Stakh Vozniak writes and speaks about building Cargofy, a company using AI agents to run logistics work such as freight tenders, bids, and shipment tracking. Vozniak also discusses how to price AI workers for logistics businesses.
Stakh Vozniak is most useful for practical AI pricing and operations in logistics.
Best for
Logistics technology companies selling to shippers or forwarders, for speaking or advisory.
Not for
Consumer fashion, home, or lifestyle brands or creative studios seeking paid ambassadors.
Key takeaways
Price AI workers in operational units such as tenders closed, not tokens.
Reject per-seat pricing because it does not match the work.
Estimate costs from actual monthly volumes with a floor and ceiling.
Check first: Confirm follower demographics, job function, seniority, company type, and contact route.
Main ideas
Price AI workers in the customer's operational units, never in tokens · Put automation behind the scenes and keep the visible brand slow and human · Build credibility with operating detail and shipped product, not a press release
Stakh Vozniak is known for building Cargofy, which uses AI agents to handle logistics work such as freight tenders, bids, and shipment tracking.
They also explain how logistics companies might budget for these AI workers, using real workflow volumes and charges tied to completed work rather than seats or tokens.
Their posts connect product and business decisions with examples from company growth, industry events, and other businesses using AI.
Vozniak makes AI operations concrete, translating them into logistics workflows and buyer decisions.
In a post on pricing, they reject per-seat and per-token models, propose a fixed platform fee plus charges for work such as tenders closed or documents processed, and outline how to estimate costs from actual monthly volumes.
Other posts offer a broader perspective: Cargofy’s scaling and live demonstrations show how the product is presented to the industry, while the Londi example argues that automation can support a human-centered customer experience.
Their post about collecting art is a personal exception to the mainly operational and commercial themes, showing how contemporary Ukrainian art became part of the company’s workplace and unexpectedly opened business conversations.
Readers interested in practical AI adoption in logistics can follow their examples linking product design, pricing, and company-building.
Stakh Vozniak writes about running logistics operations on AI agents and how those agents should be sold and budgeted.
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The recurring argument is that AI work should be priced and judged in the customer's operational units—tenders closed, carriers onboarded, documents processed, check calls made—rather than in seats, tokens or flat monthly fees.
A second theme: automation belongs behind the scenes, while the parts of a business customers actually touch should stay slow and human.
They also present credibility as something built through operating detail and live demonstrations rather than press releases, while collecting contemporary Ukrainian art—a personal priority that has quietly become a channel for business relationships.
Price AI workers in the customer's operational units, never in tokens
Stakh Vozniak argues that budgeting for an AI digital worker is nothing like buying SaaS, yet most logistics executives still budget for AI the SaaS way.
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Per-seat pricing does not fit the work; per-token pricing fails because no head of logistics can forecast what a tender costs in tokens; and a flat monthly fee ignores seasonality.
The model they describe and use at Cargofy has two layers: a fixed platform fee plus a variable charge expressed in logistics terms on the customer's statement—tenders closed, carriers onboarded, documents processed, calls handled.
They argue that this pricing model also disciplines the vendor: if the agent makes five calls where one would do, the customer still pays only for the outcome, so the vendor had better get it right the first time.
The post credits the framing to a Stanford pricing lecture they attended and quotes a Stanford professor: “price the value, meter the cost.”
It separately cites OpenAI's CFO for the phrase “cost per unit of intelligence.”
The post lays out a four-step estimate: pull last month's actual volume from the transport management system and start with one workflow; have the vendor define a billable action and clarify whether a retry counts as one action or two, then assess quality—a cheap action is worthless if the agent needs forty to close a tender; set a floor and ceiling by running the slowest and busiest month through the per-tender cost; and measure ROI against more than manual savings, since the agent can ask more carriers than a person ever could and may win a better rate on each load.
The post describes weighing the usual per-seat, per-token and flat-fee options before settling on a two-layer outcome model, presenting it as a position reached through experience rather than a starting assumption.
Put automation behind the scenes and keep the visible brand slow and human
Stakh Vozniak's broader thesis is that running a business on AI does not require it to look or feel machine-made.
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The front end can be deliberately unhurried and physical while agents run the operational back end.
They draw a direct parallel to Cargofy: watching a logistics company run on agents prompted the question of why a fashion brand could not.
