Sundeep Sanghavi is a tech entrepreneur and investor who works on VideoForce.ai, using AI to find and repurpose real creator videos into performance ads.
Sundeep Sanghavi is most useful for using AI to find and repurpose real creator videos into performance ads.
Best for
Consumer or DTC brands with paid UGC libraries seeking better ad creative hit rates.
Not for
Briefs needing a named on-camera creator, ambassador, or guaranteed audience scale.
Key takeaways
Use AI to improve real creator content, not to invent people.
The best ad moments are already in footage brands paid for.
One FITNESS SF ad reported 2.01% CTR and $0.79 CPC, with little context.
Check first: Confirm whether they offer a sponsorship package or only VideoForce.ai vendor services.
Main ideas
Use AI to improve real creator content, not to invent people · The best ad moments are already in footage brands paid for · Performance isn't broken; creative systems are
Sundeep Sanghavi is a tech entrepreneur and investor whose public posts focus on using AI to make more effective ads from real creator and customer videos.
Through VideoForce.ai, he argues that brands should find and reuse credible moments already in their video libraries rather than manufacture synthetic people or testimonials.
He also posts about investing and company-building through DX.Partners.
Follow his work for a practical, commercially minded perspective on creator sourcing, video repurposing, and testing performance creative.
The supplied material establishes these businesses and themes but does not identify a signature book, show, or earlier body of work; that broader name-search context remains to be researched.
Sanghavi connects a clear creative principle to a usable workflow: start with people already talking about or using a product, look for the hook, problem, proof, and genuine result, then use AI to find and assemble those moments.
He pairs the case for repurposing with limits: the story must remain coherent, creators' words must not be changed into claims they did not make, and multiple creators do not automatically improve an ad.
His posts offer concrete ways to evaluate the idea, including testing a multi-creator edit against a strong single-creator ad.
One post reports 2.01% CTR and $0.79 CPC for a FITNESS SF ad, but gives too little campaign context to treat those figures as a general benchmark.
A post about Nurses Week offers a personal exception to the business-focused corpus; it shows family pride and respect for nurses, not a separate marketing theme.
Sundeep Sanghavi's public posts argue that creator-led advertising works because it feels like a real moment, and that AI is most useful when it helps brands find, match, and reuse those moments rather than invent synthetic people.
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The recurring priorities are creator fit over volume, testing assembled cuts against existing winners, and activating video a brand already owns. A separate strand covers company building and investing through DX.Partners.
Use AI to improve real creator content, not to invent people
Sanghavi's sharpest position is that brands undermine the trust they paid for when they use AI to fabricate people, testimonials, and social proof.
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The distinction is between AI that makes creative better and AI that fakes what makes creative believable. Sanghavi says this while stating a belief in AI and building VideoForce around it, so the argument is not anti-AI; it is a boundary drawn around authenticity.
In a post responding to a Forbes article on creator-style video mistakes, Sanghavi writes 'FAKE HUMANS SUCK' and offers to build a brand's first multi-creator ad for free from its existing UGC during the holiday rush, with no demo and no credit card. The same post says the brand keeps the ad.
The best ad moments are already in footage brands paid for
Sanghavi argues the hook, product demo, and testimonial often sit in three different UGC videos.
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VideoForce uses AI to find those moments and assemble them into one multi-creator ad.
The guardrails matter: the story still has to make sense, and you cannot edit someone's words into a claim they never made.
Sanghavi also says three people in an ad is not automatically better than one, and that a strong single-creator ad stands on its own.
The suggested test is to pick one product, pull together the UGC the brand is cleared to edit and use in advertising, build a new cut from the strongest moments, and measure it against the best single-creator ad before booking another shoot.
From conversations with consumer brands and agencies, Sanghavi describes a repeating complaint: good content that does not convert, refreshes that never compound, and best ads that do not look like ads.
Read more
The diagnosis is that winning ads match moments buyers already recognize rather than selling harder. Sanghavi gives a repeating structure: recognizable problem, natural reaction, unscripted solution, subtle social proof, and a low-pressure call to action.
The reframing question is to stop asking 'How do we sell this?' and start asking 'What moment does our customer already believe?'
Creator sourcing is a matching problem, not a volume problem
Sanghavi argues the bottleneck is consistently matching the right creators to a brand, not finding more creators.
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The method is to look for people already talking, testing, and reacting to the product, with no scripts and no 'content days.' While sourcing, Sanghavi says the team watches for the hook, the problem, the proof, and the natural 'this actually worked for me' moment. AI is used later to surface those moments faster.
