Tom Veysey is a founder at Perch who builds custom AI and automation systems for ecommerce businesses, including tools for tracking true profit and creating ad creative.
Tom Veysey is most useful for custom AI and automation systems for ecommerce finance and ad creative.
Best for
Ecommerce automation agencies needing a delivery partner for Shopify finance and creative workflows.
Not for
B2B SaaS vendors or lead-data providers wanting sponsored placements with brand-authored scripts.
Key takeaways
They connected five Shopify stores, ad accounts, banks and payment processors for true profit tracking.
A live dashboard is not necessarily an accurate finance function.
A barber project used Google Sheets rather than a custom app because it was sufficient.
Check first: Ask for recent audience survey results or campaign delivery data before contacting them.
Main ideas
Most ecommerce brands cannot see their true profit · Live dashboards can be inaccurate; custom data connections are the fix · The AI CFO should live where the client already works
Tom Veysey is a founder at Perch, where they build custom AI and automation systems for ecommerce businesses.
Their posts are best known for showing how those systems can give Shopify brands a clearer view of profit and reduce manual work, especially in finance and ad creation.
They explain the problem, walk through the steps and share the results they say clients saw.
Their earlier experience in outbound sales also features in posts about sales automation and customer research.
Veysey makes technical work concrete, tracing solutions from business problem to data and implementation.
In one example, they describe connecting five Shopify stores, ad accounts, banks and payment processors so a client could track costs—including shipping and fees—and ask an AI CFO questions in everyday language.
In another, they describe finding currency-conversion fees, payout deductions and refunds missing from a store’s existing picture of profit.
These examples underpin their argument that a live dashboard is not necessarily an accurate finance function.
Their approach is not simply to build more software: a project for a barber used Google Sheets rather than a custom app because they judged it sufficient.
Their posts also cover outbound tools, social listening and sponsored software reviews, making ecommerce finance and creative systems a strong focus, but not their only subjects.
Tom Veysey's ideas circle around one problem: ecommerce brands scale on dashboards that show revenue but hide real costs.
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They argue the fix is custom data plumbing that connects banks, payment processors, ad accounts and supplier costs, topped with an AI agent that answers finance questions in plain language.
A second track applies the same build-it-around-the-client approach to ad creative and outbound workflows.
Their posts also show public testing of offers, narrowing Perch to AI CFO and AI Ad Engine, and a preference for simple tools over overbuilt ones.
Most ecommerce brands cannot see their true profit
Veysey treats true profit per order—not revenue or attribution—as the number that matters.
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They argue common dashboards miss fees and deductions outside Shopify: PayPal, Klarna, AmEx and international card charges, currency conversion, chargebacks, refunds issued inside PayPal, ad taxes, tariffs and bank fees deducted as payouts land. Their solution is to connect every store, Meta account, bank and processor, then keep supplier costs current through an API.
A client with 438 live products across five stores could not say its real shipping cost per product.
Veysey built a system that updates costs everywhere when a new supplier PDF arrives, while an AI CFO sends a morning briefing and answers questions such as which product is winning in which country.
They report that daily ad spend quadrupled and ROAS rose 15%, but do not claim credit for the result.
Live dashboards can be inaccurate; custom data connections are the fix
Veysey separates attribution from profit.
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Triple Whale is a cracking tool for attribution, they say, but poor for profit tracking: it does not pull real fees for processors outside Shopify Payments and uses one guessed percentage per processor.
Accounting tools often have the same gap, with PayPal fees arriving as a monthly lump rather than per order.
They argue live, accurate per-order profit is possible without waiting for month-end close or hiring virtual assistants.
In a profit leak audit of a $10M dropshipping business, Veysey lists three stores paying 60% more in fees than the other two, about $5,900 a quarter in currency conversion fees, roughly the same in bank payout deductions, $3,402 in chargebacks across three processors, and 8% of refunds issued in PayPal that Shopify still showed as paid.
The AI CFO should live where the client already works
Veysey's finance pitch is not another dashboard login.
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They install an agent in the client's chat app or Slack, connect it to Shopify, banks, Meta and Google Ads, and set it to flag anomalies, send daily and weekly briefs, and answer natural-language questions about the business. They frame the AI CFO as the most cost-effective finance hire a client will make.
For the five-store client, the AI CFO flags red flags during the day and lets the owner ask plain-English questions in the chat they already use all day, such as what last week actually made.
Custom builds beat renting software by a percentage of GMV
Veysey argues the days of paying a percentage of GMV to SaaS are fading.
