EMILY.ai

The first AI-enabled services company to replace the PR agency, from strategy to publication, with press outcomes and insights humans can't match.
Pre-seed traction: ~$187K in 12 weeks.

Law had Harvey. CS had Sierra. Now it's time for PR.

“The next $1T company will be a software company masquerading as a services firm.”

Sequoia Capital
March 2026 ↗

02/Team

EMILY.ai · Pre-seed

15 years in PR. Felt every friction. Now building the platform that replaces us.

David Malits

CEO & Co-founder

linkedin.com/in/dudi-malits-59823531

Founder/CEO for 15 yrs of a leading Israeli global tech-PR agency; served 100s of clients across US / EU / APAC, from seed-stage to public enterprises
Trusted partner to global leaders incl. Sony Semiconductor, Deutsche Bank, SentinelOne, PepsiCo Labs, NAYAX

Lena Katz

CPTO & Co-founder

linkedin.com/in/lenakatzil

VP R&D / CTO-tier roles across AppsFlyer, Gett, and Selina. Ex-8200
Built and ran engineering, product, UX, big data, and AI/Gen AI teams, from a small core to ~100, from hyper growth to IPO

Adir Alon

VP Customer Success & Co-founder

linkedin.com/in/adiralon

9 yrs of global PR. US / EU / APAC publications
Thousands of articles across all tiers - Business Insider, Forbes, TechCrunch, Bloomberg, Reuters and more - covering the biggest tech beats: AI, cybersecurity, fintech, enterprise SaaS, and cloud/infra

Clients we've worked with

Featured in

03/Our Story

EMILY.ai · Pre-seed

For 15 years we ran PR for clients from startups to global enterprises across the US, EU, and APAC. 10,000+ reporters we've engaged personally, and the data behind every exchange.

What stopped us from scaling was always the human factor. So we decided to turn the agency into a product, and joined forces with Lena - ex-8200, 20 years building AI systems at scale - to build a firm that runs PR end-to-end, where data-driven insights and prediction maximize press coverage.

Unsure clients would buy, we offered it to some of those who approached us. They bought it, at our highest close rate ever: 60%+.

And when we're ready to scale: 6,000+ warm contacts, already waiting to enter the pipeline.

04/Problem

EMILY.ai · Pre-seed

PR is one of marketing's most important, and most outdated, services.

A black box: you wait & hope

Quality and results are unpredictable. You never know how, where, when, or if coverage lands.

Your story goes into a black box, coverage is a question mark

Agencies don't match your story and voice per reporter. Results aren't maximized.

Burns your time and budget

Weeks of meetings, drafts and sign-offs on your calendar, plus a monthly invoice.

You pay for effort, not results.

Gut-driven, inconsistent operators

A human-centric industry with no data or AI skills. Turnover shakes results, and each new market means another agency.

Your results depend entirely on whoever runs your campaign.

A bad customer experience, yet every company still buys it.

05/Solution

EMILY.ai · Pre-seed

This is how EMILY solves it.

Watch the product demo↗

Full visibility: improved until it's news

Every press release is generated, evaluated, and tuned until it's newsworthy, in ~80% less time.

Press release evaluation

DRAFT 1

FINAL

+14Add a financial hook: 8 of 10 priority reporters open stories with numbers.

+8Reframe as geo-expansion: the story your matched reporters actually cover.

Data-driven insights: how, when, and where to land coverage.

The right story, per reporter's appetite

Deep company and reporter profiling keeps your voice and matches your story angles to each reporter's interests.

Reporter match

Claire Dawson

BLOOMBERG · TIER 1

MATCH · 89

Beat overlap91

Topic coverage903 articles · 4 mo

SentimentNEUTRAL

Competitor coverage83

Why & how: she owns funding milestones on your beat; pitch your EU expansion, she already covers your competitors' moves.

Maximized coverage, reporter by reporter.

Consistent results: any scale & market

Agentic AI systems trained on proprietary PR data, self-improving by learning from each campaign, intent → results.

