Use cases

What we've built

Every published case follows the same framework: problem, stack, build, hard numbers.

AI Quote & Inventory Matching Assistant

A B2B distributor carrying thousands of SKUs was drowning in inbound quote requests. Customers emailed in technical, jargon-heavy part descriptions (thread types, materials, dimensions) that took skilled staff real time to decode, cross-reference against inventory, and respond to. With only a handful of customer service reps handling the volume, routine requests ate hours that should have gone to relationship-building and complex orders, and the occasional large multi-line request could tie up the whole team for days.

Logistics & Freight / Transportation

$510k saved / year

AI Shipment Intake & Human-Verified Data Entry

A freight logistics operation was processing roughly 1,000 shipment requests a month, arriving from vendors in every format imaginable: emails, inconsistent PDFs, even handwritten forms. Getting that information into the Transportation Management System meant staff manually reading each document, decoding the details, and typing them in by hand. A team of six spent roughly half their day on this alone, and multi-stop loads made it worse: pairing related shipments relied on staff memory and manual cheat sheets, often holding up carrier booking for a full day while someone tracked down the matching load.

B2B Sales Development

$12,000 saved / year

CEO Contact Sourcing & Outreach Drafting

Every list of companies coming in the door, whether it was a tradeshow scan or a spreadsheet export from a conference, needed the same manual slog before a rep could send a single email. Someone had to figure out who actually ran the company, cross-check that name against whatever the CRM said (which was wrong often enough that nobody trusted it blind), pull together a few lines about what the company did, and then write an email that didn't read like a template. With 50 people doing this by hand for every new batch, most of the week went into research and data entry instead of selling.

Recruitment & Staffing (B2B business development)

$720k in new client value generated per year

AI-Personalized Outbound Sequencing for New Business Development

A boutique staffing and recruitment firm needed a steady stream of new client business, but its only lever was manual cold outreach — one person writing, sending, and following up on emails by hand. That caps growth at whatever a single person can personalize and track in a day, and generic, templated outreach doesn't get replies from hiring managers who get pitched constantly. Worse, there was no real signal behind who to contact: without a way to tell who's actually hiring right now, most outreach lands on companies with no open need at all. Every reply also had to be caught manually across whichever inbox it landed in, so leads went cold simply because nobody saw the response in time.

Sales & Business Development

+3,000 contacts / yr

Outbound Sales Teams Stuck Between Three Tools: One FullEnrich and HubSpot Prospecting Portal

Most outbound teams do not have a prospecting problem. They have a stitching problem. The data provider lives in one tab, the verification tool in another, and HubSpot in a third, and a rep spends their morning moving records between them by hand. Search for a target account. Find the right person. Export. Paste into a verification tool. Wait. Copy the verified email. Check whether that contact already exists in the CRM. Create the record. Repeat. The frustrating part is that the underlying data is usually fine. Teams running FullEnrich already have deep search filters and reliable email and phone verification sitting behind an API. Teams running HubSpot already have the destination. What's missing is everything between the two, and that gap gets filled by a person with a clipboard mentality and forty browser tabs. Where it breaks down Verification is a separate stop. Search returns a name and a company, not a deliverable email, so every promising contact needs a second round trip before it's worth anything. Deduplication is manual and therefore unreliable. A rep is supposed to check HubSpot before adding a contact. Under volume they stop checking, and the CRM slowly fills with the same person entered three ways. Nobody knows what enrichment costs until it's spent. Credits get burned on contacts a rep would never have called, because there's no way to see the balance and select deliberately before firing a batch. Batch prospecting doesn't really exist. Working a list of forty target accounts means forty separate searches, run one at a time, consolidated by hand afterward. The obvious fix is to buy a platform that does all three. That works until the team needs a filter the platform doesn't expose, or a field mapped somewhere it doesn't map to, and then everyone is back in spreadsheets. The tooling isn't the constraint. The gap between the tools is.

Customer Service / Call Center Quality Assurance

100% call coverage

Call Center QA Teams Spot-Checking Calls Instead of Reviewing Every One

Post-sale customer service calls get recorded, but almost none of them get properly reviewed. A QA team can only score a thin slice of the calls coming in each day, because listening to a call and grading it against a quality checklist by hand takes real time. So the same few percent gets sampled, week after week, while everything else agents say to customers goes unheard. Where it breaks down Agents consistently missing key steps, skipping a required disclosure, forgetting to confirm identity, rushing the close, can go unflagged for weeks unless a customer complains first Two reviewers scoring the same call rarely land on the same grade, since the checklist gets interpreted a little differently person to person Every new question added to the rubric means training reviewers on it, not just updating a spreadsheet The evidence behind a score usually lives in a reviewer's head or a rough note, not in a record tied to the actual moment in the call What this costs Coaching becomes reactive. Supervisors act on the handful of calls somebody happened to catch, not the pattern across the floor. And hiring more reviewers to close the sampling gap doesn't scale. Doubling the QA team doubles the sample, not the coverage.

Cross-Industry — After-Hours & Overflow Call Handling

+300 calls captured per month

Businesses Missing Calls After Hours: A VAPI Receptionist That Catches Every One

Every business with a phone line loses calls the moment nobody's free to answer. This system runs on VAPI, the voice AI platform handling the actual phone conversation, picking up the instant a human can't, whether that's a busy front desk mid-shift or after the doors close for the night. Otherwise, whoever's calling either hangs up, leaves a voicemail nobody checks for a day, or dials the next name on their list instead. Where it breaks down A voicemail box is a dead end for anyone in a hurry: a customer with an urgent problem, a lead ready to buy, a complaint getting worse by the hour Whoever eventually checks the voicemail has to replay it, pull out the important details by hand, and guess how urgent it actually was A genuine emergency sits in the same queue as a routine hours-of-operation question until someone happens to listen Front desk and support staff can't be in two places, so overflow during a rush hour behaves exactly like overflow after close: silence What this costs Every missed call is a coin flip between a customer who tries again tomorrow and one who's already calling a competitor. And unlike a queued email, a voicemail nobody's tracking doesn't show up in any report, so leadership has no real number for what this is costing until long after the fact.

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