Sushanth Raman works at Pallet on AI tools for supply-chain and logistics operations, and shares perspectives on product development and building the company.
Sushanth Raman is most useful for concrete examples of AI agents in supply chain and logistics operations.
Best for
A B2B logistics software company wanting content on AI agents in supply chain operations.
Not for
A broad business audience video campaign or high volume publishing commitment.
Key takeaways
An agent may retrieve rates and prepare a quote, but should not create an order.
EDI connections can create durable network effects, making EDI one of the strongest standards.
AI coordination could make vertical integration more attractive in delivery.
Check first: Confirm audience demographics and recent post results for comparable topics before contacting them.
Main ideas
AI agents should work across the systems operations teams already use · Enterprise agents need permissions matched to the actions they take · Old standards can become more valuable as agents connect more partners
Sushanth Raman works at Pallet on AI tools for supply-chain and logistics operations.
Raman’s LinkedIn posts are known for connecting practical product problems—such as agent permissions, browser-based workflows, and enterprise-system integrations—to broader questions about how AI may change supply chains.
They also offer glimpses of company building and hiring.
Follow Raman for concise examples of where automation meets the operational details of freight, fulfillment, and business software.
Raman’s strongest recurring approach is to make an abstract AI claim concrete through a specific workflow or constraint.
A post about agent governance draws a clear boundary: an agent may retrieve rates and prepare a quote, but should not create an order.
Posts about CargoWise and NetSuite explain how fragmented information and difficult data formats complicate automation.
Other posts widen the lens: Raman argues that EDI connections can create durable network effects, and that AI coordination could make vertical integration more attractive.
The range is useful to readers interested in both implementation and business strategy.
There is a tradeoff: several integration posts follow a similar product-focused pattern, and the posts reviewed do not provide detailed evidence for the underlying tooling or frameworks.
The supplied material contains no verified signature book, show, or earlier body of work, so targeted identity research is needed before treating this social-post sample as a complete account of what Raman is known for.
Sushanth Raman’s posts make a practical case for AI in supply-chain work: agents should handle real tasks across the systems companies already use, while operating within clear limits.
Read more
Raman also connects those operational details to broader ideas about old integration standards and how AI could change the economics of running more of a supply chain.
AI agents should work across the systems operations teams already use
Raman focuses on the friction between business workflows and the software around them, rather than presenting AI as a reason to replace existing systems.
Read more
The posts describe agents gathering scattered information, handling exceptions and updating operational tools. The examples make interoperability—not just the model’s capability—a central part of automation.
For NetSuite users, Raman describes order, inventory, procurement and fulfillment information spread across email, documents and other systems; Pallet brings that context together and keeps NetSuite current. For CargoWise, the post identifies XML schemas as a challenge for language models and says Pallet built tooling to make the data interoperable.
Enterprise agents need permissions matched to the actions they take
Raman argues that agents can affect inventory, customer commitments, costs and downstream operations, so teams need to decide what each agent may do.
Read more
The emphasis is on setting boundaries around specific actions, not treating an agent as an employee with open-ended access.
A quoting agent may retrieve rates and generate a quote, Raman writes, but should not be able to create an order. The post mentions a Pallet framework for scaling agent deployments, but does not detail the framework itself.
Old standards can become more valuable as agents connect more partners
Raman challenges the idea that network effects belong mainly to consumer platforms.
Read more
A long-established business standard can also be hard to displace when each new connection takes time and money to set up. In this view, AI’s role is to work across the varied ways partners send information, not to make those differences disappear.
Raman describes a global manufacturer using SAP with a network of warehouses and logistics providers. Partners send information through EDI, email or spreadsheets; Pallet agents can take that information and transact it in SAP or other connected systems.
AI could make controlling more stages of a supply chain more attractive
Raman uses a logistics acquisition to explore a broader business question: whether AI can reduce the coordination burden that has made vertical integration difficult.
Read more
The argument is that coordinating decisions across a whole order could matter more than optimizing fulfillment and transportation separately. This is Raman’s analysis of the deal, not a detailed financial case.
