Manu Vanderveeren writes about building Spott, an AI-native recruitment platform for agencies. Their posts discuss using AI to improve recruiting workflows, growing the business through inbound and outbound sales, and raising funding.
They are most useful for reaching recruitment agency owners and operators with founder-led posts about AI-native recruiting workflows.
Best for
This fits a B2B SaaS company selling AI agent tools to recruitment agencies.
Not for
Not for enterprise HR platforms selling to in-house corporate talent teams or brands needing a documented sponsorship package.
Key takeaways
A customer-built agent found about 250 candidate matches versus 100 through manual matching.
Spott is adding outbound sales because inbound demand cannot be turned up on command.
Fundraising reflection covers 40 hours of investor meetings and 21m raised.
Check first: Confirm sponsorship availability and ask for recent post-level results and audience survey data.
Main ideas
Agencies can grow without adding recruiters if knowledge stops living in one person's head · Back product claims with a named customer's before-and-after numbers · Consolidation beats accumulation: one workspace instead of a stack of tools
Manu Vanderveeren writes about building Spott, an AI-native recruitment platform for recruitment agencies. The posts explain Spott’s approach to recruiting workflows and share founder perspectives on sales strategy and fundraising.
The posts pair a clear business argument with operational detail.
In Spott’s product thesis, the problem is knowledge trapped in individual recruiters’ heads and time lost to administration.
The proposed “agency brain” handles work up to the decision, which stays with people.
A customer case study makes the idea tangible: the post says a customer-built agent found about 250 candidate matches, compared with 100 through manual matching.
They also explain why Spott is adding outbound sales when most customers have come inbound: inbound demand cannot be turned up on command, and some prospects are not ready to buy.
In a fundraising reflection, they weigh the value of sharpening a company’s story against the effort of fundraising.
This mix offers useful perspective for readers weighing AI-enabled recruiting operations or the practical choices involved in building a business.
Manu Vanderveeren writes as the founder of Spott, an AI-native recruitment platform for agencies.
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Across the posts, they argue about how agencies grow and how to sell that growth.
The through-line: agencies are limited less by demand than by where knowledge sits and how much admin their best people absorb.
The fix is to put a firm's knowledge and network in one place and let software do the work up to the decision, leaving that decision to the recruiter.
Three supporting positions recur: prove product claims with a named customer's before-and-after numbers, consolidate tools rather than add them, and build outbound even when inbound has carried the company so far.
The fundraising post shows another habit: reviewing company milestones, including what went badly, in the same list format used for product arguments.
Agencies can grow without adding recruiters if knowledge stops living in one person's head
Manu Vanderveeren argues that an agency's growth is capped not by demand but by headcount, for two concrete reasons: its strongest recruiter keeps the firm's knowledge in their head rather than in the system, and its most careful recruiter loses hours each week updating the CRM.
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Either way, the agency pays — in knowledge it cannot reuse or hours it cannot bill.
Spott's answer is what they call an agency brain: one place for the firm's knowledge and network that flags what deserves attention and does the work up to the decision, which stays with the recruiter.
The pitch is framed around an agency outcome — doubling placements with the same team — rather than a set of AI features.
The post announcing the $21M raise frames the problem as a rule to break — that agencies scale only by adding headcount — and positions the product against two costs: knowledge stuck in one person's head and hours lost to admin.
Back product claims with a named customer's before-and-after numbers
Manu Vanderveeren makes the case through one agency, the tools it replaced, and the matching volume it gained by building its own agent on Spott's API and MCP server.
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The agent runs every morning, matching every active candidate against every open role. The claimed payoff is not fewer clicks but more placements from the same number of conversations; credibility comes from the customer's account, not the vendor's description.
Adrien Moulias, co-founder of Flaire, a 15-recruiter Paris agency that has placed 400+ engineers at frontier AI companies, produced these figures after migrating: one workspace replaced a CRM, an ATS, Notion, and a notetaker; manual matching found about 100 options for 100 candidates in search, while the agent found roughly 250.