When agents absorb the operational load, a small team can run a global business and still have room to build something genuinely human.
Stakh Vozniak describes Londi, a lingerie and loungewear brand run by their spouse, opening its first physical space in Kyiv as a calming, forest-inspired environment rather than a conventional store: a handmade ceramic door handle shaped like a flower, rugs woven for the space, contemporary artists in every corner and a ceremonial matcha brand.
The post contrasts that slow front end with the back end: product creatives mixing real models and AI backgrounds, AI-powered customer support, and an ad system that picks which creatives convert and shifts budget toward them.
They congratulate Yuliia Vozniak and Nastya Kharytonova, the people behind the brand.
The post uses a fashion and retail case to argue that the logistics experience generalises, presenting the Cargofy model as one example of a wider AI-in-operations thesis rather than a freight-specific tool.
Build credibility with operating detail and shipped product, not a press release
When Stakh Vozniak describes Cargofy's stage, they focus on operating facts: a closed round, headcount growth, named new markets, senior hires leading each and product releases shipped during the same week as a hackathon.
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Their point is that a company's most telling details often do not fit in a press release, and public speaking should convey those details rather than offer a polished summary.
The post says Cargofy closed a Series A a couple of months earlier and has grown the team by 60%, adding markets in DACH, the Caspian region, France and Spain, with new senior people leading each.
It describes rebuilding logistics operations for shippers with AI digital workers that run tenders, collect bids and track shipments.
Stakh Vozniak also notes speaking at Future Tech Meetup: InvestDay about the journey from an idea at an Iowa accelerator to a Series A round.
The post mentions writing it “in between a week-long hackathon with our engineering and product team — shipping product releases at the same time.”
Live demos for the crowd, small human gatherings for relationships
As Stakh Vozniak describes it, Cargofy's public presence runs on two tracks: large industry events where the AI agents are demonstrated, and deliberately small gatherings—a private dinner and a breakfast—reserved for people.
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They make the distinction plainly at the end of the post: the digital workers come to every demo, but the dinner and breakfast are for humans. The venues are freight and supply-chain events rather than general technology conferences, keeping the demonstrations close to the buyers who would use the agents.
The autumn schedule spans Hannover for IAA Transportation, with a private dinner on 17 September; Amsterdam for Supply Chain Europe, where the head of sales joins 350 supply chain leaders from European retail and manufacturing and hosts a breakfast; Prague for Innovation Week; Berlin for BVL Supply Chain CX at stand C02, with live demos of agents running tenders, bids and tracking; and Chattanooga for the F3 Future of Freight Festival. Meeting requests go to a named colleague, Oleksandra Yanchenko.
Collecting contemporary Ukrainian art became a business channel as well as a personal priority
Stakh Vozniak describes collecting as a self-imposed reward for closing milestones and says it remains one of their strongest motivations.
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They did not expect the collection to become an unconventional way to grow the business: it brought in investors and entrepreneurs who also collect, and a different kind of conversation from the usual commercial one. Moving a large part of the collection into the new Cargofy hub gives the team something to draw on, with the possibility of further openings to come.
An investor Stakh Vozniak was already talking to about Cargofy bought a painting after seeing it in a Zoom background and asking about it. Art on the office wall is also a deliberate choice: since 2022, a large part of the collection has been contemporary Ukrainian work by artists living through the war, which they expect people to return to in years to come.
The collection's focus has shifted: early purchases were mostly international artists, but since 2022 a large share has been contemporary Ukrainian art.
This directly addresses how a logistics company should price and budget for an AI worker, rather than treat it like ordinary software.
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Stakh Vozniak argues that pricing AI digital workers differs from pricing SaaS.
They reject per-seat pricing because it does not match the work, and per-token pricing because logistics leaders cannot forecast a tender's token cost.
Instead, they describe a two-layer model: a fixed platform fee plus a variable charge in logistics terms, such as tenders closed, carriers onboarded, documents processed, and calls handled.
They offer a four-step estimate: pull last month's real volume from the TMS; ask the vendor to define an action and a retry; budget a range using slow and busy months; and measure ROI beyond manual savings, since an agent can ask more carriers and make more check calls than a person.
For brands planning a campaign
For a speaking or advisory engagement on AI pricing models, this post offers a concrete framework: outcome-based pricing, clear action definitions, and volume-based budgeting—not generic AI cost claims.
