SuperMe
An AI-native professional network creating conversational AI profiles based on real experts' knowledge.
Website: https://www.superme.ai/
Cover Block
Open sources
| Field | Value |
|---|---|
| Name | SuperMe |
| Tagline | An AI-native professional network creating conversational AI profiles based on real experts' knowledge [TechCrunch, October 2026] |
| Headquarters | San Francisco, CA, US [TechCrunch, October 2026] |
| Founded | 2024 [TechCrunch, October 2026] |
| Stage | Series A [TechCrunch, October 2026] |
| Business model | B2C |
| Industry | HR / Future of Work |
| Technology | AI / Machine Learning |
| Geography | Global / Remote-First |
| Growth profile | Venture Scale |
| Founding team | Co-Founders (2): Casey Winters, Ludo Antonov [TechCrunch, October 2026] |
| Funding label | Series A |
| Total disclosed funding | Approximately $20,000,001, including a reported $20,000,000 Series A in October 2026, a reported $6,800,000 seed in November 2025, and a reported $1 pre-seed entry in January 2025 [TechCrunch, October 2026] [aVenture Company Research] [StartupHub.ai] |
Links
Open sources
- Website: https://www.superme.ai/
What an Investor Needs First
PUBLIC SuperMe is building an AI-native professional network that lets users ask work and career questions to conversational profiles modeled on real experts, and it merits investor attention now because it paired that product thesis with a $20 million Series A in October 2026 led by Forerunner, with Greylock participating [TechCrunch, October 2026] [SuperMe, October 2026]. Founded in 2024 by Casey Winters and Ludo Antonov in San Francisco, the company is trying to turn public expert knowledge into an interactive advice layer rather than a static content archive, a framing that has begun to attract both venture backing and early media attention [TechCrunch, October 2026] [SuperMe, October 2026].
The product, as publicly described, routes a user question to the profiles best suited to answer and can return multiple perspectives in a group-chat style workflow; Greylock also said the product had already supported more than 30,000 conversations across early product, growth, and marketing clusters as of October 2026 [TechCrunch, October 2026] [Greylock]. That differentiation rests less on model novelty than on profile construction and network density: SuperMe says users can get answers from AI profiles of real experts, while outside coverage reports those profiles are built from public materials such as LinkedIn, YouTube, Substack, and blogs [SuperMe, October 2026] [AI Weekly, October 2026].
The founding team is a material part of the story. Winters previously held senior roles spanning Grubhub, Pinterest, Eventbrite, Greylock, and Reforge, while Antonov's background includes engineering leadership at Pinterest and Whatnot, giving the company a credible mix of growth, product, and technical experience for a network-driven consumer product [TechCrunch, February 2017] [Axios] [TechCrunch, February 2022] [Casey Winters - LinkedIn].
On financing and model, the public record is clearest on the current round and less reliable on earlier history. TechCrunch and the company both confirmed the October 2026 Series A, and public descriptions point to a B2C product that was free as of late October 2026, with reported plans for query limits and paid tiers over time [TechCrunch, October 2026] [SuperMe, October 2026] [AI Weekly, October 2026].
What matters over the next 12 to 18 months is whether SuperMe can convert novelty into repeat usage and paid behavior without diluting answer quality as the network expands. Public evidence suggests early engagement and active hiring, but the company still appears early in team scale, with reported employee counts ranging from 8 to approximately 13 across sources, so investors should watch for evidence of durable retention, clearer monetization, and whether expert supply scales fast enough to keep the product useful across more domains [PitchBook] [Silicon Report, October 2026] [The Cryptonomist, October 2026].
Partially corroborated -- Core financing and product positioning are corroborated by TechCrunch and the company, but several operating details, including traction and headcount, rely on single-source or partially corroborated reporting.
