Divyam Goel builds software to help freight forwarders manage the work that happens in email, including routing enquiries and surfacing operational knowledge for pricing and follow-up.
Most useful for reaching freight-forwarding operations and pricing staff with commentary on email workflow problems.
Best for
A freight-tech or B2B operations vendor targeting forwarders with email workflow tools.
Not for
Consumer brands or horizontal software seeking broad reach and paid sponsored posts.
Key takeaways
Pricing team lead managed eight people and over 100 enquiries a day.
Four hours a day went to figuring out who was handling what.
Enquiries sat 48 hours without a clear owner.
Check first: Confirm first-party audience data and sponsorship terms before planning spend.
Main ideas
Email is the coordination layer between business systems · Freight AI has to capture the knowledge behind a correct decision · The technical challenge is managing task state at inbox scale
Divyam Goel is a founder building software for freight forwarders, focused on work that starts in email and often gets lost between existing systems.
In their LinkedIn posts, they describe how unclear ownership, manual follow-up and operational knowledge buried in inboxes leave teams acting as human routers.
Follow their work for practical arguments about fixing that coordination gap—not simply adding another dashboard or automating a visible form.
Goel makes an abstract AI-and-operations problem tangible through specific working details.
They point to pricing knowledge buried in email threads—carrier exceptions, customer disputes and short rate-validity windows—and explain why automating a quote without surfacing that context can preserve the wrong assumptions.
They also describe how pricing, operations and customer decisions already move through email, framing the inbox as coordination infrastructure rather than a mere data source.
Their posts often open with a concrete scene or reported workload.
For the market-reaction post, ask for sources behind its claims about stock movements and sector losses.
The strongest reason to read their work is its focus on the overlooked handoffs and knowledge that determine whether freight workflows work.
Divyam Goel argues that freight-forwarding teams lose time and make mistakes in the email work between their established systems.
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In their view, better automation must account for who owns each task and for experienced staff’s operational knowledge—not just speed up forms or calculations.
Email is the coordination layer between business systems
CRMs, ERPs and transport management systems may be in place, but they do not necessarily show what is in an inbox or who should act.
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Goel describes staff becoming “human routers,” tracking replies and assigning work by hand. In a separate post, they quote ARC Advisory Group’s Jim Frazer on coordination across functions, then apply that idea to email as the coordination layer for forwarders.
Divyam Goel describes someone copying information from an email into a spreadsheet for the fortieth time in one day, then points to enquiries and follow-ups that can go unanswered because nobody owns them.
Freight AI has to capture the knowledge behind a correct decision
Goel argues that automating visible steps—forms, templates and calculations—can miss the experience that makes a quote accurate.
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That knowledge may be buried in email rather than recorded in a transport management system.
Goel describes an experienced pricing manager who knows which carriers to avoid in a particular season, which customer disputes destination charges and which lane has a shorter-than-usual rate validity. They argue that an AI tool must surface this context, not overlook it.
The technical challenge is managing task state at inbox scale
Goel frames the problem as more than reading or sorting email.
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Different task types, changing ownership and rules that vary by customer or lane all affect what should happen next.
In a hiring post, Goel describes classification across dozens of task types, state management across more than 60 inboxes, and routing that changes by customer, lane and time of day.
Freight companies face pressure to explain their technology strategy
Goel writes about AI as a market issue: public claims about freight AI can affect how investors view the sector, while uneven automation leaves many forwarders with questions to answer.
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One post says an AI freight-platform announcement was followed by declines in several logistics stocks and argues that forwarders now need a technology story. Another cites a 45% automation figure and contrasts it with routine email work that still goes unassigned.
This answers a newcomer’s first question: what does this person do, and what problem are they building against?
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It is a first-person account of moving from large-scale infrastructure engineering to freight forwarding software, without product jargon.
Divyam Goel describes seven years building systems that processed millions of transactions a day at Google Pay and Uber, then sitting with operations teams at logistics companies and law firms and watching someone copy the same information from an email into a spreadsheet for the fortieth time that day.