This explains what Cargofy is doing now and how Stakh Vozniak presents the company's stage, markets, and product in public.
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Vozniak says Cargofy closed a Series A a couple of months earlier and grew the team by 60%, adding markets in DACH, the Caspian region, France, and Spain, with new senior leaders for each.
They describe Cargofy as rebuilding logistics operations for shippers with AI digital workers that run tenders, collect bids, and track shipments.
The post also mentions a Future Tech Meetup: InvestDay talk about the journey from an Iowa accelerator idea to Series A, and a week-long hackathon running alongside product releases.
For brands planning a campaign
This provides context for a logistics technology company assessing fit. It shows they can speak credibly about scaling an AI-native logistics business, fundraising, and product execution, making them a potential advisor or keynote speaker beyond pricing.
This shows how Stakh Vozniak and Cargofy present AI agents to logistics buyers in person, and where a brand or event partner could meet them.
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The post lists Cargofy's autumn event schedule: Hannover for IAA Transportation, Amsterdam for Supply Chain Europe, Prague for Innovation Week, Berlin for BVL Supply Chain CX with stand C02 and live demos, and Chattanooga for the F3 Future of Freight Festival.
Vozniak names team members handling sales and meetings and says the digital workers will be at every demo.
The schedule includes private dinners and breakfasts as well as public conference appearances.
For brands planning a campaign
A logistics technology company can use this as evidence of active industry presence and practical demo experience. It also points to potential event collaborations, live demos, or a speaking slot on AI-native logistics operations.
This shows what Stakh Vozniak cares about outside product and fundraising, offering a personal-interest angle for a broader audience.
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Vozniak describes hanging art at the new Cargofy hub and collecting it as a reward for milestones.
Early collecting focused mostly on international artists, but since 2022 a large part of the collection has been contemporary Ukrainian art.
They say the collection has unexpectedly helped business relationships: investors and entrepreneurs who collect have started different conversations, and one investor bought a painting after seeing it in a Zoom background.
The collection is moving into the office to inspire the team.
For brands planning a campaign
This offers a human-interest or cultural angle for a campaign that aims to go beyond technical AI pricing. It also shows a relationship-building approach that could suit advisory or community-focused work.
This shows how Stakh Vozniak frames AI beyond logistics and why they see operational automation as compatible with human-centered brands.
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Vozniak describes their spouse's lingerie and loungewear brand, Londi, opening its first physical space as a forest-inspired, calming environment with a matcha brand, handmade ceramics, rugs, and contemporary art.
They contrast the slow, human front end with an AI-powered back end: product creatives mix real models and AI backgrounds, customer support uses AI, and ads shift budget toward converting creatives.
Vozniak says a relatively small team runs a global made-to-order brand because agents handle operational work, much as they do for freight companies through Cargofy.
For brands planning a campaign
For a logistics AI pricing brief, this is secondary but useful as a broader narrative example. It shows Vozniak can explain AI adoption to non-logistics audiences and connect operational efficiency to brand experience, potentially broadening a speaking or advisory engagement.
AI digital workers in freight and logistics operations
Stakh Vozniak focuses on replacing manual logistics work with AI agents that run tenders, collect bids, and track shipments.
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One post says Cargofy is rebuilding shipper operations with AI digital workers. Another lists live demos of agents running tenders, bids, and tracking.
Explains agents through concrete operational workflows rather than general AI claims.
For brands planning a campaign
A logistics technology company considering Stakh Vozniak for a speaking or advisory role can expect detailed discussion of operational deployment, not just high-level AI positioning.
Stakh Vozniak argues that AI worker pricing should move beyond SaaS models such as per seat, per token, or flat monthly fees.
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The proposed model has two layers: a fixed platform fee and a variable fee for work measured in logistics terms, such as tenders closed, carriers onboarded, documents processed, and calls handled—not tokens. The post credits a Stanford pricing lecture and the idea of pricing value while metering cost.
Asks vendors to define a billable action and account for quality: a cheap action is no bargain if an agent needs many retries. Suggests using last month's actual volumes from a TMS, modeling slow and busy months to set a floor and ceiling, and measuring ROI beyond manual savings, since agents can ask more carriers and make more check calls.