In a post about DoorDash’s acquisition of Instacart, Raman points to Instacart’s grocery fulfillment technology and retailer integrations alongside DoorDash’s last-mile delivery network. Combining them could give a company more control from inventory availability through fulfillment and delivery.
Company building means giving people room to own ambiguous work
In a hiring post, Raman presents the Founder's Office as a place to learn company building through responsibility from an early stage.
Read more
The team is described as bringing together people with different strengths and backgrounds, rather than following one standard career path.
Raman calls the office a training ground for future founders and describes team members taking on work ranging from ambiguous projects to partnerships and entry into new markets.
It answers the first question a newcomer should ask about agent work in supply chain: what is an agent permitted to do, and who decides?
Read more
Raman states the boundary problem and gives one worked example instead of a generic pitch.
Raman frames the problem directly: AI agents are not employees, but they take actions inside the systems that run a business, so supply-chain actions can move inventory, customer commitments, and costs.
The concrete example is a quoting agent that may pull rates and produce a quote but should not be able to create an order.
Raman says Pallet built a framework for IT teams scaling agent deployments.
Raman does not reproduce the framework in the post; it is pointed to in the comments, so the permission model itself is not visible in the reviewed material.
For brands planning a campaign
A collaboration could be built around agent permissions and guardrails, because that is where Raman is already pointing readers. The post establishes the topic and the example, but a partner would need the linked framework or a direct briefing to speak concretely about how the controls work.
This is the clearest sample of how Raman reasons about market structure, not just Pallet's product.
Read more
It answers what they think AI changes at the strategy level, which matters if you are evaluating their judgement rather than their tooling.
Raman uses the DoorDash acquisition of Instacart to argue that AI changes the economics of controlling more of the supply chain.
Both companies run last-mile delivery, and Instacart adds grocery fulfillment technology and retailer integrations, so a combined company could control more of the order lifecycle.
Raman's point is that operational complexity used to be the price of vertical integration, and AI coordination across fulfillment and transportation could improve profitability across the whole order instead of optimizing each stage separately.
For brands planning a campaign
Suitable for sponsoring analysis rather than product demonstration: a sponsored industry-argument format fits Raman's voice here. Treat it as Raman's interpretation of a reported deal, not verified financial analysis.
It shows two things at once: how Raman turns a customer conversation into an argument, and why an old data standard is central to current agent work.
Read more
Newcomers get the commercial context behind the integrations Raman keeps posting about.
Raman argues that EDI, a standard in use since the 1970s, may have stronger network effects than consumer platforms because every new trading-partner connection costs time and money to set up and the standard is hard to replace.
The evidence is a conversation with a global manufacturer running warehouses and third-party logistics providers on SAP.
Raman then connects it to Pallet's agents, which can take information from partners through EDI, email, or spreadsheets and transact it directly in SAP or other connected systems.
For brands planning a campaign
Raman's customer conversations are the raw material for their content, so a co-authored customer-scenario piece would fit the pattern. The manufacturer is unnamed and the details are thin, so any collaboration using this angle needs an approved customer example or anonymized detail.
It answers whether Raman's claims are grounded in engineering detail or marketing language.
Read more
One specific technical constraint, stated plainly, is more informative than a feature list.
Raman names an obstacle instead of skipping it: many customers run freight forwarding and landside operations on CargoWise, and CargoWise's XML data schemas are hard for large language models to handle reliably, so Pallet had to build its own tooling to make the data interoperable.
The work described is automatic shipment building, status updates, and quoting.
Raman does not show or name the tooling.
For brands planning a campaign
A technical collaboration could center on interoperability problems, which is where Raman shows credibility. The engineering claim is Raman's own description and no implementation detail is in the reviewed material, so a partner should validate the tooling before building content around it.
It is the shortest concrete illustration of what Pallet's agents actually do in place of a person, which helps a reader test whether the more abstract governance and integration posts describe real work.
Read more
Raman starts from an everyday annoyance: important supply-chain workflows begin with a browser asking whether you are human.
The post says Pallet agents already operated browser-based applications and can now navigate image-based CAPTCHAs, sliding puzzles, and Cloudflare challenges, so customers do not have to step in when a workflow hits a barrier.