The argument is not that those teams lack tools — they have CRMs, ERPs and project management software — but that none of those systems know what is in the inbox or who is supposed to handle what, so staff spend half the day acting as human routers.
The post sets payments, ride matching and inventory alongside the unresolved question of "who's handling this email?" and ends by asking readers for the most surprisingly manual process they have seen.
For brands planning a campaign
A useful positioning anchor for briefs aimed at product or growth hires speaking to freight operations audiences: the message is about the gap between systems, not replacing them. Goel writes to buyers through concrete scenes rather than feature lists.
This is the clearest statement of the product thesis and the failure mode the work is aimed at.
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It also names the company, which the other posts mostly avoid.
The post opens on an experienced pricing manager who knows which carriers to avoid on the West Coast in the fourth quarter, which customer always disputes destination charges, and which lane has a buy-rate validity of two weeks instead of thirty days.
None of that sits in the transport management system; it lives in an inbox, in forwarded threads and in notes inside emails.
Goel argues that AI tools built for freight forwarding get shelved because they are made by people who have never worked a request for quote from start to finish.
They automate the visible parts — the form, the template, the calculation — and miss what makes a quote correct.
The post closes by naming TaskHarmony and asking forwarders who have been through a failed AI rollout to describe what broke.
For brands planning a campaign
Names the company and the wedge: capture operational knowledge before automating the workflow around it.
The closing question offers a ready-made way to open conversations with forwarders rather than pitch them.
Ask about TaskHarmony’s product status and customers before building a campaign around either.
It states the problem in terms a buyer can check against their own team, with a headcount, an enquiry volume and hours per day.
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This is the shortest route into the argument.
The post cites a figure of 45% of forwarders automating documentation, compliance and invoicing workflows, and argues the remaining gap is not CRM or ERP adoption — most forwarders have both — but what happens between those systems: the email that triggers a workflow but sits in an inbox, the follow-up nobody sent because two people each assumed the other had it, the enquiry that waited 48 hours because ownership was unclear.
It reports a conversation with a pricing team lead running eight people against more than 100 enquiries a day in shared inboxes, who estimated four hours a day just working out who was handling what.
The post concludes that the forwarders pulling ahead automated the boring middle before anything reached the CRM.
For brands planning a campaign
Supplies a headline statistic and a buyer persona — the pricing team lead with a small team and high enquiry volume — for outreach copy and landing-page testing. The 45% figure and four-hours estimate appear without a cited source; treat them as the author's claims, not verified market data.
This places the work inside an industry narrative instead of a product pitch, making it useful for readers interested in the strategic argument rather than the origin story.
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Goel quotes Jim Frazer of ARC Advisory Group: the next separation will not be between companies that have AI and those that do not, but between companies that can coordinate decisions across functions in real time.
The post splits supply chain technology into a first phase of embedding AI in forecasting, routing and visibility, and a second phase of coordination, mentioning agent-to-agent orchestration, decision integration and persistent memory.
The counter-argument is that for most freight forwarders the coordination layer is not an enterprise platform but email — where pricing asks operations to confirm capacity, where a customer changes a pickup date, and where documents wait on a shipper response.
The conclusion is that the second phase means treating the coordination layer already in place as infrastructure.
For brands planning a campaign
A third-party analyst quote gives a campaign an anchor for a thought-leadership piece without leading with the product. Ask for independent support before presenting this single borrowed quote as established consensus.
This is the most compact version of the origin story and the only post that spells out the engineering problem and who the company is hiring.
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It is the fastest read for someone assessing technical credibility.
The post reprises the arc told elsewhere — seven years at Google Pay and Uber, then watching operations teams copy shipper details from an email into a spreadsheet, coordinating across twelve time zones with CRMs, ERPs and TMS already in place.
It adds the shape of the technical problem: classification across dozens of workflow types, state management across more than 60 inboxes, and routing logic that changes by customer, lane and time of day.