Moulias says that with the same number of conversations, they can make twice as many placements.
Manu Vanderveeren notes that Moulias had built and sold an ATS himself, so he judges tools by the details.
Consolidation beats accumulation: one workspace instead of a stack of tools
A recurring priority is reducing the number of places agency work gets fragmented.
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Rather than adding another point tool to a CRM, an ATS, a notes app, and a notetaker, Manu Vanderveeren presents Spott as a single home for a firm's knowledge and network. The same customer story supports both the consolidation and matching claims, framing consolidation as a prerequisite for useful agent work: scattered data cannot be matched automatically.
The Flaire migration replaced four tools with one workspace. Moulias's judgment is presented as informed: he had previously built and sold an ATS himself.
Inbound is the right start, but outbound has to be built anyway
Manu Vanderveeren says more than 90% of Spott's customers arrived through inbound, then argues for adding outbound anyway.
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Three reasons underpin the case: the best-fit buyers may not be in the market today but will purchase when a contract expires, so outbound can reach them first; inbound cannot be turned up on demand because it grows only as fast as content, SEO, and word of mouth; and sales skill is a muscle worth building over a career, not just a channel to switch on. The post presents a live strategic decision, not a settled doctrine.
The outbound post ends with an open hiring call for BDRs and outbound account executives, tying the argument to a change in how the company staffs itself.
A company built on inbound traffic publicly starts developing outbound capability, explaining why the earlier strategy no longer covers the whole market.
Fundraising deserves the same candid review as a product decision
Manu Vanderveeren treats the $21M raise less as a milestone than as an experience to assess, listing what worked and what did not.
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The parts worth keeping: venture firms make ten-year bets, so meetings turn philosophical about where recruitment agencies will be in a decade; and shaping the story with existing investors clarified the company's vision — a process they describe as 50% numbers and 50% storytelling.
The part worth hating is the evenings.
The post reveals the voice behind the product posts: specific, first-person, and willing to include a rejection.
One firm passed on the round because it believes only hardware and resources will matter in ten years — an objection Manu Vanderveeren reports plainly after 40 hours of investor meetings.
Answers a newcomer’s first questions: What does Spott do, and what problem does it solve?
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This is the company thesis in its shortest form, so it is the right place to start.
Manu argues that recruitment agencies are capped by headcount for two reasons: knowledge sits in one recruiter's head, and careful recruiters lose hours keeping the CRM updated.
The agency pays either way, in lost knowledge or lost hours.
Spott raised $21M to bring a firm's knowledge and network into one place—the agency brain—and surface what deserves attention while leaving the final decision to the recruiter.
The pitch is to double placements with the same team, not add AI features.
For brands planning a campaign
Lead a collaboration or advisory angle with the operational pain—knowledge silos and admin hours—not the AI label. That is how the founder sells the product, so content built around it will sound native rather than borrowed.
Shows whether the product claim holds up in practice.
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It is the strongest evidence in the set: the numbers come from a customer, not the vendor.
This post profiles Adrien Moulias, co-founder of Flaire, a 15-recruiter Paris firm that has placed 400+ engineers at frontier AI companies such as Mistral.
After moving to Spott, Moulias says one workspace replaced four tools (CRM, ATS, Notion and a notetaker).
The team also built an agent on Spott's API and MCP server that matches every active candidate against every open role each morning.
Manual matching turned up 100 options for 100 candidates in search; the agent found around 250.
The headline quote: with the same number of conversations, the firm can make twice as many placements.
Manu notes that Moulias had built and sold an ATS himself, so he judges tools by the details.
For brands planning a campaign
For AI-native recruiting content, ask for named-customer before-and-after numbers, not feature walkthroughs. This post builds credibility with a specific firm, tool count and matching figure.
Shows how the company grows and how Manu reasons through a change of mind.
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Useful for anyone assessing whether they can speak credibly about go-to-market, not just product.
Manu says more than 90% of Spott's customers arrived inbound and that the company is now doubling down on outbound.