Taxonomy Snapshot
| Axis | Value |
|---|---|
| Stage | Series A |
| Business Model | B2C |
| Industry / Vertical | HR / Future of Work |
| Technology Type | AI / Machine Learning |
| Geography | Global / Remote-First |
| Growth Profile | Venture Scale |
| Founding Team | Co-Founders (2) |
| Funding | Approximately $20,000,000 disclosed |
Inside the Company
PUBLIC
SuperMe presents itself plainly: a professional network built for asking work questions to people who have already done the job, with AI doing the routing and interface layer rather than replacing the underlying source expertise [SuperMe, October 2026].
The public record is still early, but the chronology is clean enough to follow. By October 2026, SuperMe was operating under the name SuperMe Network, Inc. on its website and offering free sign-up access to a product framed around "instant answers from real work" and "multiple perspectives" from work groups of founders and executives [SuperMe, October 2026]. In the same month, the company announced a $20 million Series A led by Forerunner with participation from Greylock, which stands as the clearest public milestone to date and marks the point when SuperMe moved from a lightly surfaced product into broader venture coverage [SuperMe, October 2026].
What is established publicly, then, is less a long operating history than a narrow but credible outline: founded in 2024, based in San Francisco, live on the web by 2026, and newly financed to expand from an early product and expert network into a larger platform [SuperMe, October 2026]. The missing pieces, including a fuller legal and operating history through state filings or a longer sequence of disclosed milestones, are simply not developed in the source set used for this section.
Claim stands unchecked -- This section relies primarily on the company website and company funding announcement, with no Crunchbase or state filing evidence used here.
Under the Hood
MIXED
The product pitch is straightforward: SuperMe lets a user ask a career or work question in natural language, then routes that question to AI profiles modeled on real experts whose public output has been indexed into the system [TechCrunch, October 2026] [SuperMe, October 2026]. Public coverage describes the core workflow as a group conversation, where multiple relevant expert profiles can answer the same prompt and the user can compare perspectives rather than receive a single synthesized response [TechCrunch, October 2026] [Greylock, retrieved 2026] [Mezha, October 2026]. The company website frames this more simply, promising "instant answers from real work" and inviting users to "ask the people who’ve actually done it" [SuperMe, October 2026].
The evidence on how those profiles are built is directionally consistent, but not equally strong across sources. TechCrunch and other October 2026 coverage report that SuperMe trains or constructs profiles from public materials such as LinkedIn, YouTube, Substack, and blogs [TechCrunch, October 2026] [AI Weekly, October 2026]. SuperMe’s site also says the service works inside Claude, ChatGPT, Cursor, and other tools via MCP, and third-party coverage identifies MCP as Model Context Protocol, a standard for connecting chat interfaces to external tools and data contexts [SuperMe, October 2026] [it-boltwise.de, retrieved 2026]. Greylock, in its write-up on the company, said SuperMe had already supported more than 30,000 conversations across early product, growth, and marketing clusters as of 2026, which suggests the matching and response loop is seeing real use at an early stage, even if retention and monetization remain undisclosed [Greylock, retrieved 2026].
The public record points to a consumer-style product first, with monetization still taking shape. Coverage in October 2026 said the service was free at that time, with plans to add query limits and paid tiers later [TechCrunch, October 2026] [AI Weekly, October 2026]. On technical stack, the safest reading is narrow: the company is clearly building around conversational AI, expert matching, and MCP-based distribution, while any deeper stack description would be inference rather than reporting; separate hiring references to conversational AI and expert matching support that framing but do not justify more specific architecture claims [The Cryptonomist, October 2026] [Silicon Report, October 2026].
Partially corroborated -- Core product workflow is corroborated by SuperMe, TechCrunch, and Greylock, but several implementation details remain company-described or lightly corroborated by secondary coverage.
Market Research
Open sources
The market matters now because generative AI is making professional advice cheaper to access, while the supply of trusted, domain-specific judgment still sits with a relatively small group of operators, investors, and functional experts [TechCrunch, October 2026] [Spotify, March 2026].