Sanghavi describes work with Carrera Eyewear, a Safilo brand, and says VideoForce.ai uses natural language processing to match brand voice, visual language, and audience nuance to creators. In a separate post, Sanghavi quotes Smith Optics leadership saying they were 'stoked with the quality of content showing up in paid' — a guest comment reported in the post.
Sanghavi's commercial premise is that most video never gets used.
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The claim is that nearly 90% of video content sits as raw footage, behind-the-scenes clips, customer reactions, and creator content that already carries trust. Winning brands, in this argument, are not creating more content but redeploying influencer, UGC, branded video, and existing ads across paid, product pages, social, and lifecycle channels.
Sanghavi asks the network whether they are fully activating their existing video library or leaving most of it untouched. The 90% figure is stated without a cited study in the post, so it is Sanghavi's framing rather than a verified benchmark.
A recurring habit in Sanghavi's posts is pairing a specific campaign metric with named partners and process details.
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This functions as a public testing culture: a multi-creator ad is reported at 2.01% CTR and $0.79 CPC, an internal 'Performance Agent' is said to have flagged it early, the team scaled it, and pacing was being tightened to lift completion. Sanghavi credits named people at Thrive Digital, Smith Optics, DXFactor, and the internal creative lead.
The Fitness SF post reports the 2.01% CTR and $0.79 CPC figures.
The Smith Optics post quotes that leadership was 'stoked with the quality of content showing up in paid' and credits named teams for sourcing through briefing, approvals, and delivery.
Build by earning a domain, compounding data, then unlocking distribution
Sanghavi also writes about company building and investing through DX.Partners.
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The stated approach is to earn the domain, compound data, unlock distribution, and then focus on winning the category. This is a separate strand from the video-marketing posts, but it shows the operating logic Sanghavi applies to portfolio companies.
Sanghavi congratulates named founders and teams on the formation of AltaDX out of Alta Technology Group and DXFactor, describing the combination as a 'category-creation force' and linking to the press release.
This is the clearest single statement of what he is arguing for and against, and it answers the question a newcomer is most likely to have about someone building an AI video company: does he want to replace creators with synthetic people?
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He says no, and explains the distinction he uses.
This post states his position directly: brands spend millions with real creators to build trust and UGC, then use AI to manufacture people, testimonials, and social proof.
He cites a Forbes article on mistakes brands make with creator-style video as the trigger, and separates two uses of AI — making creative better versus faking what makes it believable.
He then points to content brands already paid for and offers to build a first multi-creator ad free from a brand's existing UGC, with no demo and no credit card.
For brands planning a campaign
For a brand sitting on a library of paid UGC, the post is a free, low-commitment way to test repurposing existing assets before commissioning anything new — and it frames the argument a marketing team can take internally when someone suggests AI-generated testimonials. Treat the free-ad offer as a promotional pitch, not a verified outcome.
This is the clearest explanation in the set of the format he is selling, including its limits.
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A reader who wants to know what a 'multi-creator ad' actually is will get a working definition here rather than a slogan.
The post walks through a specific idea: the best hook, product demo, and testimonial are often sitting in three different UGC videos, so VideoForce uses AI to find those moments and assemble them into one ad.
He also states the guardrails — the story still has to make sense, and you cannot edit someone's words into a claim they never made.
He admits three people do not automatically make a better ad, and that a strong single-creator ad stands on its own.
The suggested test is to pick one product, pull the UGC you are cleared to edit, build a new ad from the strongest moments, and measure it against your best single-creator ad.
For brands planning a campaign
The post supplies a ready test design: one product, cleared footage only, one assembled ad against the current best single-creator ad. It also flags the permission and editorial-consent constraint that a legal or brand-safety review will ask about first.
This answers the practical question of how a creator is actually chosen, and it gives four named criteria a reader can reuse.
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It is also the post where he describes sourcing for an eyewear brand specifically.
Written around work with Carrera Eyewear, a Safilo brand, the post argues creator sourcing is about getting the right people rather than volume.
He describes looking for people already talking, testing, and reacting, with no scripts, no 'content days,' and no pretending.
While sourcing, he says the team watches for four things: the hook, the problem, the proof, and the natural 'this actually worked for me.' AI is used afterward to surface those moments faster.
He also mentions a free Highlights tool for pulling moments out of footage already sitting in folders.
For brands planning a campaign
The four criteria — hook, problem, proof, personal result — work as a selection rubric for any brand briefing creators, independent of which vendor is used. The sourcing stance implies a slower, observation-first search than a follower-count shortlist, which is worth weighing against a campaign deadline.