For brands planning a campaign
A strong fit for speaking engagements or advisory work on AI pricing models for logistics. They offer a framework a brand can present, test, and discuss with customers.
Stakh Vozniak points to lingerie and loungewear brand Londi, which is opening a physical space in Kyiv designed as a calming forest rather than a conventional store.
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The post describes handmade ceramic door handles, custom rugs, and contemporary artists, while noting that the backend uses AI for product creatives, customer support, and ad optimization. They argue that a business can run on AI without becoming polished AI slop: agents take on the operational load, leaving more room for genuinely human brand building.
Uses a fashion and retail example to make a broader argument about AI in operations.
The example comes from outside logistics, so it illustrates their wider AI thesis rather than direct freight experience.
For brands planning a campaign
A fit for broader discussions of AI adoption, retail brands, or human-plus-AI creativity. Treat the logistics parallel as a thesis, not a logistics case study.
Stakh Vozniak describes collecting art as a personal milestone reward and moving much of the collection to a new Cargofy hub.
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Since 2022, a large part of the collection has been contemporary Ukrainian art. They say the collection brought unexpected business value by connecting them with investors and entrepreneurs who collect, including one investor who bought a painting after seeing it in a Zoom background.
Uses art in personal and office settings to build relationships with investors and business peers.
This personal and cultural interest fits only some brand briefs; it is not a logistics AI topic.
For brands planning a campaign
A fit for cultural sponsorship, office art partnerships, or founder-story content—not a substitute for core logistics and AI pricing topics.
Stakh Vozniak’s documented business tie is Cargofy, which they call a company where AI agents run freight operations such as tenders, bids and shipment tracking.
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They write about its Series A, a 60% increase in team size, new markets across DACH, the Caspian, France and Spain, and how the company prices AI work for customers. Other connections in the posts include their wife Yuliia Vozniak’s fashion brand Londi, which they say runs on AI; a Stanford pricing lecture they draw on; and a Diia.City United event where they spoke.
Cargofy
Owned business
Stakh Vozniak describes Cargofy as “my company”: a business whose AI agents run logistics operations for freight companies, handling tenders, collecting bids and tracking shipments.
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They write about closing a Series A, growing the team by 60%, opening in DACH, the Caspian, France and Spain, demonstrating the product at trade events, and pricing AI work by metering volume, such as tenders closed, carriers onboarded and documents processed.
A lingerie and loungewear brand that Stakh Vozniak describes as run by their wife, Yuliia Vozniak, with partner Nastya Kharytonova.
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They say it uses AI for product backgrounds, customer support and ad buying, and call the setup similar to Cargofy’s. The connection they describe is family and a shared approach to AI operations.
Original disclosure
My wife just did the most anti-AI thing you can do. She runs Londi, a lingerie and loungewear brand.
Stakh Vozniak cites a pricing lecture they attended at Stanford and quotes a professor there — “price the value, meter the cost” — as the basis for how they think about charging for AI workers in logistics.
Stakh Vozniak spoke at Future Tech Meetup: InvestDay, an event identified as run by Diia.City United and focused on fundraising from both founder and investor sides.
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They describe sharing Cargofy’s path from an accelerator idea to a Series A round.
Start with Stakh Vozniak's posts on pricing and budgeting AI workers, then read about Cargofy's Series A and expansion, followed by the fall event schedule for live agent demos.
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The pricing post is the clearest entry point: it lays out a two-layer model, with a fixed platform fee plus variable charges in logistics units such as tenders closed, carriers onboarded, documents processed, and calls handled. For a wider view of the same argument, read the Londi store post on AI-run operations behind a deliberately human brand, then the art collection post on the quieter business relationships alongside the logistics work.
Both write about AI agents taking over operational freight work: tenders, bids, shipment tracking, carrier communication, and the day-to-day tasks that keep brokerages and logistics teams moving.
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They use concrete operating examples rather than abstract AI claims.
Jesse Buckingham writes from the perspective of a freight-software product launch, citing brokerage metrics such as higher gross profit per load, more loads per representative, and carrier reuse rates. Stakh Vozniak writes as the founder-operator of a logistics AI company, focusing on how AI workers should be priced and budgeted, and on Cargofy's scaling.
Both argue that AI changes a core business function, not just the tools inside it.
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They focus on the operational intelligence embedded in everyday work—what teams see in tenders, suppliers, and buying decisions—and how the wider business can act on it.