It ends with an open call for engineers who want to ship AI to production daily rather than work on research or demos, and repeats the question about the most surprisingly manual process readers have seen.
For brands planning a campaign
The most directly relevant item for a brief assessing whether this person can hire and lead a founding-level product or engineering team; it also shows which technical claims the company is willing to make in public. Read it as a recruiting signal, not evidence of shipped product.
A different register from the operational posts: this is about market pressure and urgency.
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It is the right starting point for readers who want to know how Goel talks to executives rather than operators.
The post recounts Algorhythm Holdings, described as a karaoke machine company with a six-million-dollar market cap, announcing an AI freight platform, after which RXO fell 20%, CH Robinson 15% and Expeditors 13%, wiping what the post puts at 23 billion dollars from the sector.
It notes the platform's claims are questionable and that analysts say it loses money on every load, then contrasts CH Robinson's response about a decade of AI use, 30 tools and a 40% productivity boost with the figure that only 45% of forwarders have automated even basic documentation workflows.
The close is that the market now asks what your technology story is, and that forwarders without an answer face the problem now rather than next year.
For brands planning a campaign
A fear-of-missing-out frame suited to executive audiences and event or webinar topics; it shows Goel turning a news event into a question for buyers. Ask for sources before reusing the market moves, price movements and analyst commentary, which the post relays secondhand.
Format and audience engagement: posts that end with a question for operators4
The “boring middle”: email as the coordination layer3
Market pressure and the technology narrative in freight2
Founder background: payments and marketplace infrastructure at scale2
Institutional knowledge lives in inboxes—and that is why AI rollouts fail1
Problem framing in detail: classification, state and routing at inbox scale1
The “boring middle”: email as the coordination layer
A recurring argument across the posts is that work that determines whether a freight shipment moves correctly happens not inside CRMs, ERPs or TMS platforms, but in email, between them.
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Teams spend hours as “human routers”, forwarding messages, checking whether anyone replied and asking who owns a task.
One post quotes Jim Frazer of ARC Advisory Group arguing that the next competitive split will be between companies that can coordinate decisions across functions in real time.
It then makes the case that, for most forwarders, email is already that coordination layer.
Moves from a concrete operational scene—someone copying shipper details from an email into a spreadsheet for the 40th time that day—to a named industry source, then reframes the issue as infrastructure, not tooling.
For brands planning a campaign
For a company selling AI email workflow tools to forwarders, this is the most directly reusable topic. It frames the buying problem in the buyer’s own language—systems, ownership and follow-up—rather than as generic AI adoption, creating a natural hook for co-produced content or a founder-led webinar on the pre-CRM layer.
Institutional knowledge lives in inboxes—and that is why AI rollouts fail
One post builds its argument around an eight-year pricing manager who knows which West Coast carriers to avoid in Q4, which customer disputes destination charges and which lanes have two-week buy-rate validity.
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None of that knowledge is in the TMS; it sits in forwarded threads and email notes.
The post argues that freight AI tools get shelved because they automate visible tasks—forms, templates and calculations—while missing what makes a quote correct.
It says forwarders must first surface their institutional knowledge.
Anchors an abstract claim about tacit knowledge in a named-role composite example, then uses it to explain why competing products fail.
For brands planning a campaign
For a technology vendor, this offers a practitioner-credibility angle: content that starts with why previous AI rollouts failed, rather than a list of capabilities. It also signals an audience that will judge tools by whether they handle real quoting and pricing exceptions.
Market pressure and the technology narrative in freight
Two posts treat freight tech as a market story.
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One describes Algorhythm Holdings, a small company, announcing an AI freight platform as roughly $23 billion was wiped from logistics sector valuations over two days.
RXO, CH Robinson and Expeditors all fell, while analysts questioned the platform’s economics.
The other cites a figure that only 45% of forwarders have automated documentation, compliance and invoicing workflows, contrasting that with CH Robinson’s claim of a decade of AI use and 30 tools.
The through-line: the market now expects forwarders to tell a technology story, not just rely on relationships.