This is the only item in the set carrying a specific performance number, so it is the fastest place to judge whether the results he describes are in a range that matters to your media plan.
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A post about work with FITNESS SF reports a multi-creator ad hitting 2.01% CTR and $0.79 CPC.
His description of the format is one hook, multiple perspectives, one clear reason to act.
He says an internal 'Performance Agent' flagged the ad early, that it was scaled, and that pacing is being tightened to push completion rate up.
He credits Badiya Nighat for leading the creative work.
For brands planning a campaign
Use the figures as a rough benchmark for a multi-creator test, not as a guarantee: the post does not state spend, platform beyond the Meta-related hashtags, audience, or duration, and the numbers are self-reported.
This is the diagnosis behind everything else he posts, and it is written as a reusable pattern rather than a product pitch.
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A reader who only opens one post to understand his thinking should open this one.
Drawing on conversations with consumer brands and agencies, the post argues the common problem is not media buying but creative that burns out or stops working when spend scales.
It lists complaints he hears repeatedly — good content that does not convert, refreshes that never compound, best ads that do not look like ads — and gives a repeating structure for ads that last: a recognizable problem, a natural reaction, an unscripted solution, subtle social proof, and a low-pressure call to action.
His reframing question is to ask what moment the customer already believes rather than how to sell the product.
For brands planning a campaign
The five-step pattern can be used to audit a failing ad set and to brief new creative, and the 'what moment does the customer already believe' question is a useful brief-writing prompt for a DTC team.
It states the commercial premise plainly: the value is in activating footage a brand already owns.
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That premise is the reason to care about the rest of his work.
The post claims nearly 90% of video content is never used and sits on drives as raw footage, behind-the-scenes clips, customer reactions, and creator content.
The argument is that winning brands are not producing more but redeploying what exists — influencer, UGC, branded video, and existing ads — across paid, product detail pages, social, and lifecycle channels.
He ends by asking his network how they handle their existing libraries and tags several marketers.
For brands planning a campaign
A brand considering a new shoot can use this as the case for a library audit first. The 90% figure is asserted in the post without a cited study, so it works as a framing argument rather than a statistic to reuse in a client deck.
It shows the actual shape of a brand-agency-vendor engagement rather than a product claim, including the steps from sourcing to approvals to delivery.
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Readers evaluating whether to work with him can see who is normally in the room.
This post names partners and describes the working setup: senior leadership at Smith Optics, quoted as being 'stoked with the quality of content showing up in paid,' alongside teams at Thrive Digital.
He says VideoForce.ai uses natural language processing to match brand voice, visual language, and audience nuance to creators, and that the real bottleneck is matching the right creators to a brand consistently.
He credits named people at Thrive Digital, at Smith Optics, and inside his own team for running sourcing through briefing, approvals, and delivery.
For brands planning a campaign
Useful for an agency or brand planning a creator program: the post implies the vendor expects to plug into an existing agency relationship and a brand-side approval process rather than replace them. The Smith Optics quote is reported by him, so it is a signal of the relationship, not independent proof of results.
It is the clearest evidence in the set that his activity is not limited to VideoForce — he operates and invests through DX.Partners and talks about consolidation and category creation.
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That context matters when reading everything else in commercial terms.
A post congratulating named founders and teams on the formation of AltaDX, describing his firm's approach as earning the domain, compounding data, unlocking distribution, and then focusing on winning the category.
He thanks portfolio colleagues, partners, and customers, and links to a press release.
The post is a business announcement rather than commentary on video or creator marketing.
For brands planning a campaign
Limited direct application to a creative or UGC brief, but relevant if a brand is considering a longer-term commercial relationship: it shows where his attention sits beyond a single campaign.
Format: short text posts built around a claim, a client example, and a test4
Multi-creator ads built from footage brands already own2
AI should improve real creator content, not manufacture people2
Creator sourcing is a matching problem, not a volume problem2
Publishing campaign metrics alongside named partners2
The bottleneck is the creative system, not performance1
Activating the video library a brand already has1
Company building and investing outside the video-marketing thread1
Occasional posts outside marketing entirely1
Multi-creator ads built from footage brands already own
Sanghavi's recurring argument is that a brand's best hook, product demo, and testimonial often sit in three different UGC videos it already paid for.
Read more
VideoForce uses AI to locate those moments and assemble them into one multi-creator ad, and he is explicit about the limits: three people in an ad is not automatically better, a strong single-creator ad stands on its own, and you cannot edit someone's words into a claim they never made. His suggested test is to pick one product, pull together the UGC the brand is cleared to use, build a cut from the strongest moments, and measure it against the best existing single-creator ad before booking another shoot.
