Nyne raised $5.3M to scan 250 million websites and tell AI agents who you really are

“How do I know you’re pregnant and sell you A, B, or C as early as possible?”

That’s not a line from a dystopian novel. It’s how Nichole Wischoff, general partner at Wischoff Ventures, described the value proposition of Nyne — a startup she helped fund to the tune of $5.3 million in a seed round announced on March 13, 2026.

Nyne wants to become the identity and intent layer for AI agents. The pitch: today’s agents can book flights, draft emails, and manage calendars, but they have no idea who you actually are beyond what you type into a chat window. Nyne aims to fix that by stitching together your public digital footprint — your Instagram posts, your Strava runs, your SoundCloud playlists, your LinkedIn job title — into a unified “people graph” that agents can query in real time.

It’s an ambitious bet on where the agentic AI stack is heading. It’s also the kind of idea that makes privacy advocates reach for the panic button.

The Context Problem AI Agents Can’t Solve Alone

Here’s the core issue Nyne is tackling: AI agents are increasingly capable at executing tasks, but they operate with minimal understanding of the humans they serve.

As founder Michael Fanous puts it: “Machines can read a LinkedIn headline, but they can’t yet tell if the same person also runs a Strava club, posts acoustic guitar covers on SoundCloud, and just signed up for a marathon.”

That fragmentation matters. An AI shopping agent that doesn’t know your fitness interests will recommend generic products. A customer service agent that can’t connect your social media activity to your support ticket treats you like a stranger every time. The Agent Action Protocol addressed agent-to-tool communication, and companies like Atlassian are deploying AI agents directly inside Jira. But the agent-to-human understanding layer — the part where an agent actually knows who it’s serving — is still largely missing from the stack.

Nyne positions itself as that missing layer. Not another agent framework, but the data infrastructure that makes existing agents smarter about people.

How Nyne Builds Its “People Graph”

The technical approach involves three components working together.

Digital Footprint Aggregation. Nyne deploys millions of lightweight software agents across the internet to scan over 250 million public websites and data sources. These include major platforms like Instagram, Facebook, X, and LinkedIn, along with niche services like SoundCloud, Strava, GitHub, forums, review sites, and public government records.

Identity Resolution Engine. This is where the ML muscle comes in. Nyne uses proprietary transformer models and graph-based reasoning to triangulate whether scattered data points belong to the same person. Someone’s LinkedIn profile, their Strava activity, and their SoundCloud account might share no common username — but overlapping location data, timing patterns, and behavioral signals can connect them. The output is a structured knowledge graph with metadata including timestamps, confidence scores, evidence sources, inferred interests, and relationship networks.

Real-Time API Layer. Developers access the people graph through API endpoints for real-time queries. Agents can ask things like “Is this user likely interested in a fitness app?” or “What life events has this person recently experienced?” The system infers intent and preferences — not just static profile data — which is what separates Nyne from a simple data aggregation play.

The company frames this as a three-layer AI stack: foundation models at the bottom, agent platforms in the middle, and context infrastructure — where Nyne lives — at the top. Without that context layer, agents are powerful but blind.

A Father-Son Startup With Nearly $1M in Revenue

Nyne was co-founded by Michael Fanous and his father Emad Fanous — an unusual founding dynamic in a startup world dominated by college roommate pairings and Stanford MBA duos.

Michael, the CEO, studied computer science and data science at UC Berkeley before working as a machine learning engineer at CareRev, a healthcare workforce platform. His father Emad serves as CTO, bringing over 15 years of enterprise-scale system design experience, including a stint as CTO at a series-C fintech company specializing in large-scale data ingestion and real-time analytics.

The father-son dynamic, according to Michael, is actually an advantage: “If I have to message him at three in the morning to finish a launch, I know he’s still going to love me the next day.”

The team is small — roughly 9 employees — but the traction is real. Before even announcing its funding, Nyne was generating nearly $1 million in annual recurring revenue. For a seed-stage startup, that signals genuine commercial demand rather than just a compelling pitch deck.

The $5.3 million seed round was co-led by Wischoff Ventures and South Park Commons, with participation from angel investor Gil Elbaz — the co-founder of Applied Semantics and a pioneer behind Google AdSense. Elbaz built one of the earliest systems for understanding web content at scale, and his investment suggests he sees a similar structural opportunity in identity data for the AI era.

South Park Commons noted that Nyne aligns well with “agent-first” applications — a world where bots negotiate, purchase, and make decisions on behalf of users. That world is arriving faster than most expected, and the infrastructure gap Nyne is filling looks increasingly urgent.

The Privacy Elephant in the Room

Let’s not dance around it: a company that deploys millions of agents to scrape public data across 250 million websites and stitch it into unified personal profiles raises significant privacy questions.