These two posts offer outward-facing market commentary rather than the first-person operational scenes typical of the others.
For brands planning a campaign
Useful for commentary-led placements around market events or survey data, where they can react quickly to freight-tech news. A vendor should expect the conversation to focus on competitive pressure and perceived urgency, rather than workflow specifics.
Founder background: payments and marketplace infrastructure at scale
Two posts open with the same credential: seven years building systems at Google Pay and Uber that processed millions of transactions daily, covering scalable infrastructure and automation.
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That background is then used as the contrast that makes the email problem stand out — payments, ride matching and inventory were solved at scale, but "who's handling this email?" was not.
For brands planning a campaign
For a brief to hire a founder-level product or growth leader, this is the core positioning evidence: large-scale marketplace and payments engineering experience applied to a deliberately unglamorous operational problem. It supports a systems-thinking narrative rather than one based on domain-native freight experience.
Problem framing in detail: classification, state and routing at inbox scale
One post details what solving the problem requires: classification across dozens of workflow types, state management across more than 60 inboxes, and routing logic that changes by customer, lane and time of day.
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It is also a hiring post, seeking engineers who want to ship AI to production daily rather than work on research or demos.
For brands planning a campaign
Signals a technically specific understanding of the workflow problem and active recruiting. A vendor or employer can engage at the engineering level—architecture, evaluation and production deployment—rather than the marketing level.
Format and audience engagement: posts that end with a question for operators
The posts follow a recognisable structure: a short claim, a concrete operational scene, a contrast or statistic, then a direct question inviting replies.
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One asks about the most surprisingly manual process inside a tech-savvy company; another asks where a team loses the most time; a third asks forwarders with failed AI rollouts what broke.
The stated audience is pricing and operations people at freight forwarders.
One cited conversation is with a pricing team lead running eight people and handling more than 100 enquiries a day.
Uses open questions as a distribution and research tool, alongside hashtags for freight, B2B operations and workflow automation communities.
The market-commentary posts instead close with broader questions to the sector, rather than questions aimed at a specific operator role.
For brands planning a campaign
Frame collaborations as invitations for operators to describe their own processes, not as product announcements. Comment threads are likely to yield useful practitioner detail for a brand.
Divyam Goel's public posts document two kinds of commercial relationships: the freight-forwarding software business TaskHarmony, which they say they are building, and earlier engineering work at Google Pay and Uber, described as a seven-year block building high-volume systems.
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Goel's posts discuss RXO, C.H. Robinson, Expeditors, Algorhythm Holdings and ARC Advisory Group as industry context, not as relationships with them.
TaskHarmony
Employment
Goel describes building TaskHarmony, software that reads freight forwarders' email to capture quoting and operational knowledge.
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In one post Goel says the company is working on "the unglamorous, inbox-deep, institutional knowledge problem," and in another asks engineers to apply to "ship AI to production daily." The posts establish that Goel builds this product but do not state a title, an ownership stake, or whether the company is Goel's own.
Goel says they spent seven years at Google Pay building payment systems that processed millions of transactions daily, before moving into freight-forwarding software.
Divyam Goel writes about the unglamorous middle of freight forwarding: the emails, ownership questions and follow-ups that determine whether a shipment moves, sitting between the CRM, ERP and TMS rather than inside them.
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Start with posts grounded in specific operational scenes — a pricing manager copying the same shipper details into a spreadsheet for the fortieth time that day, or an eight-year pricing manager whose carrier and lane knowledge exists only in forwarded email threads — then turn to market commentary on AI announcements and automation statistics.
Across both, the recurring claim is that automation pays off only when it captures task ownership and what experienced staff already know.
The posts are short, first-person and usually end with a direct question for freight operations and pricing people.
For more on AI adoption in logistics, see Alain Borri, Marcelo Fujimoto and Bryan Lacaillade, compared below.