The reasoning has three parts: the best-fit customers are not in the market today and will buy when a contract expires, so outbound reaches them first; inbound cannot be turned up on demand because it grows only as fast as content, SEO and word of mouth; and sales skill is a muscle that has to be built.
The post ends with an open hiring call for BDRs and outbound account executives.
For brands planning a campaign
A founder narrates a live strategic shift, making this a good fit for a build-in-public collaboration. The hiring call is a secondary angle: content for recruiters evaluating AI tools could also serve as employer-brand material, but the two audiences should not be blended in one piece.
Shows what the funding round involved and how Manu writes when not selling.
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It is the most personal piece in the set and offers a clear example of the voice a longer collaboration would need to match.
Manu highlights three things about fundraising: the philosophical depth of meetings, as VC firms make a ten-year bet and ask where recruitment agencies will be in a decade; shaping the story with existing investors, described as 50% numbers and 50% storytelling; and the moment the round closes.
They say they hated everything else.
One detail stands out: a VC firm passed because it believes only hardware and resources will matter in ten years.
The post closes by describing Spott as an AI-native recruitment platform for agencies, powering 500+ agencies worldwide, from executive search to temp staffing.
For brands planning a campaign
Founder-voice candor, including what did not go well, runs through this set. Sanitized success-story copy would feel off-brand; a candid retrospective is the safer fit.
Matching candidates to roles with an agent, not a manual sweep
For Flaire, a 15-recruiter Paris agency that has placed 400+ engineers at frontier AI companies, Spott's API and MCP server power an agent that checks every active candidate against every open role each morning.
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The post reports that a manual match across 100 candidates surfaced 100 options, while the agent surfaced about 250, and frames the payoff as twice the placements from the same number of conversations.
For brands planning a campaign
A useful story for brands selling API-first or integration-heavy recruiting tools. A collaboration could show the agent at work, with the number of matched options as the concrete claim to verify.
The migration replaced a CRM, an ATS, Notion, and a notetaker with one workspace.
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The buyer is described as detail-oriented because they had built and sold an ATS before. The pitch is consolidation: fewer context switches and one home for an agency's knowledge.
For brands planning a campaign
Fits a migration or comparison piece, such as a checklist for moving agency data out of a CRM/ATS/notes stack. Partner brands should bring evidence on data migration and day-to-day usability, not just feature lists.
A post argues agencies should not have to grow headcount to grow revenue.
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It frames the problem as knowledge and admin: the best recruiter never updates the CRM, while the most careful one loses hours a week doing it, so the agency pays either way.
Spott is presented as an 'agency brain' that holds the firm's knowledge and network, flags what deserves attention, and does the work up to the decision, which stays with the recruiter.
The stated goal is doubling placements with the same team.
A problem-then-product argument: name the two costs the agency already pays, then position the product against both.
For brands planning a campaign
Usable for ROI or productivity storytelling where the metric is placements per recruiter. Brands reusing the angle should cite a customer-verified number rather than repeat the claim.
The post reports that more than 90% of Spott's customers arrived inbound, then argues for adding outbound anyway.
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The best-fit buyers are not in-market today and will buy when a contract expires; inbound cannot be turned up on demand because it grows only as fast as content, SEO, and word of mouth; and outbound is a career muscle worth building. The post closes with open BDR and outbound AE roles.
For brands planning a campaign
Good raw material for a pipeline-mix conversation aimed at B2B SaaS founders. A sales-engagement or data brand could co-create a piece on when to add outbound, featuring a founder willing to describe the shift from an inbound-led start.
The post describes a $21M raise after 40 hours of investor meetings as a list of likes and dislikes.
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On the positive side, VC firms make 10-year bets, so conversations turn philosophical, and shaping the story with existing investors clarified the company's own vision, which is called 50% numbers and 50% storytelling.
The disliked part is the evenings.
One firm passed because it expected hardware and resources to matter most in a decade.
A disclosure-style list post built from specific moments, including a rejection reason, rather than a milestone announcement.
The post also restates the product description and links to the company site, so the fundraising narrative doubles as a company pitch.