No method, reliability figure, or limitation is given.
For brands planning a campaign
Useful if the campaign angle is automation coverage, since CAPTCHAs are an easy-to-recognize example. The capability is self-reported with no demonstrated method, so avoid presenting it as independently tested.
Reading this alongside the CargoWise post shows the pattern in Raman's publishing: one short post per major enterprise system, each ending in a direct-message invitation.
Read more
That pattern tells a newcomer both what Pallet connects to and how Raman chooses topics.
The structure is the one Raman uses repeatedly: customers run on NetSuite, the information needed for orders, inventory, procurement, and fulfillment is fragmented across emails, documents, and other systems, and Pallet brings the context together, resolves exceptions, and keeps NetSuite current.
The CargoWise post follows the same template with a system-specific detail swapped in.
Raman ends by inviting a direct message from anyone considering AI in those workflows.
For brands planning a campaign
An integration-specific collaboration would fit the established format, for example a new connector or ERP partnership. Because the framing is near-identical across posts, a partner should add a distinct problem or customer detail rather than repeating the template.
It answers what Raman values in hiring and how they describe their own team, which is a different view of the same person than the product posts.
Read more
It also explains the company-building narrative Raman returns to.
Raman describes the Founder's Office as a training ground for future founders and gives four examples of the people already there, listing their prior backgrounds: supply-chain study at MIT, a former founder and Stanford computer science graduate, AI transformation work at McKinsey, and global trade experience across APAC.
Raman says the best people are 'spikey in different ways' and invites high-agency, curious candidates to make contact.
The credentials are as Raman states them.
For brands planning a campaign
Team building and hiring are a real content theme for Raman, so an employer-brand or talent collaboration is plausible. It sits away from the supply-chain operations brief, so weigh whether the audience overlaps before committing.
Recurring post format: one operational problem, then an open invitation5
AI agents that work inside existing supply-chain systems3
Building the team behind the product2
Agent permissions and enterprise IT governance1
Browser-based workflows and the CAPTCHA problem1
EDI as a durable integration layer1
How AI could change the economics of vertical integration1
Founder-circuit speaking and peer events1
AI agents that work inside existing supply-chain systems
Sushanth Raman's posts return repeatedly to the same argument: agents should operate within the software operations teams already run, rather than replace it.
Read more
One post describes order, inventory, procurement and fulfillment information scattered across emails and documents, with Pallet pulling that context together and keeping NetSuite current.
Another covers CargoWise, used by many of the company's freight forwarding and landside customers, where Pallet automates shipment building, status updates and quoting — and where the post notes that CargoWise's XML schemas are hard for language models to handle reliably, so Pallet built its own tooling to make the data interoperable.
A third describes agents drawing information from partners through EDI, email or spreadsheets and transacting it directly in SAP or other connected systems.
Integration-by-system posts: name one enterprise system, name the failure mode that blocks automation, then explain the fix.
For brands planning a campaign
A collaboration framed as 'agents that run your existing stack' matches what this creator already talks about. Ask for a worked example tied to one named system (NetSuite, CargoWise, SAP) rather than a general AI pitch, since that is the level of detail shown in the posts.
One post argues that as AI agents take actions across enterprise systems, teams need to define what each agent is allowed to do.
Read more
The example given is a quoting agent that can retrieve rates and generate a quote but should not be able to create an order.
In supply chain, the post says, agent actions can touch inventory, customer commitments, costs and downstream operations.
Pallet published a framework aimed at helping IT teams scale agent deployments safely; the post directs readers to a link in the comments rather than detailing the framework itself.
For brands planning a campaign
Governance and control is a strong, less crowded angle than generic automation messaging, and it speaks to IT and operations buyers. A content collaboration on 'what should an agent be allowed to do' would extend a topic this creator already raises.
A post describes giving Pallet agents the ability to operate browser-based applications and, more recently, to navigate image-based CAPTCHAs, sliding puzzles and Cloudflare challenges, so customers do not have to step in when a workflow hits one.
Read more
The framing is that many important supply-chain workflows begin at a browser prompt asking whether the user is human.