The evidence base here is narrower than investors would usually want for a clean TAM read. No named third-party market report in the provided source set sizes a market specifically for AI-native expert networks, conversational professional advice, or AI profiles built from real experts' public output. The available reporting instead frames SuperMe as a product at the intersection of professional networking, knowledge search, and creator-style expert monetization, with the current wedge aimed at individual professionals and builders seeking advice rather than enterprise software buyers [Patrick Thompson, July 2026] [TechCrunch, October 2026]. That positioning suggests an emerging market definition rather than a settled one, which is often attractive at seed and Series A, but it also means market sizing remains more conceptual than measured on the public record [TechCrunch, October 2026].
Demand drivers are easier to identify than market size. Public coverage repeatedly describes a workflow where users ask career or work questions, and the system routes those questions to AI profiles trained on real experts' public materials, sometimes in a group setting that lets users compare viewpoints [TechCrunch, October 2026] [Mezha, October 2026] [Greylock, retrieved 2026]. That matches a broader shift in user behavior toward conversational interfaces for search and decision support, and it gives SuperMe a clear usage trigger: moments when a builder wants judgment, not just facts. Greylock said the product had already supported more than 30,000 conversations across early clusters in product, growth, and marketing, which is a useful early signal of engagement even if it does not establish retention or monetization [Greylock, retrieved 2026].
The adjacent markets are large, but the substitutes are strong. On one side, SuperMe touches professional networking and reputation graphs, where incumbents such as LinkedIn already aggregate identity, audience, and work history. On another, it overlaps with AI search and answer engines that help users synthesize public information, as well as expert networks and coaching products that monetize access to specialized human judgment. The practical question is whether users want a new destination for expert-mediated answers, or whether this behavior will be absorbed into general-purpose AI assistants, existing social graphs, or vertical communities. SuperMe's MCP integration language on its website suggests management is at least aware of that risk and is trying to distribute the product into the interfaces users already occupy, including Claude, ChatGPT, and Cursor [SuperMe, October 2026].
Macro and regulatory forces cut both ways. The positive side is that remote-first work, faster product cycles, and wider acceptance of AI copilots all increase the value of rapid access to experienced perspectives, especially for independent builders and lean teams [Spotify, March 2026] [Patrick Thompson, July 2026]. The constraint is provenance and consent: SuperMe's public descriptions say profiles are built from public materials such as LinkedIn, YouTube, Substack, and blogs, which brings questions around attribution, expert permission, accuracy of representation, and potential platform-policy changes if large content sources alter access terms or training norms [TechCrunch, October 2026] [AI Weekly, October 2026]. None of that is unique to SuperMe, but in this category trust is not a compliance footnote. It is part of the product itself.
| Market lens | Public evidence | Implication for SuperMe |
|---|---|---|
| Core category | No named third-party market size for AI-native expert networks in provided sources [TechCrunch, October 2026] | Category is emerging, so adoption evidence matters more than headline TAM for now |
| Demand signal | 30,000+ conversations across product, growth, and marketing clusters [Greylock, retrieved 2026] | Early usage suggests real curiosity around expert-mediated AI answers |
| Buyer starting point | Initial use appears oriented to individual professionals and builders rather than enterprises [Patrick Thompson, July 2026] [TechCrunch, October 2026] | Near-term go-to-market likely depends on consumer or prosumer behavior before larger team plans |
| Key substitutes | Professional networks, AI search, expert networks, and workflow-native copilots [SuperMe, October 2026] [TechCrunch, October 2026] | Differentiation must come from proprietary expert graph, answer quality, and distribution |
The table underscores the central public-market fact: this is a category-creation story more than a spreadsheet-sized market story at this stage. That can still work, but the burden of proof moves to repeat usage, expert supply density, and whether the product becomes habitual before larger platforms offer a similar experience.
Partially corroborated -- Market structure and demand drivers are supported by TechCrunch, Greylock, Spotify, Patrick Thompson, Mezha, AI Weekly, and the company website, but no independent third-party market sizing report is present in the provided sources.