Assemble from footage already cleared for advertising; keep every claim inside what the creator actually said; validate against the best single-creator ad rather than assuming more faces perform better.
For brands planning a campaign
For a DTC team with an existing paid-UGC library, this is a concrete, low-cost test that can run before new production spend: one product, cleared footage, one assembled ad measured against the current best performer.
AI should improve real creator content, not manufacture people
In his most direct post, Sanghavi argues that brands spend on real creators to build trust, then turn around and use AI to fabricate people, testimonials, and social proof.
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He draws the line between using AI to make creative better and using it to fake what makes creative believable, and places VideoForce on the first side while stating he is a believer in AI and building a company around it. The same post offers to build a brand's first multi-creator ad free from its existing UGC during the holiday rush: no demo, no credit card, the brand keeps the ad.
The free-ad offer and holiday timing appear once and are not repeated in the other reviewed posts.
For brands planning a campaign
A team weighing synthetic-avatar or AI-persona tools against a real-creator pipeline gets a clearly stated position to evaluate, with the caveat that the position comes from someone selling the alternative. The free-first-ad offer is a promotion in a post, not a documented standing offer, so treat it as a pitch to test rather than a priced service.
The bottleneck is the creative system, not performance
Drawing on what he says are dozens of conversations with consumer brands and agencies, Sanghavi reports a repeating complaint pattern: content that does not convert, refreshes that do not compound, and best-performing ads that do not look like ads.
Read more
His diagnosis is that winning ads match moments buyers already recognize rather than selling harder, and he lays out the sequence he sees working: recognizable problem, natural reaction, unscripted solution, subtle social proof, and a low-pressure call to action.
For brands planning a campaign
Useful for a team diagnosing ad fatigue or stalled creative. It reframes the brief from "how do we sell this" to "what moment does our customer already believe," which can be handed to a creative team as a working prompt.
Creator sourcing is a matching problem, not a volume problem
Sanghavi argues sourcing is about getting the right people rather than more people, describing work with Carrera Eyewear where the team looked for real humans already talking about, testing, and reacting to the product, with no scripts and no content days.
Read more
He says the sourcing process watches for specific moments: the hook, the problem, the proof, and the natural "this actually worked for me" line. In a separate post he describes an NLP-based sourcing system at VideoForce.ai that matches brand voice, visual language, and audience nuance to creators who already live in that world, and cites Smith Optics leadership's response to the quality of content showing up in paid as the signal that it worked.
For brands planning a campaign
For brands whose bottleneck is creator fit rather than creator count, this supplies selection criteria a team can adopt directly: existing category behavior, voice and visual match, and evidence of a natural proof moment. The client outcomes are described by Sanghavi and are not independently documented in the reviewed material.
Sanghavi's claim is that nearly 90% of video content never gets used and sits as raw footage, behind-the-scenes clips, customer reactions, and creator content that already carries trust.
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He argues the brands winning are not creating more content but redeploying influencer, UGC, branded video, and existing ads across paid, product pages, social, and lifecycle channels, and asks his network whether they are fully activating their library or leaving most of it untouched.
The statistic is presented without attribution in the post.
For brands planning a campaign
A useful trigger for an audit of what a brand already owns before committing new production budget. The 90% figure is stated without a source, so it should be treated as his assertion rather than a verified benchmark.
Publishing campaign metrics alongside named partners
Several posts pair a specific result with named credit.
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A multi-creator ad for Fitness SF is reported at 2.01% CTR and $0.79 CPC, with Sanghavi saying a "Performance Agent" flagged it early, that the team scaled it, and that pacing is now being tightened to lift completion. He thanks named partners at Thrive Digital and Smith Optics, credits the DXFactor team with executing sourcing, briefing, approvals, and delivery, and names the creative lead behind the winning ad.
Posts the metric, the partner names, and the internal process that produced the result, then invites interested brands to message him directly.
For brands planning a campaign
Self-reported, single-campaign numbers rather than an audited record, but they give a team a specific claim to interrogate in a vendor conversation and show that performance data is shared publicly as part of the pitch.
Company building and investing outside the video-marketing thread
A separate strand of posts covers deal-making rather than creative.
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Sanghavi congratulates named founders on the formation of AltaDX out of Alta Technology Group and DXFactor, framing it as a reunion of people with shared values and standards, and spells out the DX.Partners approach: earn domain, compound data, unlock distribution, then focus on winning the category.