Reporter matching accuracy

91%goal · 95% across 138,961 reporter interactions

Worked: high-score match → Maria Silva (Folha). Published in 4h. Keep weighting. ✓ APPROVE✗ REJECT

Didn't: high score, no reply → David Chen (TechCrunch). Beat changed. Add 30-day refresh. ✓ APPROVE✗ REJECT

Built, trained, and orchestrated by PR professionals.

Above-human results, paying for outcomes: the experience PR customers never had.

06/Traction

EMILY.ai · Pre-seed

PR's AI moment finally arrived.

We started offering the new PR product to companies that approached us, and they bought it at our highest close rate ever. 6,000+ warm contacts sit in the pipeline, ready to enter the moment we scale.

Paying clients

Closed in 12 weeks (Feb → Apr 2026).
Mid-market & enterprise tech, US + Europe + Israel.

Signed revenue

$187K

$164K ARR from 5 retainers
+ $23K from 3 project contracts

Close rate

60%+

Real paying customers.
Not pilots, not future contracts.

Marketing spend

Zero marketing budget, founder-led GTM.

Warm contacts

6,000+

Relationships built over 15 years in PR - people we know directly and can convert to clients.

Initial customers include

Outpost24 NAYAX BI Science Sony Semiconductor

07/Market

EMILY.ai · Pre-seed

A $120B PR market, growing 7–10% a year through 2030.

$120B

$31B

$6.3B

$2.4B

Total global PR [TAM]→ 2030

1.5–2M firms

GrowthYear 5+

12 countries · 7 industries

~205K firms

WedgeYear 2–5

(US + EMEA) · (Tech + Consumer)

~33,800 firms

BeachheadYear 0–2

US + UK · Tech

~9,000 firms

SOM

Starting from existing warm connections; ready to hyper-scale after the autonomous milestone.

See the bottom-up market calculations in the appendix.

08/Moat

EMILY.ai · Pre-seed

Business × Technical

Business Advantage

PR expertise · customer trust · traction · outcome-based pricing

Technical Advantage

2 agentic trained systems (Customer & Back-office) · prediction algorithms · proprietary, compounding PR data · enterprise security guardrails

Access & Foundation - 15 yrs, ~250 clients, 10K+ reporters

Proprietary Data - 5,000+ releases, ~180 signals each

Network Effect - each press release and each client sharpens all

Workflow Embedding - leaving means losing your data: back to paying for effort, not results

Anti-Thin-Wrapper - agentic systems & algorithms, trained by PR pros on PR data

Incumbents & New Entrants

IncumbentsCan't break their model

◆Agencies - wrong DNA: automate the old, can't reinvent it

◆PR-tech suppliers - can't compete without cannibalizing customers & P&L

◆Wires - no agency scope; serve IR/distribution

◆Other automations - sell to agencies, lack the judgment to replace one

New EntrantsAI-native · start at zero

◆No trust or live customers - ours earned over years

◆No PR dataset - ours: 15 yrs · 5,000+ releases

◆No analytical expertise to codify playbooks into a system

◆No learning loop - ours sharpens with every customer

See the full breakdown in the appendix.

09/Competition

EMILY.ai · Pre-seed

AI PR Agency EMILY · Newsbound

PR Co-pilot Honeyjar · MVPR · Shadow

See the full mapping in the appendix.

10/Business Model & Unit Economics

EMILY.ai · Pre-seed

Products

Concierge ● Live · Y1

AI + PR professional support (HITL)

~$84K ACV~$7K/mo, avg.

Differentiates by company tier: SMB → Enterprise · $2.4K–$13.4K monthly.

Express ○ Launches Y2

Fully autonomous

~$54K ACV~$4.5K/mo, avg.

Differentiates by company tier: SMB → Enterprise · $1.75K–$9.6K monthly.

Journalists ○ Launches Y3

Journalist demand - Marketplace Ready

Consumption-based

year three, scale unlocks a two-sided marketplace.

Per-result fee on both Concierge and Express products. Unlocks ~Y2 with prediction readiness; total spend stays similar.

Buyer

Main buyer

In-house marketing leaders and founders - companies already running PR through an agency retainer.

Untapped buyer (Opportunity market)

Companies that couldn't afford PR until now.