The post describes the capability but does not discuss site terms, vendor permission or how blocked or failed challenges are handled, so the public post alone should not be treated as a complete compliance picture.
For brands planning a campaign
This is a concrete, memorable example of where automation commonly breaks. It is also the most compliance-sensitive topic in the reviewed material, so a brand in this space should ask how the approach handles site terms and access rules before building a campaign around it.
A post argues that EDI may have one of the strongest network effects of any standard — stronger than the network effects usually cited for consumer platforms — because it has been in place since the 1970s and every new partner connection costs time and money to set up, making it hard to replace.
Read more
The example is a global manufacturer managing inventory in SAP through a network of warehouses and third-party logistics providers. Pallet's agents take partner information through EDI, email or spreadsheets and transact it in SAP or other connected systems.
For brands planning a campaign
A partnership angle around the long tail of partner connections — some on EDI, some not — fits the argument already being made. It suits an audience of supply-chain operators who recognize the standard as a constraint rather than a novelty.
How AI could change the economics of vertical integration
A post on DoorDash's acquisition of Instacart argues that AI could make vertical integration more economically attractive.
Read more
Instacart brings grocery-specific fulfillment technology and retailer integrations on top of last-mile delivery, and combining the two could give DoorDash more control over the order lifecycle, from inventory availability through fulfillment to delivery. The traditional tradeoff, the post says, is that controlling more stages means more operational complexity — and AI could change that by coordinating decisions across fulfillment and transportation, improving profitability across a whole order instead of optimizing each operation separately.
The argument is made without cited figures, deal terms or third-party data, so it should be treated as commentary rather than evidence about the transaction.
For brands planning a campaign
This is market analysis rather than product explanation, useful for reaching executive readers who follow logistics M&A and network economics. It works for a strategy or thought-leadership slot; it does not carry product proof.
A post recounts speaking at Morgan Stanley Spark alongside people from Cognition, Mercor, Baseten and EliseAI, with an observation that building a company means learning how founders in unrelated industries approach hard problems.
Read more
The post is about participation and the exchange of perspectives rather than a summary of what was presented.
For brands planning a campaign
This supports event-based formats — panels, hosted conversations, founder-audience programming — where the draw is the operator perspective, not a scripted product demo.
Two posts cover company building rather than product.
Read more
One describes growing the Founder's Office at Pallet as a training ground for future founders, naming people on the team and what they had done before — supply-chain study at MIT, founding a company after a Stanford computer science degree, AI transformation work at McKinsey, global trade across Asia-Pacific. The other announces Abbey Garwin joining as Head of Global Revenue, describing a sales career that started at Salesforce and moved through Namely, Syndio and Motive, and the intent to scale global sales as the company expands across what the post calls the physical economy.
For brands planning a campaign
These posts show the creator is willing to publish about hiring, team composition and growth, which suits employer-brand or talent-focused work more than product campaigns. A brand collaboration built on this theme should expect a company-building narrative rather than a technical one.
Recurring post format: one operational problem, then an open invitation
Across the product posts, the pattern is consistent: an opening line that states a problem, a few short paragraphs explaining why it is hard, what Pallet does about it, and a closing invitation to send a direct message.
Read more
The posts are plain text, written for operators — naming systems, schemas and workflow steps — and avoid long threads, video or heavy production. The interviews and event posts use the same short, first-person register.
Only text posts are in the reviewed material; no video, podcast or long-form writing was supplied, so conclusions about format apply to what was reviewed.
For brands planning a campaign
Pitches should match a short, example-first format with a clear operational hook. Sponsorship ideas that require long-form writing, video production or elaborate visuals do not match the demonstrated posting habit in this material.
Sushanth Raman's public posts point mainly to one workplace: Pallet, the supply-chain AI company whose agents and integrations he describes.
Read more
He says he is growing the Founder's Office there and writes about customer deployments, so Pallet is the clearest and most material connection in the reviewed material.
