DrevixTech is a new studio — we're not going to fake a portfolio. What follows is the depth and shape of the work we build: real scenarios drawn from the businesses we've consulted with, scoped exactly the way we'd ship them. The first cohort of named case studies will live here once they're past sixty days in production.
A regional services group with a steady flow of inbound calls outside business hours. Generic voicemail catches them. Most never call back. Every missed call is pipeline a competitor picks up by morning.
Calls between 6pm and 8am hit voicemail. Most callers never ring back. The booking team's first hour each morning is a callback queue, and competitors quietly take the rest.
A voice receptionist trained on your services, booking flow, and qualification criteria. Phased rollout — start with overflow, expand as it proves itself. Live tuning during week one alongside your ops lead.
The phone gets answered, around the clock, in your tone. The morning callback queue goes to zero. The booking team wakes up to qualified appointments instead of voicemails.
"Voice systems pay for themselves the first time you stop missing the call you would have missed."
How we frame this build in discovery
A boutique professional services firm where each partner spends three to five hours daily on inbox triage before client work begins. Each partner has a distinct voice. Outsourcing the inbox isn't an option.
The first hour of the day is unbillable triage. Paralegals can't replicate each partner's voice. Mornings start with sorting and end in slipped deadlines.
Per-partner inbox automation, trained on years of past replies. Auto-pulled matter context from existing systems. A clean priority queue and pre-drafted replies waiting before each partner opens the laptop.
The first hour moves from triage to actual practice. Drafts arrive in the partner's voice with full thread context. Nothing slips, because follow-ups auto-schedule when something's waiting on a reply.
"The inbox shouldn't be where the first hour of the day goes. We build systems that make sure it isn't."
How we frame this build in discovery
A media brand publishing long-form video where errors only get caught after publish — by audience comments. Pronunciation issues, unsourced statistics, audio mix problems. Each one chips away at hard-won credibility.
Editorial review is a bottleneck. One person on the team can catch every category of issue. Schedule pressure means some assets ship without final pass. Audience catches the rest.
A QA system trained on your style guide, vocabulary, and historical content. Reviews every export against pronunciation, factual claims, pacing, and audio levels. Outputs an editor-ready timestamped report.
A 45-minute video reviewed in under three. Your head editor reviews the report, not the raw asset — then makes the call on what matters. The library gets the same standard, every time.
"A QA system isn't replacing your editor. It's handing them the catches before the audience finds them."
How we frame this build in discovery
A finance team running month-end close across a dozen platforms — manually reconciled in spreadsheets. Long cycles, frequent errors, and a controller who hasn't taken a real weekend in months.
Each platform exports in its own format. Reconciliation lives in a fragile master spreadsheet that breaks any time a column moves. Errors only surface after the books are "closed" — meaning they reopen.
An automated pipeline that pulls, normalizes, and reconciles across every platform with a complete audit trail. Approval gates for sensitive postings. Failure alerts when any step doesn't run.
The close cycle compresses. Reconciliation becomes a check, not a build. The controller closes from her laptop on a Friday — and audit prep drops from a week to an afternoon.
"The right pipeline isn't the one that runs faster. It's the one you can trust when the books need to close."
How we frame this build in discovery