For brands planning a campaign
Suited to founder-audience content, such as a roundtable or interview on fundraising mechanics, where candid detail matters more than the size of the round.
The buyer is a recruitment agency, in several shapes
The customer is consistently a recruitment agency rather than an in-house talent team: Spott is described as powering 500+ agencies worldwide, from executive search to temp staffing, and the featured case is a Paris agency placing engineers with AI companies.
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For brands planning a campaign
Helps a brand decide whether an offer fits agency-scale recruiting. Campaign examples can be segmented by agency type, such as executive search versus temp staffing, instead of treating them as one audience.
Manu Vanderveeren writes as part of Spott, the AI-native recruitment platform, and promotes a Spott customer case study with Flaire.
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Ask for confirmation of any other commercial partnerships; none are named in the posts.
Spott
Employment
Manu writes as part of Spott, using “we” and “our” for the AI-native recruitment platform in posts about its $21M raise, outbound sales, hiring and product work.
Manu describes the Paris recruitment firm migrating from four tools to Spott, building an agent on Spott’s API/MCP server, and reporting 15 recruiters and 400+ engineers placed. The connection is to Spott, not a separate Manu brand deal.
Manu Vanderveeren's LinkedIn text posts make the case for how recruitment agencies grow and how software for them should be sold.
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Start with the customer case study on Flaire: one workspace replaces a CRM, an ATS, Notion, and a notetaker, alongside an agent built on Spott's API that matches every active candidate against every open role.
Then read the agency-brain post, which frames the problem as agencies paying either through knowledge stuck in one head or hours lost to CRM admin.
The outbound post shows the shift in thinking most clearly: a company that says more than 90% of its customers arrived inbound argues for building outbound anyway.
The fundraising retrospective is the most personal of the four and the best sample of the writing voice, listing what worked and what did not after 40 hours of investor meetings.
Three creators in the same collection cover adjacent ground with different emphases: Fabian Herhold on the move from agency to software and tactical recruiter outreach, Kalpesh Baxi on AI taking over tasks rather than jobs, and Oliver Feakins on travel nursing demand and recruiter capacity.
Fabian Herhold also writes as a founder who ran a recruitment agency and then built software for recruiters, and covers the same build-in-public ground: what changed when the work moved from client projects to a product, which parts of a recruiter's day are worth automating, and how an agency gets more done without adding people.
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The Hyreflow beta post reports activation and onboarding numbers the way Manu Vanderveeren reports matching volume, so both use their own product's metrics as the argument.
Herhold writes from the middle of the transition, with practical posts such as five subject-line patterns for recruitment outreach and a note on sending setup.
Manu Vanderveeren mostly makes the strategic case from a company perspective, using a named customer's before-and-after figures rather than first-person operating detail.
Herhold offers more practical guidance for readers planning their own outreach; they offer more on why agencies should consolidate their tools and data.
Kalpesh Baxi makes the same central argument as Manu Vanderveeren: break down a recruitment role, separate repetitive work from tasks that need a person, and move the former to an agent while leaving judgment to the human.
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Both write directly to recruitment business owners, not in-house hiring teams, and treat tool sprawl and an untrusted CRM as real costs of running an agency.
Baxi frames the argument through tasks versus jobs and through a network of vetted specialists who can be brought in for a specific problem, offering no product numbers and no named customer; Manu Vanderveeren ties every claim to a product outcome, such as an agent surfacing roughly 250 matching options where a manual sweep found about 100. Baxi is the better follow for a diagnostic conversation about what to fix in an agency; Manu Vanderveeren is the better follow for how one AI recruiting platform describes its results.
Oliver Feakins writes the closest structural match to Manu Vanderveeren's AI-and-work posts: a walk through the activities a recruiter currently spends the day on, an argument that software should narrow the day to the conversations that matter, and a closing reminder that humans still create the trust and close the commitment.
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Both also think in terms of capacity rather than headcount, asking what a team can produce once the routine work moves off its plate.