Suzanne Hill writes about construction procurement and the strategic choices procurement teams face as AI reshapes the market, including training and buying-decision frameworks. Stakh Vozniak focuses more closely on freight and logistics operations, pricing structures, agent workflows, and live product demonstrations.
Both build tools for international trade and logistics and write about what should be shared infrastructure rather than rebuilt by every company.
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They explain where AI fits into freight, customs, and shipment work.
Marcelo Fujimoto focuses on modular, API-first engines for trade compliance, such as duty calculation, HTS classification, and document management. Stakh Vozniak focuses on AI digital workers that run freight operations, and how to price and evaluate them.
For a speaking or advisory placement on AI pricing models for logistics, Stakh Vozniak’s posts show reach but not audience composition.
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The only measured audience figure is 6,147 LinkedIn followers on 8 October 2026.
Their posts cover logistics, freight, and AI operations, including Cargofy, AI digital workers, and pricing AI workers, but offer no platform demographics, engagement rates, or survey results.
Treat the follower count as a measure of reach within a professional network, not evidence that the audience includes the logistics decision-makers you need.
Before booking, ask for recent follower demographics and, if available, survey results on job function, seniority, and company type.
LinkedIn followers
6,147
LinkedIn account metadata for the linked profile; the figure is not stated in any individual post · 2026-10-08
None of the five posts describes a sponsorship product, partner programme, rate card or media kit, and no partnership enquiry route is published.
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Stakh Vozniak’s commercial activity in these posts runs through Cargofy, the logistics-AI company they write about: conference stands and live demos of AI agents, small private dinners and breakfasts around industry events, and meetings booked by direct message through Cargofy team member Oleksandra Yanchenko.
They also take the stage: at a Future Tech Meetup event, they described the company’s route from an accelerator to a Series A.
They write at length about pricing AI workers in logistics, drawing on a pricing lecture at Stanford and the pricing model Cargofy runs with each customer before they sign.
For a speaking slot or advisory conversation on AI pricing models, make a direct approach naming the topic and specific event or engagement.
Ask about fees, availability and booking terms; none appear in the posts.
A logistics-technology company — a TMS or freight-AI vendor, or a carrier-network platform selling to shippers and forwarders — wants to book Stakh Vozniak for a speaking engagement or advisory role on AI pricing models for logistics.
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Decision: strong fit.
Their September 2026 post on budgeting and pricing AI digital workers lays out a position a buyer can take into a room: reject per-seat pricing because it does not match the work, and per-token pricing because a head of logistics cannot forecast what a tender costs in tokens.
The alternative has two layers — a fixed platform fee plus a variable charge expressed in logistics language, such as tenders closed, carriers onboarded, documents processed, and calls handled, never tokens.
The post also gives a four-step estimate a host can turn into a workshop: pull last month's real volume from the TMS; have the vendor define what counts as an action and whether a retry is one or two; budget a floor and ceiling using the slowest and busiest months; and measure returns beyond manual savings, since an agent can ask more carriers and make more check calls than a person.
The framework is credited to a Stanford pricing lecture and a pricing model Cargofy says it runs with every customer before they sign, grounding an advisory conversation in real volumes rather than theory.
Three more factors support the booking.
Vozniak writes from an operator's seat, describing Cargofy as rebuilding logistics operations with agents that run tenders, collect bids, and track shipments.
Their posts document a talk at a Future Tech Meetup event about the route from an Iowa accelerator idea to a Series A.
They also show a repeatable in-person format: autumn appearances at IAA Transportation, Supply Chain Europe, Innovation Week, BVL Supply Chain CX with live agent demos at stand C02, and the F3 Future of Freight Festival, alongside private dinners and breakfasts for small groups.
What to settle before booking: the five posts dated 9–25 September 2026 offer only a one-month snapshot, not a full speaking history.
The sole measured audience figure is 6,147 LinkedIn followers as of 8 October 2026; reach among freight and procurement decision-makers is unproven.
The posts give no fee, availability, or booking terms, and the pricing position comes from the proposed speaker rather than customers or third parties.
Ask for follower composition by job function and seniority, the fee and availability, and whether the engagement would include Cargofy product promotion alongside the pricing talk.