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Perch's builds are custom and owned by the brand, connected to every data source where off-the-shelf tools stop. They apply the same approach to ad creative: instead of paying a markup through Higgsfield, Perch builds platforms that call image-generation models directly on the client's own API keys.
Veysey says Perch's AI Ad Engine runs on the client's own API keys at half the cost of Higgsfield, with unlimited variations and no Higgsfield credit caps.
Ad creative can be automated from proven winning patterns
Veysey describes a workflow that scrapes winning ads from brands chasing the same audience, uses Claude Code to break them down and blend them with the brand's guidelines and product photos, generates new creative, and saves the files in Google Drive ready to launch.
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The next step is launching campaigns automatically through Meta Ads MCP.
For a new ecommerce brand, Veysey claims this workflow cut time spent making ad creatives by 80%.
Veysey's stack changed as the offer matured. The first description used Higgsfield through MCP; later posts describe calling image-generation models directly to avoid paying at least double through Higgsfield and escape credit caps.
Veysey productized the first step as a $999 Profit Leak Audit.
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They promise a full refund if annualized savings do not exceed the fee; if the client proceeds to a build, the fee is credited. The audit is a paid diagnostic and the entry point to larger true-profit work.
The published audit example found processing costs at 5.1% of revenue, about $509,000 a year, before ads or product costs—none of it visible in the client's off-the-shelf profit tracker.
A recurring priority is choosing the simplest system the client will actually adopt.
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Veysey recommends a robust Google Sheet over a custom app when it is enough, a lesson they say came from working with a barber.
For a barber's business, Veysey built a Google Sheet finance tracker and admin hub rather than a custom app. They say it gave back two hours a day and ended manual tallying of staff earnings across shops.
Veysey documents the commercial side of building Perch.
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After leaving outbound, they said yes to everything for five months, audited the six different things they had been paid for, and kept the two strongest: AI CFO and AI Ad Engine. They call specialization in one offer the ideal, but say the business is still testing two.
An audit of paid work led Perch to two offers: live true profit tracking with an agent in the client's chat app, and custom ad creation platforms for ecommerce brands and agencies.
The two-offer structure is still in progress; Veysey says more testing is needed before they have clarity.
Veysey says half the practices considered normal in outbound are far less common in ecommerce, and that gap is an opportunity.
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Their outbound posts show the same operator-level approach: de-anonymize site visitors, enrich and score them, then get a Slack alert when someone visits a high-intent page; log calls in the CRM; monitor campaigns and deliverability; consolidate tools; and use social listening to gather customer language. Some posts are labeled as ads or partnerships.
Veysey set up keywords in Trigify to track X and Reddit conversations, gathered 19,000 data points over three months and fed them to Claude Code. The analysis showed where their understanding of customers was flat wrong—and the exact language and logic to use when selling to them.
The clearest answer to what Tom Veysey builds and who pays for it: a finance and reporting system for Shopify brands that cannot see their real costs.
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Veysey describes a client with 438 live products across five stores that could not tell them its real shipping cost per product.
The post walks through the build: every Shopify store and Meta Business Manager connected, banking and payment processors linked; hidden transaction fees, EU tariffs and ad taxes tracked; a new supplier PDF updating shipping costs everywhere through an API, backdated where needed; and an AI CFO sending a morning briefing and answering plain-English questions such as which product is winning in which country.
They report that daily ad spend has quadrupled and ROAS is up 15% since the work started, while explicitly saying they are not claiming credit for that result.
For brands planning a campaign
For a brand looking to connect ad spend to real margin, this is a concrete precedent. The post shows the delivery style: build around the client’s existing stack rather than sell a dashboard.
A direct account of how Perch is positioned and what it sells, worth reading before the client case posts.
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Veysey describes an earlier ecommerce attempt that went badly, a detour through outbound agencies and agency ownership, and a return to ecommerce.
They observed that practices normal in outbound are far less common in ecommerce.
The post lists three things Perch wires into ecommerce businesses: AI CFO agents connected to Shopify, banks, Meta and Google Ads that flag anomalies in Slack and send daily and weekly briefs; true profit tracking pulled directly from more than ten APIs; and custom ad creation engines that call image-generation models directly instead of paying at least double through Higgsfield.
They state that none of it is off-the-shelf and clients own what is built for them.
For brands planning a campaign
This sets expectations for a partnership: bespoke systems, client ownership and a stated dislike of percentage-of-revenue SaaS pricing.