Freight forwardingAI in logisticsWorkflow automationEmail-based operationsInstitutional knowledgeB2B operationsSupply chainFreight technology product buildingFirst-person founder postsPosts that end with a questionFreight forwarding operations and pricing teams
Marcelo Fujimoto
Both write about the same freight-tech news and reach a similar conclusion: freight forwarding retains value after automation because that value lies in exception handling — customs delays, documentation problems and carrier substitutions — rather than matching freight to capacity.
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Both cover the karaoke-company AI platform and the selloff that followed, treating them as evidence of which parts of the chain are defensible.
Fujimoto writes for investors and board members, framing the issue as market pricing of risk and explaining its implications for capital markets. Goel writes from inside the work, describing inbox ownership, operational status and what a tool needs to get right.
Both analyse how AI affects freight-forwarding software and ask who captures the value.
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Borri asks whether productivity gains stay in a forwarder's margin or pass to customers as lower prices; Goel raises the same question about technology stories and competitive pressure on forwarders.
Borri writes as a long-tenured industry executive and commentator, addresses shippers, TMS vendors and supply-chain leaders, and points readers to a newsletter. Goel writes as an email-product builder, focuses on the operational details of quoting and inbox routing, and recruits engineers.
Both build AI automation for freight forwarders and offer practitioner advice on why implementation fails, rather than lists of capabilities.
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Lacaillade's rule is to fix and standardise a process before automating it; Goel's is to surface institutional knowledge first. Both warn that automating only the visible parts produces a faster version of the same mistakes.
Lacaillade offers short, checklist-style tips drawn from conference and webinar settings. Goel devotes more of each post to the underlying problem — classification, ownership and routing at inbox scale — and mixes in first-person discovery scenes and hiring posts.
Divyam Goel’s posts address freight-forwarding and logistics operations and pricing staff—the roles a company selling AI email workflow tools to forwarders needs to reach.
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The LinkedIn account had roughly 23,600 followers as of 7 October 2026.
The posts focus on specific workflow problems: a pricing team of eight fielding 100-plus enquiries a day, confusion over ownership in shared inboxes, carrier and lane knowledge that never reaches the TMS, and quotes built from inbox threads rather than templates.
This supports LinkedIn as a channel for reaching that professional community, but does not establish how many followers are in the company’s target markets, work at forwarders of the target size or influence tooling decisions.
The posts provide no platform demographics or audience survey covering location, age, gender, employer, job function or seniority; the follower count is the only measured audience figure available.
Before committing, ask them for first-party audience data—a LinkedIn demographics breakdown covering location, seniority and company size, or documented audience survey results—and recent results for comparable posts on freight-forwarding email and workflow topics.
Divyam Goel's posts do not say they offer sponsorship, affiliate, or paid-promotion formats.
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The content falls into two categories: commentary on freight-forwarding operations—the cost of manual email routing, what breaks AI rollouts in forwarding, and how the market is repricing logistics technology—and promotion of TaskHarmony, the software business they are building to read and route that email for forwarders.
One post also recruits engineers for the business.
These are company and hiring messages, not brand placements; none describes a media kit, rate card, audience guarantee, content package, or terms for promoting a third party.
For a campaign aimed at recruiting a founder-level product or growth leader for AI email process tools built for forwarders, Goel's public activity points to building and staffing their own company, not selling placements.
A brand seeking a commercial arrangement would need to contact them directly and ask whether they accept partnerships and what terms apply.
The posts identify no official partnership inquiry route, so no link is offered.
Strong fit for the product half of the role. Invite them to a final-stage conversation and get specific answers to the gaps below before making an offer.
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Brief
A freight-forwarding technology company wants a founder-level product or growth leader to build AI email workflow tools for forwarders.
Reasons
The public writing speaks to the exact buyers this role must serve: pricing and operations teams at forwarders working out of shared inboxes. Posts describe a pricing team lead managing eight people and more than 100 enquiries a day, who estimated that four hours a day went to figuring out who was handling what. They also describe enquiries left 48 hours without a clear owner and follow-ups missed because two people each assumed the other had sent them. That is the problem the hiring company sells against, in buyers’ own words.