For brands planning a campaign
Shows a wider operating and investing role behind the video-marketing content, which matters for context but gives a consumer marketing team little to act on for a creative brief.
One reviewed post departs fully from the marketing thread: a Nurses Week tribute to his daughter Anjali, a Level IV NICU nurse, describing her work with medically complex newborns and citing Gallup's finding that nurses are the most trusted profession, AMN Healthcare data that 61% of nurses planned to leave within 12 months, and projected US nursing shortages.
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The post closes with a call to pay and value nurses.
The only non-marketing post in the reviewed material, which indicates the account is not exclusively a professional marketing channel.
For brands planning a campaign
Little direct use for a creative brief, but it is relevant to tone and audience if a brand engages with this account: personal advocacy sits alongside the professional content, and the audience reaction to it is not visible in the reviewed material.
Format: short text posts built around a claim, a client example, and a test
The reviewed posts share a consistent format: a blunt opening claim, a short explanation of what most teams get wrong, a named client or partner example, and a suggested next action such as testing a new cut against an existing ad or inspecting unused footage.
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Examples are usually offered in the comments rather than the post body. Posts frequently end with a question to the network or a direct invitation to message him.
Claim, diagnosis of the common mistake, client example, testable next step, and a call to contact; hashtags carry the recurring themes including advertising, performance marketing, UGC, and creative strategy.
For brands planning a campaign
A brand knows what to expect from outreach or a co-marketing post: a short, example-led pitch with a testing suggestion attached, rather than long-form analysis or published case material.
Sundeep Sanghavi's brand relationships center on VideoForce.ai, the AI video company they describe building, and the consumer brands they name as clients — Carrera Eyewear, Smith Optics, and FITNESS SF — plus the agency Thrive Digital.
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A post about the AltaDX deal also places them inside DX.Partners, which they describe in the first person. No reviewed post discloses a paid sponsorship or affiliate arrangement.
VideoForce.ai
Owned business
Sanghavi writes that they are building VideoForce, which uses AI to pull the strongest hook, product demo, and testimonial out of creator and UGC videos already paid for and assemble them into multi-creator ads brands can test against a single-creator version.
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The posts speak as "we" and name internal tools the company runs, including a Highlights agent and a Performance agent, plus a creative team.
A sourcing post describes work with Carrera Eyewear, a Safilo brand: instead of hunting influencers or running scripted content days, the team looked for people already talking, testing, and reacting, and used AI to surface the moments worth keeping.
Sanghavi quotes Smith Optics leadership saying they were "stoked with the quality of content showing up in paid," and describes VideoForce's NLP-based sourcing system matching brand voice, visual language, and audience nuance to creators for the brand.
Sanghavi thanks Willem Thomas, Tyler Power, and Christopher Erickson at Thrive Digital, alongside the Smith Optics contact, for "trusting the system and pushing standards higher."
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The posts call them partners but do not state the terms of the agency relationship.
A post credits Fitness SF as a "trusted partner" and reports that a multi-creator ad built for the brand returned 2.01% CTR and $0.79 CPC, with the team scaling it and tightening pacing to lift completion.
In a post congratulating AltaDX on its formation, Sanghavi writes "this is how we build at DX.Partners: earn domain, compound data, unlock distribution," and thanks the DXFactor team, partners, and investors.
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The post does not state their role, ownership stake, or investment in the firm.
Sundeep Sanghavi's readable archive is a set of short LinkedIn posts about making creator-led video ads work: sourcing real creators instead of more creators, reusing UGC a brand has already paid for, and using AI to find and assemble the strongest moments rather than to invent people.
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Start with the multi-creator ad argument, which is the clearest statement of the position, then read the sourcing and creative-system posts for the working method behind it. A second, separate thread covers company building and investing, and one post steps outside marketing entirely.
Both treat creator content as performance infrastructure rather than a one-off campaign purchase.
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Rocklein's TikTok Shop write-up makes the same underlying argument Sanghavi makes about UGC: creators are a system to build, content velocity is the moat, and the winning brands run repeatable engines instead of polished campaigns.
Rocklein's material is platform mechanics for TikTok Shop: in-app discovery, seller versus creator content, 6-to-12-hour live sessions, halo lift into Amazon and DTC. Sanghavi stays at the level of the ad itself, arguing about sourcing the right creator and assembling a multi-creator cut from footage the brand already owns.
Both publish tactical creative advice for brands buying paid media, and both put UGC and creator-made assets at the center of performance rather than treating them as a brand-awareness line item.