Shows the range of work in one place, including a small job that reveals how Veysey decides how much to build.
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The post describes three builds.
For a new ecommerce brand: an automated ad creative process where Apify scrapes winning ads from competing brands, Claude Code blends them with brand guidelines and product photos, Higgsfield generates new creative via MCP, and the output lands in Google Drive, with automatic campaign launch through the Meta Ads MCP as the next step; the claimed result is 80% less time spent making ad creatives.
For a nine-store Shopify business: a finance hub pulling nine storefronts into one dashboard, wired to every Meta ad account and real banking data, surfacing transaction and shipping fees, with an AI CMO auditing accounts and an AI CFO flagging cash-flow problems; the claimed result is revenue up 26% in two months.
For Veysey’s barber: a finance tracker for staff earnings and an admin view, built as a Google Sheet system rather than a custom app, which they say gave back two hours a day.
They draw the lesson that you do not need to over-engineer something just because you can build it custom.
For brands planning a campaign
For ad creative at scale, this is the closest thing to a process demo. The barber project signals a partner willing to recommend a spreadsheet when an app would be wasteful.
Explains how Perch chose its current two offers, the fastest way to understand what the business is built to sell now.
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Veysey describes deliberately taking any business they could for five months after leaving outbound, then auditing the six different things they had been paid for and keeping the two strongest.
The two are AI CFO (live true profit tracking, with an agent in the client’s chat app for finance questions) and AI Ad Engine (custom ad creation platforms for ecommerce brands and agencies, running on the client’s own API keys at half the cost, with unlimited variations and no Higgsfield credit caps).
They say specialising in one offer would be ideal, but for now the business is testing two.
For brands planning a campaign
Scope can be assessed against two defined offers rather than a general automation service. The post also shows a partner still in a deliberate testing phase, which may shape how a first engagement is framed.
A concrete inventory of the money leaks Veysey looks for, useful for checking whether a brand’s own blind spots match.
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Working with a $10M dropshipping business, the post lists findings from a profit leak audit: stores paying out to bank accounts in different countries, with three paying 60% more in fees than the other two; about $5,900 a quarter in currency conversion fees blended into processing fees and never shown separately; 0.3% taken by the bank on every payout as it lands, roughly another $5,900 a quarter, invisible to any Shopify-connected tracker; $3,402 lost to chargebacks across three processors, only some of which appear in Shopify; 8% of refunds issued inside PayPal, so Shopify still shows those orders as paid; and processing costs of 5.1% of revenue, about $509,000 a year. The post offers a $999 Profit Leak Audit with a full refund if annualised savings do not exceed the price, credited to the build if the client proceeds.
For brands planning a campaign
Gives the entry-level offer, its price and its guarantee. A partner pitch would likely start with this audit before any build.
The clearest statement of how Perch says it differs from the off-the-shelf attribution tools most brands already run.
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Veysey calls Triple Whale excellent for attribution but poor for profit tracking because it does not pull real fees for processors outside Shopify Payments and applies one guessed percentage per processor.
They say the result is data that is live but inaccurate, leaving brands blind to what each order actually made.
They argue accounting tools have a similar problem: most do not connect natively to every processor, so PayPal fees land as a monthly lump rather than per order.
Their conclusion is that live, accurate per-order profit is achievable without an army of virtual assistants or waiting for month-end close.
For brands planning a campaign
Useful for campaigns comparing tool-led and build-led finance reporting. The post names the competitor frame Veysey uses in conversations with brands.
A published example of how they handle a paid partnership, relevant if the campaign is sponsored rather than a service engagement.
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The post opens by saying Veysey turns down most sponsorship requests and explains why they accepted this one.
Since starting Perch, they have made cold calls most days, describing them as the fastest way to start a real conversation.
The challenge is remembering who they spoke to, what was said, and when to follow up.
They say Aircall logs calls automatically, keeps recordings, produces AI summaries, and feeds into their CRM so follow-up is ready.
They credit it as a big reason they signed a couple of clients.
For brands planning a campaign
Shows sponsored content written as a first-person account of a process, with a stated reason for accepting the sponsorship, rather than a product feature list. Brands considering a sponsored slot get a realistic picture of the format.
Shows the part of Veysey's writing shaped by their outbound background and where their automation instincts were formed.
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Addressed to sales leaders, the post argues that the constraint is not talent but time spent on manual work.