Gross-margin path - HITL shrinks, margin grows 30% → 65% → 80%

Gross margin

Y1 · MVP launch

30% GM

Y2 · Post-MVP

65% GM

Y3+

80%+ GM

See the elaborated unit economics in the appendix.

11/The Ask & Use of Funds

EMILY.ai · Pre-seed

Raising $2.5M to reach >$1M ARR · 20 paying clients by the end of year 1.

- Where the money goes 5 buckets

R&D & Product 59% $1.48M

GTM & Sales/Marketing 17% $425K

AI Infra & Tools 13% $325K

Ops / G&A / Legal 4% $100K

Buffer 7% $175K

This round is focused on building the technology, data flywheel, and industry-specific training. Each one lowers the HITL, unlocking scale.

Year 1 → 2

Focusing on reaching a fully autonomous system at >95% accuracy; the last 20% of accuracy and autonomy is the hardest milestone. In parallel, building the operation to onboard 60 customers.

- Roadmap

Q3 2026 · ✓ done

Pre-raise foundation built

8 paying B2B clients - contractually bound to deploy every new AI tool
Prototype agents live · MVP design done

YOU ARE HERE →

Q4 2026

Round closed, R&D staffed

Design partners locked in
35% HITL reduction

Q1 2027

Build & train the systems

HITL reduced >50% · >80% autonomous accuracy
Onboarding new customers

Q2 2027

Scale & refine

HITL reduced >65% · >80% autonomous accuracy
Onboarding new customers

YEAR 1 →

Q3 2027 · ★ seed-ready

MVP GA · >80% HITL reduction · >80% autonomous accuracy

Seed-ready: 20 paying clients · $1M ARR
Multi-agent system in production · flywheel turning · partially autonomous capabilities

12/Agency PR Work Breakdown

EMILY.ai · Pre-seed

Beachhead · Y0–Y2 · % = share of effort

EMILY autonomy · today → Y1 → Y2Autonomous todayWithin year 1Within year 2

Onboarding, Profiling & Planning

Company media profiling & positioning · 13% effort

Goals, sensitivities & PR plan · 9% effort

Reporter & outlet digital twins · 10% effort

Create & Score

Story & angle ideation · 8% effort

Press release, interview & content generation · 15% effort

Newsworthiness scoring & improvement · 11% effort

Story ↔ reporter matching · 10% effort

Distribute & Measure

Personalized pitching & outreach · 11% effort

Two-way engagement, reporters & clients (correspondence · requests) · 8% effort

Monitoring, reporting & analytics · 5% effort

Blended · Beachhead · 100% effort

Wedge · Y2–Y5

Crisis & reactive PR

Two-sided marketplace

GEO, AI-search visibility

Long-tailMarket expansion beyond PR to the $120B house, IR · internal communications · social media

85–90%

async & digital agency work, not lunches

86%

press releases rejected for irrelevance, not weak relationships

93–96%

journalists prefer email over calls or in-person

−50%

newsroom jobs since 2008, the relationship layer is shrinking

Source: PR-workflow automation research, 2026 (Cision / Muck Rack / CIPR / Propel) · weighted across activities

13/Why Now

EMILY.ai · Pre-seed

Adoption

Enterprises that once banned AI now chase it.

Moment

The PR market is growing as LLMs cite news (GEO) and audiences reject fake content. PR becomes the trust layer.

Technology

Only now are models smart, fast, and conversational enough to build an agentic, autonomous PR system.

Opportunity

A $120B industry stuck in the past is about to be reinvented, and we're the team to do it.

14/Hard Questions

EMILY.ai · Pre-seed

The questions sharp investors ask

and our straight answers

The questionOur answer

Q1"Isn't PR a relationship business you can't automate?"

PR isn't relationship-based, it's quality-based, especially when one release goes to hundreds to thousands of reporters, and in the PR market 86% of pitches are rejected for irrelevance. EMILY only pitches relevant reporters and gates each story on a newsworthiness threshold (slide 33); the relationships that matter are held by EMILY's PR professionals, David and Adir (slide 32).

Q2"Too much human-in-the-loop, isn't this just a services business?"