The technical challenge is spelled out: classification across dozens of workflow types, state management across more than 60 inboxes, and routing logic that changes by customer, lane and time of day. Someone who frames the work that precisely can talk to engineers on day one, rather than simply relay product requirements.
Goel says they spent seven years building systems at Google Pay and Uber that processed millions of transactions daily. The posts repeat that account at least twice, but do not independently confirm it. Treat it as their public framing, not established fact.
Goel is recruiting engineers to ship AI to production daily, rather than work on research or demos. That signals intent to hire and lead a founding team, but does not establish that they have managed a team or shipped a product.
The weakness is the growth half. The posts show no channel ownership, acquisition numbers, pricing experiments or demand-generation results. If the role leans toward go-to-market, ask for that evidence directly rather than inferring it from the writing.
Verify
Ask what TaskHarmony has shipped and how many forwarders use it. Only two posts mention the company, and neither describes live customers or product status.
Ask about team size and reporting lines they have actually managed, rather than roles they have posted.
Ask for first-party demand and audience data: LinkedIn demographics by location, seniority and company size, plus results for posts on freight email and workflow topics comparable to the company’s campaign.
Ask for independent confirmation of their Google Pay and Uber tenure and the market figures they relay in the news-style posts.
When to choose a specialist
Decline the paid placement. If the brand wants a relationship, start a direct conversation rather than booking from a rate card.
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Brief
A consumer brand or horizontal software company wants paid sponsored posts and a media placement from this creator, aimed at a broad professional audience and measured on reach and sign-ups.
Reasons
The audience is narrow and specific: the writing addresses freight-forwarding operations, pricing and documentation staff. Nothing reviewed demonstrates reach into a general professional or consumer audience, so a brief built on broad reach lacks supporting evidence.
The posts offer no sponsorship product: no media kit, rate card, audience guarantee, package terms or disclosed third-party brand partners. The only commercial content promotes Goel’s own business, TaskHarmony, or advertises an engineering role. Those are company and hiring messages, not placements.
The editorial agenda would work against the placement. The content advances a product thesis for an AI email workflow tool for forwarders. A brand selling into the same category would pay for posts that highlight competitors’ failure modes and its own product.
The only measured reach figure is roughly 23,600 LinkedIn followers as of 7 October 2026. There are no demographics or survey data on location, seniority, employer or buying power, so reach among a target audience cannot be assessed.
No official partnership inquiry route is documented, so there is no established channel for buying.
Partial fit
A freight-tech or B2B operations vendor targeting forwarders has a clear audience match, but the size and terms of the opportunity are unknown: the sponsorship offer, terms and audience composition are undocumented. Treat an approach as a bespoke conversation about what Goel would accept, and confirm the offer in writing before planning spend.
A worked campaign example
Angle
Run the routing tax audit: turn the operational pain Goel already writes about into a measured benchmark that brings forwarders into the conversation and gives the hiring company a public position in the category.
Brief
The hiring need is the same: a founder-level product or growth leader to build AI email workflow tools for forwarders.
Notes
The news-style post relays market-movement figures—the RXO, CH Robinson and Expeditors declines, and the $23 billion figure—secondhand, without a source. Verify them before reusing them in a campaign. As a hiring pitch, the audit also works as a sample of the job: it tests whether the candidate can turn an operator complaint into a measured programme.
Steps
Treat the four-hours-a-day estimate Goel attributes to a pricing team lead as a hypothesis, not a benchmark. It comes from one reported conversation and is not independently sourced, so test it rather than quote it as fact.
Recruit 20 to 30 forwarders to keep a one-day time log on shared inboxes: hours spent deciding who handles what, enquiries that went more than 24 hours without an owner, and follow-ups missed because two people each assumed the other had sent them. The existing posts trade in this operational detail, which the audience will recognize.
Publish two outputs from the data: a short benchmark report on the routing tax and an interview series with forwarders who have experienced a failed AI rollout, describing what broke. Goel’s direct question to forwarders is the strongest existing close and a natural way to open real conversations instead of pitching.