It walks through four areas where Smartlead's Smart Agents make a difference: continuously monitoring campaigns and flagging underperformance early; watching mailbox and domain health and predicting when new mailboxes are needed; enriching and scoring leads automatically and updating the CRM; and detecting interested replies to draft a contextual response for human approval.
The stated position is that AI handles repetitive execution while the team focuses on conversations and closing, with no headcount cuts required.
For brands planning a campaign
Relevant if a campaign touches sales or outbound automation rather than ecommerce finance. It shows the same approach—building a process around the team—applied to a different problem.
True profit tracking and AI CFO systems for ecommerce brands6
Outbound and sales operations automation6
AI ad creation engines3
Founder testing, offer narrowing and practical builds3
Profit Leak Audit as a productized entry point2
Tool critique and the case against off-the-shelf profit trackers2
True profit tracking and AI CFO systems for ecommerce brands
Veysey focuses on true profit tracking for Shopify and ecommerce businesses.
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They argue that most sub-$10M brands cannot see real profit because fees, currency conversion, chargebacks, PayPal refunds, ad taxes, tariffs and bank payout deductions sit outside Shopify or Triple Whale.
They describe connecting every Shopify store, Meta Business Manager, banks and payment processors, supplier quote PDFs and an AI CFO that sends daily briefings and answers finance questions in chat.
One client had 438 live products across five stores.
A nine-store Shopify business saw revenue rise 26% in two months after the finance hub was built, and a five-store client increased daily ad spend fourfold with ROAS up 15% after the system was in place.
Builds with APIs around the client's actual stack, then installs an AI agent in the chat tool the client already uses.
Veysey distinguishes attribution from profit tracking: Triple Whale can be good for attribution while still being inaccurate for profit.
For brands planning a campaign
Brands and agencies can assess Veysey for a finance-data and AI-agent build, particularly if they operate multiple stores, use complex payment processors or lack a reliable per-order profit view. For a dashboard, AI CFO agent or both, agree on the deliverables: the posts show these components evolving together.
In one build, Apify scrapes winning ads from brands chasing the same audience; Claude Code breaks them down and blends them with the brand's guidelines and product photos; Higgsfield generates new creative through MCP; files land in Google Drive; and the next step is launching campaigns through Meta Ads MCP.
Veysey later says Perch builds ad platforms that call image-generation models directly, using the client's own API keys at half the cost of Higgsfield, with unlimited variations instead of credit caps.
The approach is custom and owned by the brand, not an off-the-shelf subscription.
Uses scraping, LLM analysis, image-generation APIs and workflow automation to produce ad creative at volume.
The described stack shifts from Higgsfield to direct model integrations. Ask which technical stack is included in the proposed offer.
For brands planning a campaign
Brands and agencies can assess Veysey for a creative-automation build when ad volume is the bottleneck. Before partnering on a managed service, white-label engine or co-built platform, clarify the deliverables: the posts describe each model differently over time.
A $999 Profit Leak Audit is the productized front door.
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Veysey promises a full refund if annualized savings do not exceed the fee, and credits the fee toward a build if the client proceeds. A published audit example found three stores paying 60% more than two others in fees; about $5,900 a quarter in currency conversion fees; about $5,900 a quarter in bank payout deductions; $3,402 in chargebacks across three processors; 8% of refunds issued inside PayPal but still marked paid in Shopify; and processing costs at 5.1% of revenue, about $509,000 a year.
Audit first, build second, with the audit fee credited to implementation if the client continues.
The audit is a paid diagnostic, not a free assessment.
For brands planning a campaign
A brand or agency could co-market the audit, use it to scope a finance-automation engagement or position it as the first step before a larger true-profit build.
Before and alongside Perch, Veysey writes detailed outbound workflow teardowns.
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Posts cover a speed-to-lead flow using RB2B to de-anonymize site visitors, enrich them, push them to HubSpot and score them, then alert Veysey in Slack when a high-intent page is visited; Aircall logging call transcripts into HubSpot; Smartlead Smart Agents monitoring campaigns, deliverability and lead qualification; tool consolidation; FullEnrich Search across 20+ data sources; and Trigify social listening on X and Reddit, where 19,000 data points were grouped by Claude Code into customer language and wrong assumptions. Several posts are labeled as ads or partnerships.
Shows practical, operator-level workflow design rather than abstract commentary.
This serves a different buyer need from ecommerce finance and creative work. Some posts carry sponsored or partner labels; ask whether a proposed partnership is paid.