Feakins works inside travel nursing, so the posts and the stakes are healthcare staffing: an index of open travel nurse jobs, marketing as fuel, recruiting as the engine, technology as the transmission.
Manu Vanderveeren works at the software layer for agencies generally, across executive search and temp staffing, and backs the argument with SaaS growth and fundraising, not with a market-demand index.
Follow Feakins for staffing-market timing in healthcare; follow Vanderveeren for how an AI platform for agencies makes its case.
Manu Vanderveeren is a fit for campaigns aimed at recruitment-agency owners and operators, rather than broad consumer or general SaaS audiences.
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The only measured audience figure available is their LinkedIn following, about 16,000 people as of 8 October 2026.
The four posts show who the content is written for: agency-side recruiters and founders.
Each addresses them directly, using their vocabulary — placements, candidates in search, CRM and ATS hygiene, contract expiry, BDRs and outbound AEs.
One post reports a customer's operational detail from a 15-recruiter Paris agency placing engineers at frontier AI companies.
For a B2B SaaS company selling AI-native recruiting workflows, that topic alignment is useful: the following is being spoken to about subjects a buyer would sponsor.
The tradeoff is a modest, single-channel LinkedIn footprint, with no published engagement, video or sponsored-post performance data to indicate how a paid placement would travel.
Before committing budget, ask for recent post-level results — impressions, engagement and any sponsored or partner placements — on comparable topics, depth and formats from the last few months.
Also ask whether they have audience survey results covering seniority, function, company size and geography; the posts do not publish platform demographics or survey results, so the following's geography, age, seniority, employer type and job function are unverified.
LinkedIn followers
16065
LinkedIn profile account snapshot for Manu Vanderveeren, taken 8 October 2026 · 2026-10-08
The four reviewed LinkedIn posts from Manu Vanderveeren describe no sponsorship or partnership offering.
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They include no published rate card, media kit, format list, or partnership inquiry route.
The posts are company updates about Spott—its recruiting product, a customer migration, the move into outbound sales, and a $21M raise—written to customers, candidates, and hiring contacts rather than prospective brand partners.
Brands have no documented package to choose from; ask Manu or Spott directly about availability and terms.
A B2B SaaS company selling AI agent tools wants to reach recruitment-agency owners and operators through a founder who can speak credibly about AI-native recruiting inside an agency. Manu Vanderveeren fits on audience, subject and voice.
Audience. Manu’s posts address agency recruiters rather than in-house corporate talent teams: placements, candidates in search, CRM and ATS habits, contract expiry, BDRs and outbound account executives. One post details a 15-recruiter Paris firm placing engineers at frontier AI companies. A B2B company selling into that market would be speaking to the same people the posts already reach, without needing to translate the topic into a new vocabulary.
Subject. The campaign’s central question is what AI-native recruiting looks like day to day. Manu’s posts answer with operational specifics, not feature language. The Flaire case is the clearest example: one workspace replacing a CRM, an ATS, Notion and a notetaker; a customer-built agent running on an API and MCP server that matches every active candidate against every open role each morning; and manual matching that found 100 options for 100 candidates in search, against roughly 250 from the agent. The company-thesis post frames the same problem from the founder’s side: agencies pay either in knowledge stuck in one recruiter’s head or in hours lost to admin. The proposed fix is a shared, firm-wide knowledge layer that surfaces what needs attention while leaving the decision with the recruiter. A tooling vendor can build a campaign on either framing without inventing examples.
Voice and format. The posts are short and specific, and include unflattering details: a VC firm passed because it expected hardware and resources to matter most in a decade, and Manu admits that more than 90% of customers arrived inbound before the company decided to build outbound anyway. That candid founder voice is difficult to fake. A co-created piece in the same plainspoken register could feel native rather than sponsored.
Fit and due diligence. This suits a written, founder-voice collaboration or an advisory arrangement, not a turnkey sponsored placement. Before committing, ask for commercial terms and any available engagement, video or sponsored-placement performance data. The published reach figure covers one channel: about 16,000 LinkedIn followers as of 8 October 2026. Topic and voice fit are clear; campaign performance is unverified.