Competition and Substitutes
MIXED SuperMe is not competing head-on with a single established software category so much as it is sitting between professional networking, expert search, and AI copilots for knowledge work, which makes the nearest alternatives more important than any one direct peer [TechCrunch, October 2026] [SuperMe, October 2026].
The clearest incumbent reference point is LinkedIn, not because the products appear interchangeable, but because LinkedIn already owns the identity graph for professional credibility and public work history at internet scale [SuperMe, October 2026]. If SuperMe builds AI profiles from public professional material such as LinkedIn, YouTube, Substack, and blogs, then part of its product value depends on organizing knowledge that is first published elsewhere, rather than originating inside a closed proprietary network [AI Weekly, October 2026]. Adjacent substitutes are also easy to see: ChatGPT and Claude already answer career and workplace questions in a conversational format, while search products and creator platforms already host expert content, even if they do not route users into a multi-expert discussion tied to named operators [SuperMe, October 2026] [Spotify, March 2026].
That leaves SuperMe in a challenger position defined less by category ownership than by workflow design. Coverage describes a product that routes a business question to relevant expert profiles, returns multiple points of view, and preserves some connection to real people rather than a generic model response [TechCrunch, October 2026] [Greylock]. The practical bet is that users want advice with provenance, not just fluency. If that holds, the company has an edge in curation and in founder-market fit: Casey Winters' product and growth background across Grubhub, Pinterest, Eventbrite, Greylock, and Reforge gives SuperMe access to dense networks of operators whose knowledge has market value, while Ludo Antonov's engineering history at Pinterest and Whatnot is relevant to building the matching and profile infrastructure behind the product [TechCrunch, February 2017] [Axios] [Casey Winters - LinkedIn] [TechCrunch, February 2022]. The durability of that edge is less clear. Founder access and early expert goodwill can help seed supply, but unless those experts contribute ongoing interactions that improve answer quality over time, the advantage is perishable and can be copied by better-capitalized platforms with larger user graphs [Greylock] [Silicon Report, October 2026].
The main exposure is distribution. LinkedIn can place advice, identity, and network interactions inside a product that already has habitual use across recruiting, posting, messaging, and profile maintenance, while general AI assistants control the interface where many users now ask work questions in the first place. SuperMe's own site says it works inside Claude, ChatGPT, Cursor, and other tools via MCP, which is useful for reach but also signals reliance on platforms it does not own [SuperMe, October 2026]. If those interfaces improve native expert search or recommendation layers, SuperMe risks becoming a data source or lightweight add-on rather than the destination. A second exposure is monetization discipline: public reporting said the service was free in late October 2026 with plans for query limits and paid tiers, so the company is still proving that differentiated expert answers can convert into durable paid behavior rather than episodic curiosity [AI Weekly, October 2026].
The most plausible 18-month scenario is a split market. LinkedIn is the likely winner if professional identity and expert discovery converge inside an incumbent graph, because it already owns the profile substrate and the audience habit. SuperMe is the likely winner if users decide that decision support needs attributable, multi-perspective answers from recognizable operators rather than single-model synthesis, and if the company can turn its reported 30,000-plus conversations into a compounding relevance loop that improves matching and answer quality over time [Greylock]. The most exposed loser if generic assistants absorb provenance and expert comparison into their native product could be standalone expert-answer networks as a group, with SuperMe included, because their clearest wedge would narrow from a distinct user experience to a feature layer inside larger AI surfaces [SuperMe, October 2026] [Spotify, March 2026].
Partially corroborated -- This section relies on public reporting from TechCrunch, AI Weekly, Silicon Report, Spotify, company materials, and founder background sources, but it contains analytical comparisons to unnamed platform alternatives without independently confirmed direct competitor lists.
Opportunity
PUBLIC
The prize here is not another advice chatbot, but a new professional knowledge layer that sits between search, mentoring, and workflow software, and if SuperMe executes, it could become the default interface for asking high-stakes work questions to a network of AI representations grounded in real operators' public knowledge [TechCrunch, October 2026] [Greylock, retrieved 2026] [SuperMe, October 2026].
