Case Studies: Three AI Systems We Run Inside Real B2B Operators
Inside each business, an AI install is sitting where a payroll line used to be. No vendor demos, no consulting decks. Real operations.
Negodiuk AI · Brooklyn, NY · Updated · 12 min read
The Negodiuk AI practice operates 5+ businesses across e-commerce, B2B distribution, retail, education, and AI consulting. Each one is an in-house lab. Every system sold to an outside client runs in one of those labs first, in production, with the operator wearing the consequences when something breaks. The three case studies below are the most relevant cuts of that work for a B2B operator deciding whether the fit is real.
The names of the underlying businesses are not on this page. The reasons are in the closing section. What matters for an outside reader is the shape of the install, the work it replaces, and the kind of operation it fits.
Want the same shape of system inside your operation? Start with the 5-day Leak Audit ($2,500 flat, no fit no fee).
Voice operator stack 24/7 in 15+ languages, replacing a multilingual SDR team that was never going to get hired
Setup
A NYC paint distribution operator selling B2B into contractors, property managers, and trade buyers across the five boroughs and inner New Jersey. The customer base speaks Spanish, Russian, Ukrainian, Polish, Mandarin, Korean, and Arabic in addition to English. Orders come in by phone, by email, by WhatsApp, and sometimes by a contractor walking into the showroom at 6 AM before a job site opens. The operation runs lean. A full-time multilingual SDR team at NYC salaries was never in the budget for year one.
What the AI does
24/7 inbound voice operator stack answering trade calls in 15+ languages, switching mid-call when the contractor flips from English to Spanish or Russian without re-prompting.
Outbound voice and email pipeline running the SDR workload across cold and warm trade contacts, with reply scoring and follow-up cadences baked in.
SMS and LinkedIn outbound layer reaching property managers and fabricators on the channels they actually read, sequenced with the voice and email pipeline.
Inbound triage scoring every lead by margin potential and routing high-value calls to the senior salesperson within minutes.
Daily morning brief at 7 AM with competitor pricing changes, supplier signals, at-risk accounts, and the day's top three actions for the owner.
What changed in the operation
Trade calls in the operator's non-English customer segments now close in the caller's first language, on first contact, around the clock. The segments an English-only sales team would have lost are now part of the book.
Three small operators inside the same NYC trade-supply network onboarded onto the shared voice operator platform, turning a single install into a multi-tenant operator playbook.
The build pattern is part of the public record. The Forbes feature in April 2026 by Gene Marks documents the Fractional AI Officer model the install runs under, including the multilingual voice operator stack as one of the canonical examples.
Inbound voice agent closing trade calls end to end, qualifying buyers, quoting jobs, and routing fulfillment with no SDR floor
Setup
A B2B stone distribution operator importing quarried slab and serving residential and commercial accounts across the Midwest and the East Coast. Buyers are construction companies, kitchen and bath fabricators, and design-build firms. Calls come in during normal hours on one coast and after hours on the other, every day, with no realistic way to staff a 24/7 SDR floor at the unit economics of the operation. ESL contractor accents are the norm on the inbound line.
What the AI does
24/7 inbound voice agent picking up trade calls and closing them end to end: takes the spec, prices the slab, books the freight quote, sends the written confirmation, and updates the fulfillment queue.
Multilingual call handling that holds steady across ESL contractor accents and language switches mid-conversation, with no measurable drop in the qualification quality compared to a human SDR.
Automatic escalation to a human only on the genuinely complex jobs (multi-slab matching, custom finish, unusual freight routing), keeping the operator's attention on the calls that actually need it.
CRM, ERP, and freight system integration so the voice agent writes directly into the operator's existing stack without a separate workflow tool to maintain.
Same daily brief layer as the paint lab, tuned to slab inventory, lead times, and contractor reorder patterns specific to stone distribution.
What changed in the operation
The operator has full 24/7 coverage on inbound trade calls with zero outbound SDR headcount, and zero human picking up the phone after hours.
ESL contractor accents and language switches are handled natively, removing the language barrier that historically lost calls before a quote was sent.
The voice agent has been in production for months as the only voice on the inbound line, which is the longest stress test the install pattern has across the practice and the reference point for the same Sprint inside any distribution operation.
Operator-side AI stack replacing the full vendor SaaS layer across listings, reviews, ads, and returns
Setup
A photo-mosaic consumer brand the practice operates on Amazon FBA. The product turns a customer-uploaded photograph into a printed mosaic-style wall piece, which means every order carries a custom-art workflow on top of the standard Amazon listing economics. The operation runs the full Amazon FBA stack: keyword research, listing copy, A+ content, sponsored ads, review responses, returns, and the customer-service queue that comes with custom-art e-commerce.
What the AI does
Listing optimization layer rewriting titles, bullets, and A+ content from inside the operator account, tied to live search-term and conversion data instead of generic SEO heuristics.
Review monitoring across every variant, flagging negative-review patterns within hours of posting and drafting the operator response in the customer's tone.
Ad-bid optimization on sponsored campaigns, adjusting bids and keyword targeting on the data the operator account already has, replacing the third-party PPC tool the operation used to pay for.
Returns triage that reads the buyer message, classifies the return reason, drafts the response, and queues the rare cases that need a human.
Daily ops brief covering listing health, ad spend pacing, review sentiment shift, and the next-best action across the catalog, written into the operator's inbox before 7 AM.
What changed in the operation
The operator-side AI stack replaces the full vendor SaaS layer the brand used to pay for across listings, ads, reviews, and returns, removing both the license cost and the workflow drag of switching between four separate dashboards.
Custom-art e-commerce, which usually carries a heavier customer-service load than a standard Amazon brand, runs on the same headcount as a fully off-the-shelf catalog because the AI absorbs the triage work.
The pattern is portable. Any Amazon brand that is paying for a separate listing tool, a separate PPC tool, a separate review tool, and a separate returns workflow can collapse the same stack into an operator-side AI layer under the practice's Install tier.
Operator privacy, competitor obscurity, source data over selling points
The operators inside these case studies are real businesses serving real customers. The trade buyers who call the voice operator did not opt in to becoming a marketing reference. The install patterns are competitive moats inside the same NYC zip codes and the same Amazon categories, and naming the underlying brands invites copycats who would rather clone the system than build their own. The case studies on this page are also not selling points in the usual sense. They are the source data for the Leak Audit methodology, and the methodology travels regardless of which brand name sits on the storefront. The pattern is the product, the pattern works, and the pattern is what an outside operator buys when they hire the practice.
What to do next
If three of the five things below describe the operation, the Audit will surface at least three leaks worth fixing inside the first week.
Annual revenue between $5M and $50M, bootstrapped or close to it.
A customer or supplier base that speaks more than one language, or an Amazon catalog that carries customer-service load above a standard catalog.
Orders that come in by phone, by email, by WhatsApp, or by direct buyer message, not only through a clean e-commerce funnel.
Recurring frustration about missed after-hours calls, slow quote turnaround, slow review response, or a stack of vendor SaaS tools nobody is fully using.
No in-house AI engineer, and no realistic budget for a $300,000 full-time Chief AI Officer hire.