When to choose a specialist
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An enterprise HR platform selling to in-house corporate talent acquisition teams wants a multi-channel sponsored campaign with guaranteed impressions, video assets and a documented media kit listing formats, rates and audience demographics. This is a poor fit: agency-side and corporate-side recruiting audiences may look interchangeable from a distance, but the posts point to a different buyer and offer little of the infrastructure this campaign requires.
Audience mismatch. Manu’s posts are built for recruitment agencies, not talent acquisition inside an employer. The company describes itself as powering more than 500 agencies, from executive search to temp staffing, and the featured customer is an agency placing engineers at AI companies. An enterprise platform selling to corporate TA leaders would be buying reach into a different market; the posts do not establish that the following includes corporate HR decision-makers.
Channel and format. The material reviewed consists of written LinkedIn posts. It offers no basis for planning video assets. Reach is single-channel, at about 16,000 LinkedIn followers as of 8 October 2026, and there are no impressions, engagement or sponsored-placement results to support a guaranteed-impressions commitment.
Commercial requirements. The posts document no sponsorship format, rate card, media kit or partnership inquiry route. They are company updates aimed at customers, candidates and hiring contacts, not partner-facing inventory. A brand that needs a published package for procurement would need to request the available formats, rates and audience data directly.
Register mismatch. The posts sell Spott. A piece written for another vendor’s product would need a different structure, while a sanitized success-story format could sit awkwardly beside the candid, sometimes self-critical writing this audience already reads. Both audience and tone point away from a high-volume sponsored campaign and toward a narrower, named-piece collaboration—if the buyer is willing to change the campaign’s shape.
A worked campaign example
A campaign aimed at recruitment-agency owners and operators could take the form of a three-part co-authored series on the headcount cap in recruitment agencies, with the partner brand supplying tooling context and Manu Vanderveeren supplying the operational detail.
Part one: the problem. A founder-voice post or short essay could open with the two costs an agency already pays: knowledge that lives in one recruiter’s head, and the hours a careful recruiter loses keeping the CRM current. The argument: scaling revenue without scaling headcount is a knowledge problem before it is an AI problem. The partner’s product enters as the layer that holds the firm’s knowledge and flags what deserves attention, while the recruiter retains the hiring decision.
Part two: the proof. A teardown of the Flaire migration: one workspace replacing a CRM, an ATS, Notion and a notetaker, followed by a customer-built agent on an API and MCP server that matches every active candidate against every open role each morning. The customer supplied the figures: 100 candidates in search yielded 100 manual options against roughly 250 from the agent, alongside a claim of twice the placements from the same number of conversations. Label the 2x as a customer-stated figure, and ask Flaire whether the matching counts can be shown in more detail before publishing.
Part three: the working resource. A portable template or prompt set, co-branded with the partner, could let an agency run the same matching exercise on its own data. This gives readers something to do, and lets a tooling partner demonstrate the process rather than describe it.
Advisory alternative. If the buyer prefers no co-created content, consider a paid roundtable or recorded working session on when an agency should add outbound. It could build on the channel-shift post’s three arguments: best-fit buyers are not in-market today and will buy when a contract expires; inbound cannot be turned up on demand because it grows only as fast as content, SEO and word of mouth; and outbound is a career skill that has to be built.
Before committing budget, ask for recent post-level performance on comparable topics and formats, any audience survey covering seniority, function, company size and geography, and Manu’s terms for a collaboration. If performance or audience data are thin, plan around topic and voice fit, keep the deliverable narrow and named, and avoid guaranteed-impression targets the available figures cannot support.
Choose Fabian Herhold when the collaboration needs a founder who can describe building and selling an AI sourcing and business-development tool from inside a recruitment agency, with concrete activation and measurement detail rather than an outside product teardown.
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The Hyreflow beta post gives campaign-ready specifics: 150 workspaces three weeks after launch, 64 active in a week, an average first result in about 24 minutes, 148 tickets filed, and a deliberate move from agency project work to software.