Beyond that, the brand names that appear in his posts fall into two groups: software platforms Pallet builds compatibility with — NetSuite, CargoWise, and SAP — where the posts show product integrations and customer use rather than any stated formal partnership; and outside companies he comments on or appears alongside, such as Morgan Stanley, which hosted a speaking event he attended, and DoorDash, whose acquisition of Instacart he analyzed.
The reviewed material contains no sponsorship, affiliate, or investment relationship.
Pallet
Employment
Raman writes as an insider at Pallet, the supply-chain AI company.
Read more
He says he is growing the Founder's Office, describes the team as a training ground for future founders, and refers to Pallet's customers, agents, and product work in the first person plural. The posts show employment at Pallet; they do not state an ownership stake, so no ownership claim is made here.
Raman says many of Pallet's customers run their businesses on NetSuite and that Pallet built compatibility with NetSuite workflows, pulling order, inventory, procurement, and fulfillment context into one place.
Read more
The post describes a product integration and invites readers to ask about it; it does not describe a formal partnership, so none is claimed.
Raman writes that many customers run freight forwarding and landside operations on CargoWise, and that Pallet built its own tooling to make CargoWise's XML schemas usable by its agents for building shipments, updating status, and quoting.
Read more
As with NetSuite, this is a described integration rather than a stated commercial partnership.
Raman describes a manufacturer managing inventory in SAP across a network of warehouses and 3PLs, and says Pallet's AI agents take information from partners via EDI, email, or spreadsheets and transact it directly in SAP or other connected systems.
Read more
SAP appears as a system Pallet connects to, not as a stated partner.
Raman says he spoke at Morgan Stanley Spark alongside founders from Cognition, Mercor, Baseten, EliseAI, and others, and thanks Sanjit Singh and Pat Dillon for the conversation.
Read more
The post shows he appeared at a Morgan Stanley event; it does not state a sponsorship or ongoing commercial arrangement.
Raman analyzed DoorDash's acquisition of Instacart as an example of AI making vertical integration more economically attractive across last-mile delivery and grocery fulfillment.
Read more
DoorDash is the subject of his market commentary, not a disclosed business relationship.
Sushanth Raman's posts are most useful if you read them as a working argument rather than a feed of announcements.
Read more
Start with the pieces that name a single enterprise system and a specific bottleneck — NetSuite, CargoWise or SAP — because that is where the reasoning is most concrete.
From there, the agent-governance post is the one that shifts from what automation can do to what it should be allowed to do, and it is the clearest statement of how Raman thinks about limits.
The DoorDash–Instacart post and the EDI post widen the frame to market structure and long-lived integration standards.
If you are following the company rather than the ideas, the hiring and event posts show how Raman talks about team building and the founder circuit.
Use the tags to pick a direction.
System integration and agent permissions are the deepest threads in the reviewed material; browser automation is the most niche and the most compliance-sensitive; company building is a separate register entirely.
The comparable creators below write in the same short, example-first style about AI meeting existing business software, and each one differs from Raman in a way that matters for what you would actually get by following them.
LaSov publishes the same kind of post Raman does: one named system, one process that eats a team's week, and an agent that removes it.
Read more
His examples run through NetSuite — matching bills of lading to sales orders, rebuilding a payroll accrual workbook, explaining revenue movement, automating cash application — and each post gives the before-and-after time cost, the level of operational detail Raman uses when writing about quoting, shipment building or inventory in SAP. Both treat specificity about the workflow as the point of the post rather than the AI itself.
LaSov stays inside finance and accounting processes in one system, while Raman ranges across operations — inventory, quoting, freight forwarding, partner data exchange — and across NetSuite, CargoWise, SAP and non-EDI sources. Raman also writes about agent permissions and network standards, which LaSov's reviewed posts do not address.
Chess writes about the same collision point between AI and existing enterprise software.
Read more
One post examines what NetSuite customers actually ask when they can query their ERP in plain language, moving from overdue invoices to cash shortages and margin pressure; another announces an AI finance skill built on authoritative NetSuite reports and finance guardrails. That concern — bringing AI to the systems a business already trusts, with controls attached — is the same ground Raman covers when he describes agents transacting in SAP and when he argues that each agent needs defined permissions.