Human-in-the-loop is real today, but it falls fast on our roadmap (slide 11): EMILY is ~25% autonomous now, >80% by year 1, >95% by year 2 (slide 12). Humans stay in the loop for controls, orchestration, and training; margin moves from agency to software as autonomy climbs (slide 10).

Q3"This just automates the press release, it's a point tool."

The press release is 15% of the work. EMILY runs the whole agency: profiling, media lists, targeting, pitching, follow-up, monitoring, ~25% automated today, >95% within two years. The press release is the part you saw; the product is the end-to-end workflow (slide 12).

Q4"Reporters distrust AI pitches, and can't anyone do this with ChatGPT?"

Generic AI spam is the problem, and our moat. Anyone can pitch one reporter; each release needs hundreds to thousands engaged one by one, every angle tailored yet true to the client's story. EMILY closes the full loop, what the client wanted and what actually published, sharpening on 15 years of reporter data with every release. You can't prompt that.

Q5"Why is this the team that wins a competitive category?"

Nobody else has all three: 15 years of proprietary reporter data, PR-native operators who lived the problem, and a top-grade AI engineering leader who has built and scaled tech organizations. Proof: $200K in 12 weeks, zero marketing, highest close rate ever, then we stopped, at capacity.

Q6"Why not build the marketplace, the Uber for PR?"

We do, just not a pay-to-publish exchange. EMILY rewires today's one-sided push into a two-sided marketplace matching story supply to reporter demand, operated by PR professionals. It unlocks in year 2-3, once hundreds of customers are live and supply is liquid enough to match at scale (slide 36).

Q7"Agencies run at ~15% margins, why will yours be a software business?"

Because the cost structure inverts. Per $100 of work, an agency spends ~$62 on delivery labor; EMILY spends ~$7, taking gross margin from ~37% to ~80% and EBITDA to ~50%, at ~$700K revenue per employee vs. $248K (slide 17). Even at a 50% price cut to win share, we stay software-grade.

15/Appendix

EMILY.ai · Pre-seed

Supporting material

Appendix.

16/Business Model & Unit Economics 2

EMILY.ai · Pre-seed

Per-tier pricing - what each segment will pay at the market penetration phase vs. what they currently pay

Wedge · Tech Industry Customers	Old school agencies	EMILY.ai's Concierge (−30%)	EMILY.ai's Express (−50%)
Pre-seed / Seed	$42K/yr $3.5K/mo	$29K/yr $2.4K/mo	$21K/yr $1.75K/mo
Series A	$95K/yr $7.9K/mo	$67K/yr $5.6K/mo	$48K/yr $4K/mo
Series B	$120K/yr $10K/mo	$84K/yr $7K/mo	$60K/yr $5K/mo
Series C / Growth / Big privates / Smaller publics	$230K/yr $19K/mo	$161K/yr $13.4K/mo	$115K/yr $9.6K/mo

Buyer

Main buyer

In-house marketing leaders and founders - companies already running PR through an agency retainer.

Untapped buyer (Opportunity market)

Companies that couldn't afford PR until now.

Why they pay

Quality 01

From gut feeling and operator-dependent → forecastable results powered by big-data prediction

Time 02

From 2–2.5 weeks per press release → ~1 week (50–60% faster)

Price 03

From paying for vague work → a hybrid model: per seat for strategy and media profile, per result for coverage

Continuity 04

From a single point of failure (employee turnover on either side = drop) → a system that retains the full media profile

Global reach 05

From a patchwork of regional agencies → regulations and control under one roof

17/Business Model & Unit Economics 3

EMILY.ai · Pre-seed

Traditional agency vs. EMILY: economics per unit of work (market price $100)

Per unit of work	Traditional Agency	EMILY at maturity	EMILY -50% penetration
Delivery automation	0-30%	~100%	~100%
Price to client	$100	$100	$50
Delivery labor	$62.50	$7	$7
Inference + platform	~$0	$10	$10
Other services	-	$3	$3
Total delivery cost	~$62.50	$20	$20
Gross margin	~37%	~80%	~60%
Overhead / SG&A	~$22	~$30	~$30
EBITDA margin	15.3%	~50%	~0% (breakeven)
Revenue / employee	$248K	~$700K	~$350K

Gross-margin path: Y1 · 30% → Y2 · 80%+

18/Market · Bottom-Up Math

EMILY.ai · Pre-seed

A $120B agency market,growing 7–10% a year through 2030.