For brands planning a campaign
Useful for partnerships involving outbound or sales tech: the posts show hands-on workflow design and sponsored-tool demonstrations. Ask for ecommerce implementation examples if that is the brief.
Founder testing, offer narrowing and practical builds
Veysey documents going solo, saying yes to everything for five months, and auditing six paid offers before narrowing Perch to two main offers: AI CFO and AI Ad Engine.
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They describe a barber finance tracker, their mother's counselling site and putting their father's 10,000+ record collection onto Shopify with a stock-tracking app. A recurring lesson is not to over-engineer: Veysey used a Google Sheet for the barber instead of a custom app because the simpler tool would actually be used.
Tests offers in public, audits what clients pay for, then narrows around the strongest demand.
The current two-offer structure is explicitly described as in progress, with more testing expected.
For brands planning a campaign
Brands can evaluate Veysey as an early-stage founder-operator who favors practical, used systems over polished but unused software. Partnerships should allow iteration and direct founder involvement; clarify which offers are current.
Tool critique and the case against off-the-shelf profit trackers
In an unpopular-opinion post, Veysey criticizes Triple Whale’s profit tracking, not its attribution.
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They say it does not pull real fees for PayPal, Klarna, AmEx surcharges, international cards or currency conversion, and instead uses one guessed percentage per processor.
Accounting tools often do not connect natively to every processor, so PayPal fees arrive as a monthly lump rather than per order.
They argue that a custom build can deliver 100% live per-order true profit and that paying a percentage of GMV to SaaS is fading.
Compares off-the-shelf trackers with custom, processor-level data connections.
Veysey still calls Triple Whale a cracking tool for attribution and says to keep it for that purpose.
For brands planning a campaign
This gives a brand a clear way to assess fit if inaccurate profit data—not attribution—is the problem. It positions Veysey as an alternative or complement to off-the-shelf trackers, with an intentionally polarizing opinion.
Tom Veysey presents themself as the person behind Perch, the ecommerce AI automation business whose AI CFO and AI ad-creation offers they describe in their posts.
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They have taken paid sponsorship work with Aircall, promoting it twice with an affiliate link and an ad disclosure, and identify as a FullEnrich partner. They also post in detail about Smartlead's products without stating any relationship to the company.
Perch
Owned business
Veysey posts as the person behind Perch, describing “what we do at Perch: bring ecom brands into the AI age” after going “all in on Perch”.
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They say Perch settled on two main offers: an AI CFO (live true-profit tracking plus a finance agent in the client's chat app) and an AI Ad Engine that runs ad creation on the client's own API keys.
A post marked “#ad” and “In partnership with Aircall” walks through how Aircall logs their cold-call transcripts into HubSpot and shares an affiliate sign-up link.
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A second post explains why they accepted the sponsorship over others, citing automatic call logging, recordings and AI summaries, and links to Aircall again.
Original disclosure
“In partnership with Aircall #ad” (first post); “I turn down most sponsorship requests.
Veysey writes that they have been a FullEnrich user “for a while” and are “proud to be a #FullEnrichPartner”, then introduces FullEnrich Search, covering its 20+ sources, saved lists, filters and API access.
Two posts detail Smartlead's features: one frames its Smart Agents as handling campaign monitoring, deliverability, lead enrichment and fast reply drafting for in-house outbound teams; the other welcomes its Unlimited Contact Storage and triple-verified lead data as part of tool-stack consolidation.
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Neither post states a sponsorship, partnership, client or affiliate relationship.
Tom Veysey writes about building custom AI systems for ecommerce brands, mainly for true profit tracking and ad creative.
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His posts start with a recurring finance problem: most brands scale on dashboards that show revenue but miss fees, currency conversion, chargebacks, refunds and bank deductions.
Other posts cover the AI CFO they install in a client's chat app, the $999 Profit Leak Audit, and AI ad creation engines that run on a brand's own API keys.
A second track covers outbound and sales operations automation—speed-to-lead workflows, call logging, lead enrichment and social listening—and how they transferred habits from outbound agencies into Perch.
The posts also include founder reflections on testing offers in public and choosing simple tools over over-built ones.
For similar creators, compare Asad Khan on DTC store trust and Meta Ads, Joshua Graham on acquisition economics and LTV, and Abraham Buchinger on ecommerce lessons and platform shifts.
Both write about DTC ecommerce growth and Meta Ads from an operator's perspective.