Chess writes from the software vendor's product and launch angle, with an emphasis on what finance users ask and on controls inside financial reporting. Raman writes as an operator-founder about agents moving data across EDI, email and spreadsheets for logistics and supply-chain work, and about where long-standing integration standards create durable value.
Durrani makes a version of Raman's central claim from the brand side: growth stalls when the operating system underneath it breaks, not when the marketing does.
Read more
His posts trace fulfillment delays, packaging failures and inventory planning as the reasons D2C food brands lose repeat customers, and argue that founders should build reliable operational systems early. That is the same instinct Raman applies to supply-chain software — the workflow, not the model, is where the problem lives.
Durrani addresses consumer-brand founders and stays at the level of operational principles, without naming software or describing how systems connect. Raman names specific enterprise systems and explains the mechanics of integration, including where language models fall short and what tooling was built around them.
The only measured audience figure in the reviewed material is a LinkedIn following of 15,853, observed 8 October 2026.
Read more
Nothing supplied measures who that following is: there is no demographics export, survey, or engagement breakdown covering job function, seniority, geography, or company size.
So audience relevance has to be judged from the topics the posts reach rather than from a composition profile.
Those topics are unusually specific.
Sushanth Raman's posts address supply-chain and logistics operators directly — CargoWise and NetSuite workflows, EDI networks, SAP-connected inventory, 3PL and freight-forwarding operations, browser-based workflow automation — and also address founders and operators through hiring and team-building posts about Pallet's founder's office and revenue leadership.
For a collaboration on AI agents in supply chain operations, that topical match is the strongest signal available: the people who read these posts are already working on the exact problems the brief covers, and several posts end with a direct invitation to discuss automating those workflows.
Topical overlap is not the same as verified buyer reach.
A follower count and a run of on-topic posts do not show that the audience contains the specific job functions, seniority, or regions your campaign needs, and they do not show how any single post performed.
Before committing, ask whether an audience demographics export or survey exists for the LinkedIn account, and request recent results for posts on comparable topics, depth, and formats — for example the agent-governance and CargoWise pieces — so the decision rests on what similar content actually reached.
Sushanth Raman's reviewed LinkedIn posts contain no published sponsorship policy, media kit, rate card, or disclosed paid brand collaboration.
Read more
What they do show is a consistent direct-response habit: several posts close by asking readers to message directly to see Pallet's agents automate a workflow or to talk through a use case.
Those invitations are sales and product conversations, not documented partnership offerings, and they should not be read as a sponsorship format or as a rate card.
The posts also show the kind of work a brand would be buying access to: hands-on commentary on AI agents in supply-chain operations, integration specifics for systems such as NetSuite and CargoWise, and named role changes at Pallet.
A brand planning a collaboration should treat commercial terms as unresolved and confirm format, deliverables, pricing, exclusivity, and disclosure directly with Sushanth Raman before committing budget or building a timeline around an assumed package.
No official partnership or sponsorship inquiry page was verified in the reviewed material, so no inquiry link is shown here.
A B2B logistics software company asking for a content collaboration on AI agents in supply chain operations is asking about the center of what Raman already publishes.
Read more
The reviewed posts are built around agents operating inside the systems supply-chain teams already run: Pallet pulling fragmented order, inventory, procurement and fulfillment context together and keeping NetSuite current; automating shipment building, status updates and quoting inside CargoWise, including building custom tooling because CargoWise's XML schemas are hard for language models to handle reliably; and taking partner information through EDI, email or spreadsheets and transacting it in SAP.
Agent control is a second thread, with the argument that teams must define what each agent may do and the example of a quoting agent that can retrieve rates and generate a quote but not create an order.
Raman also writes about browser-based workflows and the CAPTCHA problem, and argues that EDI's durability makes it one of the strongest network effects of any standard.
That is the same ground the brief covers, at the same level of detail a buyer would want.
The format matches too.
Each product post is short plain text: one operational problem, a few paragraphs on why it is hard and what Pallet does, then a direct-message invitation to see the automation or discuss a use case.