Keep technical claims at the level the posts reach: classification across workflow types, state across 60-plus inboxes, and routing that changes by customer, lane and time of day. This audience judges tools by whether they handle quoting and pricing exceptions, not by capability lists.
Lead outreach copy with the process problem, not the product. Open with a specific request: which handoff broke, and what did the previous tool miss?
Measure three things: owner-confusion hours before and after, enquiries with no owner past 24 hours, and the share of forwarders automating documentation, compliance and invoicing. Goel cites 45% on the last one without a source in the post; publish your own survey number rather than repeating that figure.
Choose Fujimoto over Goel for trade-content infrastructure—duty calculation, HTS classification, AD/CVD intelligence, document management and developer-facing APIs—rather than shared-inbox AI.
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The published work describes a live US import duty calculator and a modular, API-first engine strategy at Tandom, with HTS and AD/CVD intelligence described as coming next. A company that needs a founder-level leader to ship interoperable trade-data components and win forwarder, broker and shipper adoption through APIs should talk to Fujimoto first.
Divyam Goel's posts focus on trade-compliance content engines and exception management, not email triage, shared-inbox routing or the operator-level routing tax they write about.
A role centered on AI email workflows for forwarders would trade away that specific problem framing.
Before a campaign aimed at those areas, ask for evidence of team-building, growth metrics and hiring; the duty-calculator post says the product launched free with feedback requested, so commercial traction remains unproven.
Choose Borri for market strategy, category narrative and executive credibility rather than day-to-day product build.
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Borri writes as CEO of Sightness in Singapore and frames the AI question in freight forwarding as a value-capture problem: whether productivity gains stay in the forwarder's margin or pass to customers, and whether TMS survives as a standalone category as AI agents and APIs make transport management modular. A company that needs a founder-level leader to position an AI email workflow product against incumbents, talk to shippers and TMS vendors, and shape a public argument about where value moves should consider Borri.
The tradeoff is distance from hands-on AI email workflow construction.
The posts focus on industry structure and the TMS market; they do not describe building classification, state management or inbox routing, or recruiting a team or shipping a product in that space.
Borri also promotes a newsletter and brings a 30-year analyst-style vantage point, which can strengthen a market narrative.
Ask for evidence of operating a startup in this product category.
Choose Lacaillade when the company needs someone who has run a freight-forwarding software business through competitive pressure and can advise forwarding leaders on fixing broken processes before automating them.
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The published work covers resolving an 18-month legal dispute with Flexport while continuing to build Freightmate, and a practical AI automation message delivered to forwarding leaders through a Freight & Trade Alliance webinar: standardize the process first, then automate. That is a better match when the role requires credibility with forwarder operations leaders, change management and resilience in a contested market.
The posts offer little on AI email workflow mechanics or product and growth metrics.
Lacaillade's posts are company and process-advisory messages, not a public record of building classification, routing or inbox-state systems for forwarders.
The legal dispute is resolved in their telling, but the public post remains a company statement.
Ask for specific product and growth outcomes the candidate would need to own, separate from Freightmate's account.
Six full LinkedIn posts and Goel's public LinkedIn profile show about 23,600 followers on 7 October 2026.
The posts cover freight forwarding operations, AI adoption in logistics, workflow automation and hiring for a freight software team.
They often open with a working scene or a reported workload and close with a question to freight operations and pricing staff.
The posts here have no publication dates, so this page does not claim a sequence or a change over time.
The posts here include no videos, transcripts, newsletters, company materials or third-party coverage, and mention no signature book, show or earlier career material beyond what they mention.
Statements in the posts about earlier work at Google Pay and Uber, about stock movements and market reaction, about automation rates among forwarders, and about day-to-day time losses are claims made in the posts, not independently verified.
No sponsorship offer, media kit, rate card, audience demographics or client relationship is published in the posts or profile here; that absence means none is documented here, not that none exists.
This page helps readers understand who Goel is and what they work on, independently of any hiring decision.