BEACHHEAD

Year 0 – 2
US + UK, Tech Industry

~9,000 firms × $42K – $1.26M ACV = $2.4B

ACV ranges from $42K (early-stage startups) to $1.26M (large enterprises). Average ACV: ~$270K (smaller firms outnumber larger ones). Excludes top Fortune 500 - deferred Y3+.

$2.4B

WEDGE

Year 2 – 5
US + EMEA, tech + consumer

~33,800 firms × $39K – $1.26M ACV = $4.5B + $0.4 – 1.8B opportunity

~9K US tech ($2.4B) + ~16.3K UK tech ($0.9B) + ~8.5K US consumer ($1.2B). Avg ACV ~$133K. Excludes Fortune 500 + FTSE 100 + top CPG - deferred Y3+.

$4.5 – 6.3B

Incl. Opportunity $4.9 – 6.3B

GROWTH

Year 5+
12 countries, 7 industries

~205K firms × $14K – $465K ACV = $24B + $1.4 – 6.8B opportunity

12 countries × 7 industries. Avg ACV ~$118K. Top tier (Fortune 500 etc.) included in some industries, deferred in others - see appendix for full breakdown.

$24 – 31B

Incl. Opportunity $25 – 31B

OPPORTUNITY · SOM · VISION

Opportunity market = a new market on top of the existing one: smaller firms (20–99 employees) that need PR but can't afford agency retainers. EMILY.ai's pricing unlocks them.

SOM

Y1 / Y2 / Y5

YEAR 1

~20 customers

YEAR 2

~80 customers

YEAR 5

~5,000 customers

Market growth: $120B global PR market · +5% in 2024 · projected 7–10% CAGR through 2030 · fueled by GEO, AI training data, and the collapse of digital trust.

Sources - $120B: Statista, IBISWorld · +5%: PRWeek · 7–10% CAGR: Grand View Research

19/Business Expansion Models

EMILY.ai · Pre-seed

Future Expansion Models.

Alongside organic growth: ways to accelerate and enter additional markets.

CUSTOMER EXPANSION

Acquire or partner with PR agencies

Agencies bring an instant book of paying clients and decades of credibility - plus a fast way into new markets and industries beyond the Beachhead and Wedge.

TARGETS

Mid-market & boutique agencies
- e.g. a UK healthcare specialist

REPORTER ENGAGEMENT

Grow and acquire the reporter graph

A live graph of who covers what - and how each reporter wants to be pitched. It grows each time we pitch a reporter; acquisitions extend its global reach and accuracy.

EXAMPLES

Roxhill (UK/EU reach) · JournoFinder
(US, multi-vertical) · Qwoted (live pitch signal)

PR DATA FOR TRAINING

Acquire or partner with PR data sources

Structured news and PR datasets train the models behind newsworthiness scoring and press-release generation - a fast way to accelerate and sharpen the algorithms.

SOURCES

News & PR data providers,
e.g. Event Registry

20/Competition · Tech, Agencies, Tools

EMILY.ai · Pre-seed

AI PR Agency EMILY · Newsbound

PR Co-pilot Honeyjar · MVPR · Shadow

Big Agencies Edelman · Ketchum

Smaller Agencies Bospar

Wire Services PR Newswire · Business Wire

21/Competition Comparison

EMILY.ai · Pre-seed

PR isn't just operations. It's the strategy and judgment to predict what reporters and clients will say yes to - and what they won't. Without it, a system is an empty shell; competitors don't have it.