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Both write for the same reader: an ecommerce or DTC team trying to make ads that convert and keep converting.
Brown's posts are platform-specific and format-specific, covering AppLovin hook timing, interactive end cards, and long-form video structure, and they come from an agency selling media buying, CRO, and UGC packages. Sanghavi's advice is about the source of the creative, arguing that the strongest ad already exists across footage the brand paid for and should be assembled rather than shot again.
Both make the case that brands are sitting on usable material and should redeploy it instead of starting from scratch.
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Sanghavi makes it about unused video and creator footage; Mikey H. makes the same move for B2B content, turning existing SEO articles, case studies, and reports into social and video output.
Mikey H. works on B2B demand generation and the balance between demand creation and demand capture, with repurposing feeding a content-led go-to-market pipeline. Sanghavi works on consumer video advertising, where the reused material is UGC and the payoff is measured in ad metrics such as click-through rate and cost per click.
Placement read: Sanghavi's posts are written for the people who commission and run creator content — brand-side creative and paid-social leads, agency performance teams, and DTC marketers — so a consumer brand or DTC team looking for a voice on UGC-based ad creative is in the intended room.
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The content is not aimed at general consumers.
What is actually measured is thin.
The LinkedIn profile carried 11,451 followers when recorded on 11 October 2026.
That is practitioner scale: large enough for brand and agency peers to notice the work, not a consumer-reach audience.
Nothing in the supplied posts or profile data measures who those followers are, so the read below is about content appeal, not measured audience composition.
The appeal is clearly professional.
The work discussed is brand and agency work: sourcing real creators for Carrera eyewear (a Safilo brand), multi-creator ads for FITNESS SF reported at 2.01% CTR and $0.79 CPC, creator sourcing credited by contacts at Smith Optics and Thrive Digital, and a repeated argument against AI-fabricated people in UGC.
People named in the posts are partners, clients, and colleagues, which shows who Sanghavi works with — not who reads.
One Nurses Week post about family sits outside the marketing theme and will reach a broader personal network.
Gap and next step: no demographics or engagement export was supplied, so geography, age, gender, employer, and job seniority are unknown.
Ask for recent LinkedIn follower and engagement figures, any audience-survey results or platform demographics export, and results from sponsored posts in comparable topics and formats, so the audience can be judged on delivery rather than reach alone.
No published creator sponsorship package is documented in the reviewed posts.
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The verified partnership surface is VideoForce.ai: a first multi-creator ad built free from a brand's existing UGC; a free Highlights agent that pulls usable moments from existing videos; and an AI-led sourcing system that matches creators to brand voice, visual language, and audience.
The posts also describe an operating sequence — sourcing, briefing, approvals, delivery — and a creative pattern: recognizable problem, natural reaction, unscripted solution, subtle social proof, low-pressure CTA.
Disclosed client work includes Smith Optics, Carrera Eyewear, FITNESS SF, and Thrive Digital, but the posts do not state pricing after the free offers, usage windows, exclusivity, or minimum spend.
A buyer should confirm those terms directly with VideoForce before planning spend.
Free first multi-creator ad from existing UGC
Buyer requirement
Provide existing UGC that the brand has permission to edit and use in advertising. The offer is for a first multi-creator ad at no cost, with no demo or credit card, and the brand keeps the ad.
Free Highlights extraction from existing video
Buyer requirement
Have existing videos sitting in folders and be willing to let the Highlights agent pull out moments worth keeping. The post offers this free and asks interested brands to DM Sanghavi.
AI-led creator sourcing for paid and organic content
Buyer requirement
Supply brand voice, visual language, and audience detail for creator matching. Expect an operating sequence of sourcing, briefing, approvals, and delivery; the post describes the system as a way to scale creator volume without losing brand aesthetic and to use existing creator content in paid ads and other organic marketing channels without operational chaos.
A consumer brand or DTC team that already owns a library of paid UGC and wants to raise the hit rate of its ad creative is the closest match to the documented work.
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Sanghavi's posts describe exactly that job: take footage the brand has already paid for and is cleared to use, find the strongest hook, product demo, and testimonial moments, and assemble them into one multi-creator ad to test against the current best single-creator ad.
Named client work runs in this direction — Carrera Eyewear, a Safilo brand; Smith Optics; FITNESS SF; and agency partners at Thrive Digital — and one post reports a multi-creator ad for FITNESS SF at 2.01% CTR and $0.79 CPC, with the caveat that the figure is self-reported and the post does not state spend, placement, audience, or duration.