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Asad Khan argues that dropshipping is not dead but cheap-looking stores are, focusing on building trust and testing creative angles. Tom Veysey also treats customer experience and ad creative as levers for ecommerce growth.
Khan's posts centre on Shopify store trust, product-page quality and Meta ad angles for dropshipping, with a free audit offer. Veysey's work goes deeper into finance-data plumbing, AI CFO agents, payment-processor fee reconciliation and custom ad-creation engines.
Graham uses acquisitions and LTV-to-CAC ratios to explain why repeat purchase and customer value matter more than cheap acquisition; Veysey likewise argues that knowing true profit per order is non-negotiable when scaling ad spend.
Graham focuses on growth strategy, acquisition economics and the Meta-to-Amazon demand halo. Veysey builds custom finance and creative automation systems for individual brands rather than offering strategic commentary.
Both offer practical ecommerce lessons for brand operators.
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Buchinger stresses clarity over design, segmentation and focusing on traffic and messaging; Veysey stresses accurate profit data and using the simplest tool the client will actually adopt.
Buchinger's posts offer strategic lessons and platform commentary, such as Google's Gemini shopping move. Veysey documents custom implementation work, including AI finance agents and automated ad engines.
Tom Veysey's posts point to content aimed at ecommerce founders and operators, especially sub-$10M Shopify brands concerned with true profit, AI CFO agents, and ad creative.
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The only published audience figure is 6,223 LinkedIn followers on October 9, 2026; it shows platform reach, not company size, job title, seniority, or budget authority.
For campaigns targeting Shopify brand owners, finance leads, or agency decision-makers who can approve AI finance and creative workflow projects, ask Tom for recent audience survey results or campaign delivery data showing whether readers include those groups.
Audience fit is unverified until then.
If video performance matters to the campaign, ask for a video performance summary; the posts provide no video-level audience or delivery comparison.
LinkedIn followers
6223
Tom Veysey's LinkedIn profile account metadata · 2026-10-09
Tom Veysey's posts show two disclosed commercial relationships and a service business, but no published sponsorship policy, media kit or rate card.
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The two clearly disclosed sponsored posts are both for Aircall, the calling and CRM software they use for their own cold-call workflow: one (16 June 2026) is labelled #ad with a tracked signup link; the other (4 August 2026) opens by saying they turn down most sponsorship requests and said yes to this one, again with a signup link.
A separate relationship with lead-data vendor FullEnrich is stated in a 9 February 2026 post, where they label themself a partner and walk through the product's sources, filters and API.
The posts do not state fee, duration, exclusivity, approval rights or reporting; ask for these terms before agreeing a campaign.
They also write positively about Smartlead in January and February 2026 without any paid-partnership disclosure, so payment is unconfirmed and should not be assumed.
Their commercial model is their own service business, Perch: two named offers, AI CFO and AI Ad Engine, scoped and priced through direct conversation rather than a public rate card, with a $999 Profit Leak Audit credited to the build as the documented paid entry step.
The posts show no official partnership or sponsorship inquiry route; ask whether a LinkedIn direct message is the preferred contact route.
Disclosed sponsored post (Aircall)
Buyer requirement
Expect a first-person workflow story in Veysey's own voice, labelled as an ad or sponsorship and carrying a tracked signup link, rather than a brand-authored script.
Both disclosed examples promote the same vendor roughly seven weeks apart, so a single-post test is the realistic first step.
The posts do not state the fee, timing, exclusivity or approval rights; agree those in writing before setting campaign terms.
Ongoing software partner content (FullEnrich)
Buyer requirement
This relationship is marked with a partner label instead of an ad label.
The post is a product walkthrough covering data sources, list and filter behaviour, and API availability, and closes with an open question to the audience.
Treat it as an ongoing partner-flag arrangement rather than a one-off advert.
Confirm in writing whether the partner label is contractually required, what it obliges them to post, and whether competing vendors are excluded.
Done-for-you AI build (Perch's AI CFO and AI Ad Engine)
Buyer requirement
Veysey sells implementation, not audience access.
Two offers are named and described, scoped and priced in direct conversation, with no public rate card.
The documented paid entry point is a $999 Profit Leak Audit, refunded if annualised savings do not exceed the fee and credited to the build if work proceeds.
For an agency proposing a joint build, expect a scoped project on the client's own systems and API keys.
Confirm who holds the client relationship, the code and ongoing support before agreeing terms.
Tool commentary with no payment disclosure (Smartlead)
Buyer requirement
Do not read every vendor mention as a partnership.
