, number and emergency address by hand. Big sites took days and small typos broke E911.
What we built. A self-service intake form (or Excel template) that checks every order first and sorts it into "customer must fix", "needs review" and "clean". Clean orders build themselves through the phone platform's API in stages (locations, seats, devices, numbers, E911, phone keys), hold ported numbers until the port date, and post progress to the helpdesk ticket.
A billing gap nobody could see
Problem. What was provisioned and what was billed lived in two systems with no shared view, across parent and child accounts.
What we built. An audit that walks both account trees through their APIs, groups seat types, learns from corrections, and emails the customer an approve/dispute link for each mismatch.
Installs that stopped stalling
Problem. Go-live dates slipped because dispatch dates, tracking numbers and porting conflicts surfaced in the last days.
What we built. Readiness rules on the project board that send a nudge a set number of days before go-live, escalate if ignored, and give every rep a daily work plan.
Tickets that stopped going stale
Problem. A support team tracked aging tickets by hand and the same ones kept slipping.
What we built. Scheduled KPI reports (aging, per-agent workload, week-over-week) and nudge emails to the owner with a one-click "ignore", plus a weekly "wins" email for the top closers.
Ticket QA without sending data to the cloud
Problem. Leadership wanted every support reply reviewed, but nobody had time and customer data couldn't leave the network.
What we built. A two-stage AI pipeline on the company's own hardware: a small model sorts, a larger one scores each reply on 7 quality dimensions and writes coaching notes, feeding a nightly churn score.
A firehose, and only the right alert
Problem. On a real-time data platform we built and run: millions of raw events a day, and people only care about a handful.
What we built. A pipeline that decodes and de-duplicates about 1.9 million events a day, scores each against rules, and alerts only the people who opted in, with their own filters, in seconds.
Engagement walkthroughs
One-minute case files that build an engagement step by step: intake, validation, build, exceptions, outcome. Music only, no narration.
Walkthrough: 12 clinics, 340 seats, one spreadsheet
A fictional veterinary group goes from a 412-row intake spreadsheet to 340 live seats. Demo data only.
Provisioning automationWalkthrough: a nine-stage nudge system
How stale tickets get a named owner, a reminder and an escalation path at a fictional software company. Demo data only.
Helpdesk reporting and nudgesWhy no client names
The systems described here were built for a UCaaS provider and for a data platform we run ourselves. We do not publish customer names, account data or screenshots of real systems. Every number is one the system reported, rounded, and every screen on this site is built from fictional companies.
On a call we can go into as much technical depth as you like.
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