The sourcing stance also fits brands whose problem is creator fit rather than creator count.
Sanghavi describes looking for people already talking about, testing, and reacting to the product, with no scripts and no content days, then screening for four things: the hook, the problem, the proof, and a natural "this actually worked for me" moment.
A brand that has been choosing creators by follower count has something concrete to try instead.
Two free entry points appear in the reviewed posts, which makes a small pilot cheap to scope: a first multi-creator ad built from existing UGC, with no demo or credit card and the brand keeping the ad, and a Highlights tool that pulls usable moments out of footage already sitting in folders.
These are pitched in posts rather than documented as standing offers, so treat them as a pitch to test.
A brand can also use the thinking without buying anything.
The five-part structure Sanghavi describes — recognizable problem, natural reaction, unscripted solution, subtle social proof, low-pressure call to action — can be handed to a creative team as a brief-writing prompt, and his reframing question is to ask what moment the customer already believes rather than how to sell the product.
His stated position on AI is a fit for brands that want AI used on real footage and not to invent people or testimonials, which is how he separates the two uses in his own posts.
When to choose a specialist
A brief that needs a named creator to appear on camera is a poor match on the reviewed evidence.
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Nothing supplied documents a sponsorship package, on-camera creator format, exclusivity term, usage window, or deliverable list.
The commercial surface described in the posts is a vendor pipeline: creator sourcing, briefing, approvals, delivery, and ad assembly, with clients named as brands and agencies rather than as campaigns where Sanghavi appears.
A brand shopping for an ambassador or a face for a consumer campaign is looking at a different service than the one on offer.
A brief built on guaranteed audience scale is also a poor fit from what is documented.
The account carried 11,451 followers when recorded on 11 October 2026, which is practitioner scale — enough for brand and agency peers to notice the work, not a consumer-reach audience — and no demographics, engagement rate, geography, or follower-growth data was supplied.
Selection by follower count is the practice his sourcing argument pushes against, so a media plan whose reach case rests on audience size has no support here.
A brief that needs fixed commercial terms before planning cannot be completed from this material either.
The posts state free offers but not pricing after them, and they do not set out usage rights, minimum spend, exclusivity, or delivery terms.
The campaign results are reported by Sanghavi and no independent documentation of them was available, so a buyer should confirm numbers and terms directly with VideoForce before committing spend.
The clearest mismatch is a team that wants synthetic AI avatars or fabricated testimonials.
Sanghavi argues directly against using AI to manufacture people, testimonials, and social proof, and says so while stating he is building an AI video company.
A brief premised on AI-generated humans would be arguing with the vendor's stated position.
A worked campaign example
Brief: a DTC skincare brand has around 60 paid UGC clips from the past year, its best-performing ad has plateaued, and the team wants one testable creative idea before committing to another shoot.
Angle: build one multi-creator ad from the footage the brand already owns, then measure it against the brand's best single-creator ad rather than against a new production.
Step 1 — Audit before producing.
Run the library through the claim Sanghavi makes that nearly 90% of video content never gets used.
Treat the figure as his framing argument, not a benchmark with a cited study, but use the audit to list which clips contain a hook, a demo, or a proof moment.
Step 2 — Pick one product.
His suggested test is deliberately narrow: one product, not the full range.
Choose the SKU with the most usable existing clips and the clearest before-and-after evidence.
Step 3 — Pull only footage the brand is cleared to edit and use in advertising. This is the first constraint the post names, and it is where legal or brand-safety review will start.
Step 4 — Assemble one cut using the five-part structure he describes: a recognizable problem, a natural reaction, an unscripted solution, subtle social proof, and a low-pressure call to action.
Source the problem line from one creator, the demo from a second, and the testimonial from a third — the specific combination his post calls the reason multi-creator ads exist.
Keep two guardrails he states: the story still has to make sense, and no creator's words can be edited into a claim they never made.
More faces do not automatically make a better ad, so the single-creator version stays in the test.
Step 5 — Set the measurement.
The reference point in the reviewed material is a reported 2.01% CTR at $0.79 CPC on a FITNESS SF multi-creator ad, flagged as self-reported and without stated spend or placement.
Use it as a rough range to interrogate, not a promised result, and decide in advance what improvement over the brand's current best ad would justify scaling.
Step 6 — If new creators are needed, apply the sourcing criteria from the Carrera work: find people already talking about and testing the product, and screen for a real hook, a stated problem, proof, and a natural result line.
Handoff: the brand ends up with one new ad built from existing assets, a defined test against its current winner, and a sourcing rubric if the test needs fresh footage.