When to choose a specialist
A consumer fashion, home, or lifestyle brand, or a creative studio working for one, wants Stakh Vozniak as a paid ambassador or spokesperson for a retail product line.
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The campaign idea: a human-feeling store or product story running on an AI back end, with the creator fronting the launch.
Decision: poor fit for a paid consumer-product ambassador or spokesperson campaign.
The retail post is a thesis, not coverage of a consumer market.
The Kyiv space described in the September 2026 post belongs to a lingerie and loungewear brand, but Vozniak's point is about operations: creatives mixing real models and AI backgrounds, AI-run customer support, and ad spend shifting toward whatever converts.
The example sits outside logistics and says nothing about consumer buying behaviour, product quality, or retail category knowledge.
A brand would be buying an argument about AI in operations from a logistics founder, not a voice with a consumer following.
The posts offer little audience evidence for the campaign.
The only measured figure is 6,147 LinkedIn followers, observed on 8 October 2026, with no demographics or engagement data.
Nothing shows consumer-fashion or lifestyle readers.
The visible commercial activity points elsewhere: conference stands and live agent demos, small dinners and breakfasts around industry events, and meetings arranged by direct message through a named Cargofy team member.
The five posts show no commercial product to buy: no sponsorship format, partner programme, rate card, media kit, or partnership enquiry route.
Ask whether any ambassador offer exists before pursuing a campaign.
A consumer ambassador campaign would also rely on the weakest overlap with the documented work, which centres on AI pricing, tenders, carrier onboarding, and logistics events.
The posts contain no visuals, transcripts, or campaign assets, so a campaign dependent on imagery, product styling, or in-person retail presence cannot be assessed.
If the need shifts to an industry audience — a talk or advisory session on running operations on AI while keeping a human-facing brand — the overlap improves and merits reassessment.
A worked campaign example
Worked campaign angle for a logistics-technology vendor seeking a keynote and paid advisory session on outcome-based pricing for AI workers.
Format.
A 40-minute keynote at a logistics-tech vendor's customer or prospect event, followed by a 60-minute working session for a smaller group and an advisory retainer of two further sessions with the vendor's product and finance leads.
This follows the small-group pattern in the September 2026 event post — private dinners and breakfasts alongside conference stands — rather than a large-stage-only booking.
Angle.
"The AI worker salary: pricing agents by outcome, not by tokens."
The session tackles a live procurement debate and gives the audience practical work to do.
Run of show.
First, lay out the problem: per-seat pricing does not match the work, a head of logistics cannot forecast per-token costs, and flat monthly fees ignore seasonality.
Second, present the two-layer model — a fixed platform fee plus a variable charge stated in logistics terms: tenders closed, carriers onboarded, documents processed, calls handled.
Third, define the unit of work: have the vendor specify what counts as an action, whether a retry is one or two, and whether a rate request to twenty carriers is one or twenty.
Then test quality separately; a cheap action is worthless if the agent needs forty of them to close a tender.
Fourth, run the working session: each participant pulls last month's actual volumes from their own TMS — tenders, bids, carriers onboarded, documents, check calls — starting with a single workflow, compares the slowest and busiest month to set a floor and ceiling, and leaves with a range rather than a single number.
Fifth, revisit ROI: measure beyond manual savings, since the agent asks more carriers and makes every check call.
The comparison is not like-for-like with a person doing less.
Assets.
Turn the four estimate steps into a one-page worksheet the host can hand out and reuse after the event, then adapt them into a follow-up email series.
Both are portable: they use participants' own volume data, not the creator's.
Why it fits the posts.
The framework already appears in one post in a form the audience can use that week.
Vozniak credits it to a Stanford pricing lecture and a pricing model Cargofy says it runs with each customer before signing, giving the host a reason to bring them before a procurement-minded room.
The event and demo schedule shows they already work this circuit in person.
Booking route and unknowns.
No fee, availability, or application process is published.
The posts show only a direct approach naming the topic and event, or a message to the Cargofy team member who coordinates meetings.
Ask whether the engagement includes Cargofy product demos; the September event post suggests demos travel with the speaker.
Ask for follower composition by job function and seniority, plus recordings of past talks.
The posts provide none.
The pricing framework has no customer outcome data, third-party review, or transcript of the underlying lecture, so present it as a working method to test with the room, not settled fact.