The recruitment-email subject-line post supplies tactical material a B2B SaaS partner could co-brand into a working resource: five subject-line types with examples and a warning not to send from a CRM or primary domain.
For a brief about AI-native recruiting workflows and founder-led growth, this is the closest fit when the buyer wants the builder's own operating numbers and a resource the audience can put to work.
The tradeoff is scope and independence.
The posts focus on Fabian Herhold's own product and agency, so a partner would be buying a first-person account rather than an outside assessment of the category.
The tactical email post is agency business-development advice, not AI-native workflow architecture.
If a campaign needs a neutral operator teaching the category, Herhold is a weaker choice than an advisor who comments on other firms' systems; if it needs someone who has shipped the tool, the tradeoff is acceptable.
Ask for audience size, engagement, and commercial terms before booking a collaboration.
Choose Kalpesh Baxi when the campaign needs an agency-side advisor who speaks directly to recruitment business owners about what to fix, and who can frame AI adoption as a tasks-versus-jobs decision rather than a product launch.
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The tasks post gives a usable argument for a B2B SaaS partner selling agent tooling: AI replaces tasks, not jobs, and the real competitive threat is a rival with the same headcount and more capacity.
The owner-question post shows the advisory route: it lists pipeline, disconnected tools, unclear AI plans, and a second opinion as the things owners actually ask for, then points to a network of vetted specialists.
That makes Baxi a better choice than Manu Vanderveeren when the deliverable is a diagnostic conversation, a roundtable, or a peer recommendation aimed at agency leadership rather than a product case study.
The posts are closer to consulting positioning than operating evidence: one describes Kritmatta's approach, while the other promotes RecXperts and its specialist network.
They offer no product migration numbers or customer results.
A buyer seeking a named-customer teardown or measurable campaign performance would get less here than from a founder publishing their own product data.
Ask for customer proof and campaign results if those are central to the brief.
Choose Oliver Feakins when the campaign needs a specific vertical market where demand is moving and the argument can be carried by a data point.
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The travel-nursing post anchors a pitch on Aya's Index showing 10,824 open travel nurse jobs on September 28, up 16% in 30 days and 67% year over year, then argues that agencies that cut marketing and recruiter capacity may not be able to meet the recovery.
The recruiter-of-2027 post supplies the operating frame: AI handles volume, data finds opportunity, humans create trust, with AI described as screening, summarizing, searching, recommending, and re-engaging so recruiters can focus on the conversations that close.
For a B2B SaaS company selling recruiter-workflow or matching tooling into healthcare staffing, Feakins is a better choice than a generalist when the buyer wants a vertical story with a market indicator attached.
The tradeoff is narrowness.
The posts focus on travel nursing and healthcare staffing, so the content does not transfer cleanly to executive search, technology, or temp industrial agencies.
The demand figure comes from a third-party index and provides context, not an independently verified internal metric.
The 2027 framing is a prediction, not a documented result.
Ask for audience size, engagement, and commercial availability; for a campaign outside this vertical, commission research specific to the brief.
This page covers four LinkedIn posts by Manu Vanderveeren: a customer case study on the Paris agency Flaire and the agent it built on Spott's API, a post arguing that agencies can scale without adding recruiters by putting firm-wide knowledge in one place, a post explaining why Spott is adding outbound sales after more than 90% of customers arrived inbound, and a fundraising retrospective on a $21M raise after 40 hours of investor meetings.
The four posts carry no publication dates, so they are read as a set rather than as a timeline, and they are not the whole history of the account.
As of 8 October 2026, the LinkedIn account has about 16,000 followers.
Comparisons on the discovery section draw on two posts each from Fabian Herhold, Kalpesh Baxi, and Oliver Feakins, dated February 2025 and October 2026; similarity and difference statements rest on those posts alone.
Customer figures such as matching counts and placement claims are reported as the posts state them and were not independently verified.
No video, podcast, or transcript material appears here, and the posts provide no engagement or sponsored-placement performance data, no audience demographics, and no published partnership formats.
This covers these four posts, not full-lifetime coverage.