A collaboration could take that shape without asking Raman to change register, and the operator vocabulary in the posts — 3PLs, order lifecycle, schemas, quoting — speaks to the people the brief is trying to reach.
The audience signal is topical rather than measured: the only figure supplied is a LinkedIn following of 15,853, with no demographics or engagement data, so fit rests on who the topics address rather than a proven buyer profile.
The unresolved parts are commercial, not editorial.
No sponsorship policy, media kit, rate card or disclosed paid collaboration appears in the reviewed posts, so format, deliverables, pricing, exclusivity and disclosure would have to be agreed directly.
The invitations Raman writes are product and sales conversations, not a published package, and should not be read as one.
Before committing budget, ask for performance on comparable posts — the agent-governance and CargoWise pieces are the closest analogues — and treat terms as an open question.
When to choose a specialist
A materially different brief that does not fit is a broad business-audience awareness campaign built on video: for example, a workplace-productivity or general enterprise software brand targeting a wide set of business professionals, wanting a video-led or high-volume publishing commitment.
Read more
The reviewed material contains only short text posts.
No video, podcast, transcript or long-form article was supplied from this account, so there is no evidence of the production capability that brief assumes.
The topics are also narrower than a general business audience: CargoWise freight forwarding, EDI trading-partner connections, SAP inventory management, NetSuite order and procurement workflows, and browser-based workflow automation.
Those subjects reach operators, not a broad professional readership, and the only measured audience figure — 15,853 LinkedIn followers — carries no composition data to show otherwise.
The commercial side is equally unproven for that brief.
Nothing in the reviewed posts shows a sponsorship format, a rate card, a media kit, or a disclosed paid partnership, so there is no basis to assume Raman accepts brand work, what a placement would cost, or how it would be disclosed.
The posts' direct-message invitations point to product conversations about automating a workflow, not to a partnership offer, and treating them as one would misread them.
This is a gap in evidence, not a judgment that the work would go badly; the point is that a campaign dependent on video reach, broad-audience demographics or a known commercial package has nothing in the reviewed material to stand on.
A brief closer to the operator topics, and in text format, is the better test of fit.
A worked campaign example
Take the good-fit brief — a logistics software company wanting a collaboration on AI agents in supply chain operations — and build the angle out of Raman's governance post, which is the least crowded idea in the reviewed material and the one that speaks to IT and operations buyers.
The post argues that agents are not employees but take actions across enterprise systems, names the actions that matter in supply chain (inventory, customer commitments, costs, downstream operations), and gives one worked example: a quoting agent that may retrieve rates and generate a quote but should not be able to create an order.
Pallet published a framework on scaling agent deployments, but the post only points to it in the comments, so the framework itself is not in the reviewed material and would need to be supplied or briefed before any piece leans on it.
The collaboration would open with a short text post or comment-led piece on the question the governance post raises — what should an agent be allowed to do, and who decides — and then carry one concrete workflow from the integration posts as the demonstration.
A NetSuite order-and-inventory scenario is the easiest to make legible to a non-technical reader: information arrives across emails, documents and other systems, the agent resolves exceptions and keeps the system current, and the piece states plainly where the agent stops and a person takes over.
The CargoWise post offers a second, more technical option if the audience is engineering-leaning, since it names a real obstacle — XML schemas that language models handle unreliably — and the custom tooling Pallet built in response.
Format should follow Raman's existing habit: short, plain text, one problem per post, closing with the same direct-message invitation used throughout the account.
Before drafting, settle four things.
Confirm whether the agent-permission framework can be shared or described, because the reviewed posts only reference it.
Confirm whether a customer example can be named or must be anonymized, since the EDI post rests on an unnamed global manufacturer.
Agree the commercial terms directly — format, deliverables, pricing, exclusivity and disclosure — because no sponsorship package is documented.
And set the baseline by asking for results on comparable posts, particularly the agent-governance and CargoWise pieces, so the collaboration is measured against what similar content already reached rather than against a follower count.
LaSov is the stronger choice when the collaboration is about agents doing finance and accounting work inside NetSuite rather than agents running supply-chain operations.