Two posts praise Smartlead's pricing update and its new agent features with no ad, sponsorship or partner label.
Before assuming a paid relationship exists, or treating them as unaffiliated with a competitor, ask directly which vendor posts are commercial and which are unprompted.
A B2B ecommerce automation agency wants a delivery partner to build AI-driven finance and creative workflows for scaling Shopify brands, with the agency holding the client relationship and Tom Veysey supplying the builds.
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This is the closest match to what the posts show being sold.
Two named offers sit behind it: AI CFO, described as live true-profit tracking plus an agent in the client's own chat app, and AI Ad Engine, described as a custom ad creation platform that runs on the client's own API keys at roughly half the cost of going through Higgsfield, with unlimited variations instead of credit caps.
The finance side is documented in more detail than most partner pitches: every Shopify store, every Meta Business Manager, banking and payment processors connected; hidden transaction fees, EU tariffs and ad taxes tracked; supplier quote PDFs updating shipping costs across the catalogue through an API; and an AI CFO that briefs the client each morning and answers questions such as which product is winning in which country.
Client examples include a five-store, 438-product brand that could not state its real shipping cost per product; a nine-store Shopify business whose nine storefronts were pulled into one dashboard wired to every Meta ad account and real banking data; and a $10M dropshipping business whose audit found processing costs at 5.1% of revenue, about $509,000 a year.
The commercial shape suits an agency partnership.
The documented paid entry point is a $999 Profit Leak Audit, refunded if annualised savings do not exceed the fee and credited to the build if the client proceeds.
It gives an agency a low-risk discovery step before a larger engagement.
Tom also states that clients own what is built for them, that none of it is off-the-shelf, and that paying a percentage of GMV to SaaS is fading—a position an agency can resell rather than compete with.
Three limits belong in the partnership terms.
The offers are explicitly still being tested: two survived an audit of six paid projects, and the posts say specialisation in one offer would be ideal.
Client results are reported in Tom's own words, with no independent figures, contracts or delivery data.
The finance claims target sub-$10M brands and criticise off-the-shelf profit trackers.
Ask for evidence of enterprise-scale finance transformation before pitching this to larger clients.
When to choose a specialist
A B2B SaaS vendor or lead-data provider wants paid reach—a straightforward sponsored placement to Tom Veysey's LinkedIn audience, judged on impressions and tracked link clicks, with a brand-authored script and no implementation angle.
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Four things make this a poor fit.
First, audience composition is unverified.
The only measured audience figure published here is 6,223 LinkedIn followers on the account observed 9 October 2026, with no job titles, company sizes, seniority or budget authority.
A vendor cannot substantiate reach against a named buyer group before paying.
Second, the format is a mismatch.
Both disclosed sponsored posts are for Aircall and written as first-person stories about a tool Tom uses in their own cold-call workflow, with a tracked signup link.
The disclosed FullEnrich relationship is a product walkthrough covering data sources, filters and API availability.
In the second Aircall post, Tom opens by saying most sponsorship requests are turned down and explains why that one was accepted.
A vendor wanting a scripted, brand-voice advert is asking for a format the posts do not show.
Third, the commercial terms are undocumented.
No sponsorship policy, media kit, rate card or inquiry page appeared in the posts, so fee, duration, exclusivity and approval rights must be agreed in writing before a briefing.
Fourth, the vendor landscape is ambiguous.
Tom writes positively about Smartlead in January and February 2026 with no advertisement, sponsorship or partner label, while criticising Triple Whale directly on profit tracking in a deliberately polarising post.
A vendor cannot tell from the outside which favourable mentions were paid; one competing with a praised tool also faces an alignment risk the posts do not resolve.
A second poor fit is finance or reporting work for brands well above $10M.
The posts target sub-$10M ecommerce brands and describe building around off-the-shelf tools those brands already run.
Ask for evidence of larger enterprise finance work before proceeding.
A worked campaign example
Worked angle for the agency's need: a co-branded profit leak pilot, with the agency selling and Tom delivering.
Step one: lead with the audit, not a build.
Offer the $999 Profit Leak Audit as the first paid engagement for a jointly chosen client.
It is the only offer with a stated price and guarantee: a full refund if annualised savings do not exceed the fee, and the fee credited toward the build if the client continues.
The agency can position this as paid discovery with a refund floor, which is easier to sell than an open-ended automation project.
Step two: scope the deliverable to one of the two current offers before writing any statement of work.