A fit for freight brokerage or carrier-sales leadership when the session should focus on operational work—inbound freight, coverage plans, and track and trace—rather than pricing the AI itself.
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Both posts address the same buyer task through operator results.
One describes brokerage reps lifting gross profit per load by 5–10%, doubling loads per rep, and raising carrier reuse above 50%, with named examples: Brotherly Freight moving carrier reuse from 35% to 53%, and Evans reaching 75%.
The other describes track and trace handled by one person doing the work of six or seven, with a customer handing over all its expedite freight after two weeks of detailed, on-time updates.
A host seeking proof-of-outcome material and a session on how a rep’s day changes when agents take on transactional work will get more from this than from a pricing framework.
The posts market Vee and Vooma Track, explaining what the software does and what customers report.
They do not explain how to budget for or price an AI worker.
A buyer seeking a pricing argument—per seat, per token or outcome-based, and how to estimate volumes—would need to ask Stakh Vozniak directly.
The quoted results are the vendor’s own customer examples, not independently verified.
A fit when the audience is in construction procurement, not freight, and the session should explore how AI changes the buying decision rather than how to price an agent.
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One post draws a distinction suited to a workshop: AI for procurement means using tools to do research, tender analysis, contract review, supplier assessment, and spend analysis faster; procurement for the AI era means choosing among technology, outsourcing, partnering, building in-house, a managed service, or buying an outcome.
The questions become what is being bought, who owns the data and IP, where risk sits, and how dependent the business becomes on the supplier.
The second post argues that estimating, procurement, and commercial teams sit close enough to the supply chain to spot market shifts early—new suppliers, new delivery methods, and technology vendors competing for scope that used to belong to a subcontractor.
AI now makes it feasible to join up those scattered signals instead of leaving them in one person’s head.
For a construction-adjacent platform or a firm selling into contractors, this is a closer fit than a freight pricing talk.
The setting is construction, not freight.
The posts offer no logistics pricing material, per-tender or per-action cost model, or volume-based estimate a host could use in the room.
They therefore do not cover the buyer’s core need: AI pricing models for logistics.
Both close by pointing to a training offer and asking readers to send a message, so a host should expect a commercial framing alongside the operational account.
Ask directly about speaking appearances, audience size, or session recordings if those are important to the booking.
A fit when the buyer wants a platform or product-strategy angle rather than a pricing framework: an advisory or speaking session on building modular, API-first engines for international trade, covering duty calculation, HTS classification, anti-dumping and countervailing-duty intelligence, document management, and email triage.
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The engines are released so forwarders, customs brokers, shippers, and other builders can call an API instead of rebuilding a tariff engine.
One post launches a free US import duty calculator and names the trigger: the Supreme Court struck down IEEPA tariffs and Section 122 replaced them overnight, prompting the team to ship a free tool for verifying entries and quoting landed costs.
The second offers market-structure commentary: it argues that a selloff in freight brokerage stocks after an AI claim reflected investor uncertainty over which parts of the logistics value chain are defensible, and that value lies in coordinating exceptions automated systems cannot handle—customs delays, documentation discrepancies, and carrier substitutions.
A host seeking a speaker for product teams or an investor-facing audience on where AI actually bites in international trade will find that angle here.
Both posts are dated February and March 2026, roughly six months older than the posts behind Stakh Vozniak’s pricing position, and neither addresses how AI work is priced or budgeted.
The duty-calculator post is a launch announcement, not a method; the market commentary argues about investor behaviour without supporting data.
For the stated need, this is the weakest of the three on pricing, and the strongest only for a product- or investment-side audience.
Ask directly about speaking experience, audience figures, or advisory services if those affect the booking.
Stakh Vozniak's LinkedIn posts cover Cargofy's Series A and expansion, pricing and budgeting AI workers in logistics, an autumn freight-event schedule, a Kyiv retail brand with an AI-run back end, and a contemporary Ukrainian art collection.
The profile showed 6,147 followers on 8 October 2026.
The posts here are LinkedIn text, and they do not all carry publication dates, so the order of these ideas over time cannot be established.
This is a snapshot rather than a full career or speaking history.
The posts here do not provide engagement metrics, follower demographics, talk recordings, fees, booking terms, sponsorship formats, or partnership pages, and they do not establish earlier work, books, or shows through a separate name search.
Statements about business ties rest on the post text alone.