The headline opportunity is to build a network business where the product gets better as both sides deepen: more experts create more useful answers, more user conversations create more reasons for experts to participate, and that in turn can support paid access, premium profiles, or workflow integrations over time [TechCrunch, October 2026] [AI Weekly, October 2026] [SuperMe, October 2026]. That outcome is still early rather than proven, but it is at least reachable from the current starting point because the company has raised a $20 million Series A led by Forerunner with Greylock participating, has founders with prior product and engineering leadership across Pinterest, Eventbrite, Grubhub, Lyft, and Whatnot, and has already supported more than 30,000 conversations in early clusters such as product, growth, and marketing [TechCrunch, October 2026] [SuperMe, October 2026] [TechCrunch, February 2017] [Axios] [TechCrunch, February 2022] [Greylock, retrieved 2026]. The underlying bet is that expert knowledge is fragmented across blogs, podcasts, LinkedIn posts, and internal work artifacts, and a service that can structure that knowledge into usable, conversational profiles may become habit-forming for professionals making decisions under uncertainty [AI Weekly, October 2026] [Spotify, March 2026] [SuperMe, October 2026].
A few paths to large-scale outcomes are visible even with limited public data.
| Scenario | What happens | Catalyst | Why it's plausible |
|---|---|---|---|
| Vertical authority network | SuperMe becomes the default place to ask career, product, growth, and operator questions, then expands into adjacent expert domains | A steady buildout of expert coverage and work groups beyond the initial product, growth, and marketing clusters [Greylock, retrieved 2026] | The current product already routes business questions to relevant expert AIs and compares viewpoints, which is a strong format for knowledge-dense functions where users want multiple perspectives rather than one answer [Greylock, retrieved 2026] [Mezha, October 2026] |
| Embedded expert layer inside AI workflows | SuperMe becomes infrastructure that users access from third-party AI tools rather than only through its own destination product | Broader adoption of its MCP-based access across tools such as Claude, ChatGPT, and Cursor [SuperMe, October 2026] [it-boltwise.de, retrieved 2026] | If professional users can call SuperMe from the AI surfaces where they already work, the company can capture usage without first winning a full social-network habit, which lowers distribution friction [SuperMe, October 2026] [it-boltwise.de, retrieved 2026] |
| Paid knowledge graph for professionals | SuperMe converts from free usage into a subscription network with tiered access, query limits, and higher-value expert interactions | Introduction of paid tiers after free adoption proves engagement and repeat use [AI Weekly, October 2026] | The service was reported as free in late 2026 with plans for paid tiers and limits, suggesting the company is using product adoption first and monetization second, a common motion for consumer and prosumer networks [AI Weekly, October 2026] [TechCrunch, October 2026] |
The compounding mechanism matters more than the current revenue line, which has not been publicly disclosed. Public evidence suggests a plausible flywheel already forming: experts contribute source material from public channels, SuperMe turns that into AI profiles, users ask questions, conversations generate signal on which profiles are useful for which queries, and that relevance can improve future routing and answer quality [TechCrunch, October 2026] [AI Weekly, October 2026] [Greylock, retrieved 2026]. Greylock described the product as Perspective Search that routes a business question to the most relevant experts and enables users to compare viewpoints, while LinkedIn posts from people associated with the company framed the network as one that improves as more conversations occur and more insights are reinforced or added [Greylock, retrieved 2026] [Savraj Singh - LinkedIn, retrieved 2026]. If that loop strengthens, SuperMe does not only gain content supply, it gains interaction data tied to intent, domain, and outcome, which is usually more defensible than a static library of expert content alone.