Read more
The reviewed posts are built around named processes and measured time savings: a five-agent build covering bills of lading matched to sales orders, payroll accruals for 1,000 employees across 18 subsidiaries and all 50 states, revenue analysis, order entry and cash application, with each agent tied to a specific weekly task and a before-and-after number.
The revenue-analysis post is the clearest single demonstration for a finance audience — a CFO at a $250M company spending three to four days a month digging through transactions, cut to about 30 minutes, and an argument that the value is not speed alone but surfacing variances the CFO had not known to look for.
A brief that wants finance-leader readers and a NetSuite accounting frame fits this material closely.
The reviewed LaSov posts stay inside finance and NetSuite automation.
There is no freight forwarding, EDI, 3PL, SAP or warehouse-operations content, and no supply-chain software product narrative.
A B2B logistics software company would have to reframe its brief as an ERP and finance agent story, which changes both the reader and the promise of the collaboration.
As with every alternative here, no sponsorship format, rate card or disclosed paid partnership appears in the reviewed posts, so commercial terms remain unproven.
Chess fits when the collaboration is about how people talk to business software and how finance analysis gets packaged for large language models, rather than about agents executing logistics work.
Read more
One reviewed post reports on Ask Oracle, a NetSuite feature through which customers have had thousands of conversations, and it lays out what those questions actually look like — overdue invoices and order status at first, then cash shortages, margin pressure, month-end anomalies and manufacturing forecasts — and the progression from what happened to why it happened to what deserves attention to what can be done about it.
The other announces a NetSuite Finance Analyst Skill for Claude and ChatGPT, with named guardrails around period status, materiality and source authority, and executive-ready narratives as the output.
A brief aimed at NetSuite customers, finance leaders or product teams working on AI in the enterprise would find a question-led frame here that has already been tested with real users.
Chess's reviewed material is an ERP vendor and AI product narrative.
It does not cover agent-run logistics workflows, freight forwarding, EDI or warehouse operations, so a supply-chain operations collaboration would have to move toward ERP conversation and finance skills, away from the operator vocabulary of Raman's posts.
No sponsorship format, rate card or disclosed paid partnership appears in the reviewed posts either, so commercial terms are open.
Khan Durrani is the better choice when the campaign is aimed at D2C food and consumer-brand founders and the argument is operational rather than marketing-led.
Read more
The two reviewed posts make the same case from different angles: growth breaks in fulfillment, packaging and inventory planning rather than in ad creative, and systems that hold at 20 orders a day fail at 200.
Repeat customers are framed as the real growth engine, with product, packaging, fulfillment and customer experience as a chain where one broken step loses the buyer.
The posts are short and plain, closing with a direct question to the reader — a format a consumer-brand collaboration could adopt without changing register.
The reviewed Khan Durrani posts address consumer-brand operators, not B2B logistics software buyers.
There is no AI agent, ERP, EDI or supply-chain software content, and no enterprise technology audience.
A logistics software company would be buying consumer-brand relevance that does not speak the operator language of its buyers or carry the agent and integration topics its brief covers.
No sponsorship format, rate card or disclosed paid partnership appears in the reviewed posts, so commercial terms are undocumented here as well.
This profile is based on full review of nine LinkedIn posts published from Sushanth Raman's account: product and integration posts about Pallet's supply-chain agents, a post on agent permissions and governance, one on browser-based workflows, market commentary on a delivery-industry acquisition, a post on EDI network effects, and two company-building announcements.
Each post was read in full; the supplied posts carry no publication dates, so the page makes no claims about how Raman's themes changed over time.
The similar-creator comparisons draw on retained posts from three other accounts reviewed in the same period.
No video, podcast, transcript, long-form article, media kit, rate card or sponsorship disclosure was available, and no engagement or audience-composition data was reviewed.
The only measured audience figure is a LinkedIn following of 15,853 observed on 8 October 2026, which is not treated as evidence of buyer reach.
The agent-permission framework referenced in Raman's posts is not in the reviewed material and remains an open item; identity leads such as earlier signature work also remain unverified.
Recent engagement
Across their last 8 LinkedIn posts, the median post got 115 reactions and 4 comments. Checked 2026-11-02.