The Wischoff quote about identifying pregnant women for early-stage targeting is, to put it mildly, not going to calm anyone’s nerves. It echoes the infamous Target pregnancy-prediction story from over a decade ago — except now it’s powered by AI agents rather than purchase history algorithms, and the data sources span every corner of someone’s online presence.

Research on LLM-powered deanonymization has already shown how AI can strip away online anonymity at scale using publicly available information. Nyne’s technology essentially productizes a similar capability — with guardrails, but still.

To its credit, Nyne says it’s building for GDPR and CPRA compliance from day one. The company points to several safeguards:

Consent management and opt-out workflows
Data minimization practices
PII hashing and tokenization
Role-based access controls
Audit logs documenting which signals informed each attribute

The company’s position is that its differentiation lies in explicit permissions and utility for “agent decisioning” — not ad targeting — and in making confidence scores and data provenance first-class citizens, so enterprises can enforce their own risk thresholds.

Whether that’s sufficient depends on your perspective. The real test will come when a Nyne-powered agent makes a decision based on inferred personal data that a user didn’t explicitly share. The company will need to demonstrate that its consent and transparency mechanisms hold up under that kind of scrutiny — especially as regulators pay closer attention to the AI agent ecosystem.

Where Nyne Fits Against the Competition

Nyne isn’t building in a vacuum. Several categories of competitors overlap with what they’re doing, each from a different angle.

Traditional data enrichment platforms like Clearbit (now part of HubSpot), ZoomInfo, and Apollo.io have offered contact enrichment APIs for years. But these tools were built for CRM-driven sales workflows — structured databases of job titles, company sizes, and email addresses. Nyne argues its graph-based identity resolution and behavioral inference capabilities go well beyond firmographic lookups. The difference: ZoomInfo tells you someone is a VP of Engineering; Nyne tells you they also run ultramarathons and are active in open-source Rust communities.

Agent memory systems take a complementary approach. Projects like Hindsight by Vectorize give agents persistent memory based on past interactions, mimicking how human long-term memory works. This solves a different problem: Hindsight remembers what happened in previous conversations, while Nyne provides external context the agent never had in the first place.

Major AI platforms from OpenAI, Google, and Anthropic are building their own agent ecosystems. These platforms currently lack a dedicated identity resolution layer, which is exactly the gap Nyne is filling. But the risk is obvious: if any of these companies decide to build that layer in-house, Nyne’s independent position gets much harder to defend.

The estimated market for contextual data services is projected to exceed $10 billion by 2030. If AI agents become as ubiquitous as current trends suggest — with everyone from Stripe running 1,300 AI-generated pull requests per week to enterprises deploying agents across entire workforces — every deployment will eventually need some version of “who is this person” infrastructure. Nyne’s bet is that they can own that layer before the incumbents get around to building it.

With $5.3 million in the bank and nearly $1 million ARR already in hand, Nyne has an estimated 18-24 months of runway to prove that bet right. The target verticals — sales automation, personalized e-commerce, recruiting, fintech onboarding, and customer experience — are all spaces where knowing more about the user translates directly into revenue. Whether the market embraces or recoils from that level of AI-powered personal insight will likely determine whether Nyne becomes infrastructure or a cautionary tale.

Frequently Asked Questions

What is Nyne and what does it do?
Nyne is a data infrastructure startup that builds identity and intent profiles for AI agents. It scans over 250 million public websites and data sources — including Instagram, LinkedIn, X, Strava, and SoundCloud — and uses machine learning to link scattered digital footprints to the same person, creating a real-time “people graph” that AI agents can query through APIs.

How much funding has Nyne raised?
Nyne closed a $5.3 million seed round in March 2026, co-led by Wischoff Ventures and South Park Commons. Angel investors include Gil Elbaz, co-founder of Applied Semantics and a pioneer behind Google AdSense. The company was already generating nearly $1 million in annual recurring revenue before announcing the round.

Is Nyne’s data collection legal and privacy-compliant?
Nyne says it only collects publicly available information and is building for GDPR and CPRA compliance from day one. Safeguards include consent management, data minimization, opt-out and deletion workflows, PII tokenization, and audit logs. However, the legal and ethical boundaries around large-scale public data aggregation and identity resolution remain actively debated across the industry.

Who are Nyne’s main competitors?
In the data enrichment space, competitors include Clearbit (HubSpot), ZoomInfo, and Apollo.io for sales intelligence. In the AI agent context layer, Nyne also competes with agent memory systems and — potentially — with identity features that major AI platforms like OpenAI, Google, or Anthropic could build into their own agent ecosystems. Nyne differentiates by focusing specifically on real-time identity resolution and behavioral inference for AI agents.

What industries can use Nyne?
Primary use cases include sales automation (enriching outreach with full prospect profiles), personalized e-commerce (agents that understand customer preferences across platforms), recruiting (matching candidates based on full digital presence), fintech onboarding (faster KYC through public data triangulation), and customer experience automation (agents that recognize and adapt to individual users).

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