The size of the win depends on which behavior SuperMe ultimately owns. One useful public comparable is LinkedIn, which Microsoft acquired for $26.2 billion in 2016 to own professional identity, graph, and distribution [Microsoft, June 2016]. SuperMe is not competing with LinkedIn on broad resume identity today, but if it became a meaningful professional decision layer, with destination usage plus embedded workflow access, the upper-end outcome could support a multibillion-dollar enterprise value, perhaps in the low single-digit billions (scenario, not a forecast), especially if paid access and tool integrations convert knowledge interactions into recurring software-like revenue. A more conservative analogue is the modern expert-network and prosumer-subscription stack: if SuperMe proves that thousands of experts can support millions of work questions with strong retention, the company could occupy a category that traditional expert networks never reached because their service model did not scale conversational access [TechCrunch, October 2026] [SuperMe, October 2026] [Spotify, March 2026]. That is the core upside case. The company is trying to turn scarce professional judgment into software.
Partially corroborated -- This section relies on a mix of independent reporting, company materials, and investor commentary; the major funding and product-direction claims are corroborated, but monetization and compounding remain early and only partially evidenced.
Sources
Open sources
[TechCrunch, October 2026] SuperMe raises $20M for its network of AI expert profiles | https://techcrunch.com/2026/10/29/superme-raises-20m-for-its-network-of-ai-expert-profiles/
[aVenture Company Research] SuperMe funding research profile | https://www.aventure.vc/
[StartupHub.ai] SuperMe company profile | https://www.startuphub.ai/
[SuperMe, October 2026] SuperMe | Think it through with people who’ve been there | https://www.superme.ai/
[Greylock] SuperMe | https://greylock.com/
[AI Weekly, October 2026] SuperMe Raises $20M to Simulate Experts in AI Group Chats | https://aiweekly.co/alerts/superme-raises-20m-to-simulate-experts-in-ai-group-chats
[TechCrunch, February 2017] Greylock hires a growth advisor in residence to help goose its portfolio companies | https://techcrunch.com/2017/02/09/greylock-hires-a-growth-advisor-in-residence-to-help-goose-its-portfolio-companies/
[Axios] 2. Gates: Breaking up Big Tech not the answer | https://www.axios.com/newsletters/axios-login-f051d4ed-da1a-499d-9f2a-ad326512728f.html?chunk=1
[TechCrunch, February 2022] Whatnot valuation doubles to $3.7B amid livestream shopping boom | https://techcrunch.com/2022/02/16/whatnot-series-d/
[Casey Winters - LinkedIn, retrieved 2026] Casey Winters | LinkedIn | https://www.linkedin.com/in/caseywinters/
[PitchBook] SuperMe company profile | https://pitchbook.com/
[Silicon Report, October 2026] SuperMe raises $20M in funding led by Forerunner | https://www.siliconreport.com/superme-raises-20m-in-funding-led-by-forerunner
[The Cryptonomist, October 2026] SuperMe AI Expert Network Raises $20M for Expansion | https://en.cryptonomist.ch/2026/10/29/superme-ai-expert-network/
[Mezha, October 2026] SuperMe raises $20M to build a network of AI expert profiles | https://mezha.net/eng/news/mezha-ff158af3f95be268c57c3b81/
[it-boltwise.de, retrieved 2026] Model Context Protocol overview | https://www.it-boltwise.de/
[Spotify, March 2026] How SuperMe is Building the Professional Network for the AI Era | https://open.spotify.com/episode/0cByw6jQnxFzvJpOiOaSd0
[Patrick Thompson, July 2026] Investors fund founders, not decks | https://patrickthompson.substack.com/p/investors-fund-founders-not-decks
[SuperMe, October 2026] SuperMe Raises $20M to Bring Experienced Builders into Every Decision | https://www.superme.ai/blog/series-a
[Savraj Singh - LinkedIn, retrieved 2026] Savraj Singh | LinkedIn | https://www.linkedin.com/
[TechCrunch, November 2023] 20GROWTH secures $5M to invest in early-stage startups, provide growth strategies | https://techcrunch.com/2023/11/06/20growth-5m-venture-capital-startups/?trk=public_post_comment-text
Articles about SuperMe
- Casey Winters' SuperMe Has Indexed 30,000 Conversations in Its AI Expert Network — The startup, founded by ex-Pinterest operators, is betting that professionals will pay to query AI profiles trained on public content from real VCs and executives.