Budget, usage terms, and what applies after the free first-ad offer still have to be confirmed with VideoForce directly; that information is not in the reviewed posts.
Two evidence gaps remain open: no independent confirmation of the reported campaign figures, and no long-run post history, since the reviewed posts carry only two timestamps and otherwise cannot be dated reliably.
Choose Rocklein when the brief is TikTok Shop specifically.
Read more
The two reviewed posts are recaps of a TikTok Shop Summit and lay out a full-funnel system for that platform: creator content for awareness, seller content for conversion, the Shop tab and in-app search for intent capture, 90-day content calendars, 6-to-12-hour mega-lives with scarcity mechanics, affiliate and long-term creator relationships, and a measurement case for TikTok-driven lift on Amazon, Google, and DTC. A brand whose problem is "we need to sell on TikTok Shop and build a repeatable engine there" is closer to this material than to Sanghavi's multi-creator ad assembly, which focuses on cutting existing UGC into a testable ad rather than running a channel.
The work is channel-specific.
Nothing in the posts addresses taking a brand's existing library of paid UGC and assembling hook, demo, and testimonial moments into new ads for Meta, AppLovin, or brand-owned placements, which is the decision Sanghavi's posts speak to.
There are also no reported campaign numbers in the reviewed posts, and the two posts describe the same Summit day, so the evidence is one event rather than a body of client work.
A brand that needs one measurable ad from footage it already owns gets a system and a calendar, not an editing method.
Choose Brown when the placement is a mobile or in-app ad network such as AppLovin, where there is no scroll to stop and the viewer can skip at five seconds.
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One post gives a three-part creative framework for that setting: hold attention past the five-second skip mark, treat end cards as micro-landing pages with quizzes and carousels, and use the full 60 seconds to agitate the problem and stack social proof.
The other post pitches the wider agency package behind it — media buying across Meta, TikTok, Google, and AppLovin, conversion-rate optimization, affiliate management, landing pages, and UGC content.
A brand that wants one agency to own media buying, CRO, and creative together matches this more closely than Sanghavi's sourcing-and-ad-assembly offer.
The reviewed posts are service pitches rather than documented client results, so there are no performance figures to interrogate and nothing showing how the framework performed in market.
The creative advice applies to one network's ad format, and the second post points to hiring the agency rather than giving the brand a method it can run in-house.
Neither post addresses reusing a brand's existing UGC library, which is the problem Sanghavi's material is built around.
Choose Howe when the buyer task is B2B demand generation rather than consumer ad creative.
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One post frames the problem as balancing demand creation against demand capture — content for the roughly 95% of the market that is not buying yet, and a capture system for the 5% that is — and names the failure modes of over-investing in either side.
The second post speaks directly to a team sitting on years of SEO content: turn the existing case studies, articles, statistics, and reports into carousels, long-form video, infographics, and gated content through a central searchable knowledge base and a small repurposing team.
If the brief is "we have content nobody sees and a pipeline that needs feeding," this is the closer read.
The work is B2B and content-led, not consumer video advertising.
It repurposes written and SEO assets into social distribution rather than turning sponsored UGC into paid ads, so on the specific decision of raising a DTC ad's hit rate it answers a neighbouring question.
Both posts close with a pitch for the same consultancy, and no campaign outcomes are reported, so a brand comparing vendors gets a method without a measured result to weigh against Sanghavi's reported, self-reported campaign figure.
This profile draws on nine LinkedIn posts from Sundeep Sanghavi's account, all read in full, plus an account snapshot that recorded 11,451 followers on 11 October 2026.
Most of the posts carry no reliable publication date in the supplied material, so no timeline of changing views could be established.
Two timestamps appear (29 January 2026 and 6 February 2026).
Claims about client results, including a reported 2.01% CTR and $0.79 CPC for a FITNESS SF ad, and the statement that nearly 90% of video content goes unused, appear as written in the posts and have no independent documentation available.
Similar-creator comparisons draw on retained posts from Kelly Rocklein, Stephanie Brown, and Mikey H.
The reviewed posts document Work through VideoForce.ai, named client work with Carrera Eyewear, Smith Optics, FITNESS SF, and Thrive Digital, a company-formation announcement, and one personal post; they do not establish earlier career history or signature work such as a book or show, and that remains an open research gap.
Not available: pricing after the free offers, usage, exclusivity, or minimum-spend terms, the ads themselves, and the cited Forbes article.
Recent engagement
Across their last 8 LinkedIn posts, the median post got 27 reactions and 2 comments. Checked 2026-11-02.