A client whose pain is fees, currency conversion, chargebacks and payout deductions behind Shopify points to AI CFO and true-profit tracking.
A client whose bottleneck is ad volume points to AI Ad Engine.
The agency should say whether it wants a managed service, a white-label engine or a co-built platform, since the posts describe each model at different points.
Step three: use the audit output as the campaign hook.
The published audit inventory is specific enough to reuse as a client-facing checklist: stores paying out to bank accounts in different countries, where three paid 60% more in fees than the other two; about $5,900 a quarter in currency conversion fees blended into processing fees; another $5,900 a quarter in bank payout deductions invisible to Shopify-connected trackers; $3,402 in chargebacks across three processors; 8% of refunds issued inside PayPal while Shopify still shows those orders as paid; and processing costs at 5.1% of revenue.
Step four: agree in writing on what the posts leave open: fees and revenue split, who holds the client relationship, who owns the code and the client's API keys, ongoing support responsibilities, and whether Tom may sell the same offer to the agency's competitors.
The posts state that clients own what is built and that systems run on the client's own API keys, but include no contract.
Document the default rather than assume it.
Step five: plan one deliverable before scaling.
The one-source operational floor and missing audience and performance data mean the pilot's success should be measured on client outcomes—fees found, savings annualised, hours saved—not reach.
Put two risks to the agency at the pitch stage: client results in the posts are self-reported without independent figures, and the business is still testing whether to specialise in one of its two offers, so a long exclusivity clause would be premature.
Choose Asad Khan when the need is to convert traffic once it reaches a store, rather than build automated finance and ad-creation systems.
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The posts describe rebuilding a store to cross $100,000 in sales by making it look like a real brand, answering customers’ questions before they ask, testing several creative angles instead of relying on one winning ad, and scaling gradually rather than forcing growth.
Another post argues that the best marketers study people, not ad platforms, because messages convince people and platform settings do not.
For an agency whose client already has a builder and needs to improve storefront trust and test creative angles first, Khan is the closer match.
The work shown is manual: store rebuilds, creative testing and recorded Loom audits of a store, product page and ad funnel.
There is no finance integration, API plumbing or repeatable automated delivery system shown, so a client seeking a live profit engine or an ad engine running on its own API keys would still need a builder such as Tom Veysey.
Store and creative testing, plus manual audits; no finance integration or automated system build shown.
Choose Joshua Graham when the agency’s client needs to decide what to sell, how much it can afford to spend acquiring a customer, or how to count demand that converts somewhere other than the site.
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The posts analyse P&G’s $3.8 billion purchase of a supplement brand and Unilever’s reported $1.2 billion purchase of another.
They argue that the buyers paid for customer economics, clear positioning and repeat purchase behaviour, including the claim that one founder runs the business at a minimum three times fully burdened LTV to CAC.
Another post argues that Meta creates demand that Amazon harvests, so a brand focused only on Shopify ROAS may pull back spend even when its real acquisition cost is lower than the dashboard shows.
For a client tackling growth economics or channel attribution before any build, this analysis is the better starting point.
The posts offer analysis and recommendations, not delivery.
For AI-driven finance and creative workflows, the client would still need an implementation partner; that work appears in Tom Veysey’s posts.
Growth economics and attribution analysis; no implementation or automated builds shown.
Choose Abraham Buchinger when the client needs channel and messaging strategy before anything is built.
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One post argues that better tools do not raise sales without a decision owner, that traffic and messaging should be fixed before conversion work or email design, and that clear offers and plain communication beat polished design.
Another reports Google expanding shopping inside Gemini with Walmart, Shopify and Wayfair, notes shoppers using AI during their buying journey, and argues that single-channel Amazon brands face rising platform risk.
For a client still deciding where demand should start and who owns each decision, this framing is useful before implementation.
The posts offer commentary, not delivery: they do not show a finance build, ad engine or custom automation work.
Channel and messaging strategy commentary; no technical build or delivery evidence.
Tom Veysey's 13 LinkedIn posts cover Perch's ecommerce finance and ad-creation systems, earlier outbound and sales-automation work, social listening, and software sponsorships or partnerships.
Three other ecommerce creators' six LinkedIn posts were published from January to August 2026.
Tom Veysey's posts do not say when they were published; a snapshot of the account is dated October 9, 2026.
Some posts are labelled as advertisements or partnerships.
No videos, podcasts, first-party site pages, or independent verification of reported client results accompany these posts, so client outcomes are described as the posts state them.