EMILY.ai

Newsbound

MVPR

Shadow

Honeyjar

In PR since

2026 · agency since 2010

2025

2020

2024

2025

Founders

PR + tech, 15 yrs

almost none

ex-Edelman

not from PR

comms, not owner

Trust & record

enterprise, earned

logos unverified

strong pedigree

borrowed logos

pre-launch

Prediction and insights

predicts what lands

none

keyword-level

generic listening

"human part"

End-to-end

full cycle + data

one direction

cycle, no data

parts, no judgment

human-driven

Quality

client + reporter

DIY-level

human, not data

below agency-grade

capped at operator

Price *

premium by design

low (thin)

ordinary

low (thin)

~$250/seat

Pricing model

outcome-based

not outcome-based

Autonomy

by design

partial

none

human oversight

copilot only

Ability to scale

scalable, AI-native

automation-limited

human agency, capped

tool, partial end-to-end

SaaS, scales

Product–market fit

selling, high close

tool for pros

services sell

speaks to experts

NewsboundAutomation/SaaS wrapper, not a PR partner - founders have almost no PR experience, so there's no data, deal flow, or judgment.

MVPRReal ex-Edelman agency with genuine client trust, but human-capped - no learning-data engine and not autonomous.

ShadowAutomation shop that pivoted to selling a tool to PR agencies - and like Honeyjar, targets the far smaller PR-expert market (~$5B vs $120B).

HoneyjarCopilot for comms pros that addresses the far smaller PR-expert market (~$5B vs $120B); pre-launch, no paying clients.

Strong Partial Weak Absent

* Low price signals a thin product, not an advantage.

22/Moat 1 · Detailed

EMILY.ai · Pre-seed

Layer 1

Access & Foundation · 15 years running a PR agency - trusted by startups, enterprises, and top-tier outlets. ~250 customers and communication patterns from 10,000+ reporters, with strong traction in our ICP: tech-industry PR.

Layer 2

Proprietary PR Agency Data · 5,000+ press releases, coverage in ~500 media outlets, across ~30 verticals, plus the playbooks and processes behind them. Every campaign captures end-to-end data unique to us: customer media sensitivities, press release newsworthiness scores, reporter preferences, campaign outcomes vs. goals, and ~180 additional PR signals - the dataset deepens with every release shipped.

Layer 3

Network Impact · Each system-customer interaction has a cross-client effect. It improves overall insights and newsworthiness prediction, industry press intelligence, press release pattern understanding, and reporter profiling - allowing each client to benefit from the others, under hardened security controls.

Layer 4

Workflow Embedding · When a client walks away, it loses its data and goes back to paying for effort instead of results. Gone: its media profile, what works with each reporter and across its industry - and what doesn't, and the predictions that let us guarantee results and charge per outcome. It downgrades by reverting to people with partial data who keep turning over, facing a re-learning curve of over six months on average, at a cost of tens to hundreds of thousands of dollars.

Layer 5

Anti-Thin-Wrapper · Between us and an LLM stand three layers: (1) a system trained on proprietary PR datasets - insights and algorithms that predict what passes each reporter, visible only to us, among other compounding models; (2) an end-to-end agency Agentic Operating System, with HITL corrections; (3) an end-to-end client Agentic Operating System. See the “Moat 2 · Tech” diagram.

23/Moat 2 · Tech

EMILY.ai · Pre-seed

With each interaction,
every brain compounds.

23/Moat 3 · Incumbents & New Entrants

EMILY.ai · Pre-seed

Two ways into the market - both structurally blocked.

IncumbentsCan't break their model

Agencies (legacy or AI-equipped) - wrong DNA: incumbents rarely think outside the box; at best they automate parts of the old process, while we redefine the market.
PR-tech suppliers can't compete without cannibalizing their own customers - and their own P&L.
Wires have no agency scope - they serve IR/distribution needs.
Other automations mostly sell to PR agencies, and lack the judgment and technical depth to replicate one.

New EntrantsAI-native · start at zero

No trust or live customers - ours is earned over years in the field.
No PR-specific dataset to train on - ours spans 15 years and 5,000+ releases.
No PR analytical expertise to build prediction algorithms or codify playbooks into a system - a rare skill in a field that runs on gut feel.
No learning loop - our system already improves on tens of thousands of HITL corrections, sharpening with every new customer.

See the full breakdown in the appendix.

24/Moat · Data Examples

EMILY.ai · Pre-seed · 2026

Access & Foundation · 25 cumulative years consulting and running PR: 5,000+ press releases, columns and commentaries, 10,000+ reporters engaged, coverage in ~500 media outlets, ~250 clients, across ~30 verticals.

Examples of data we capture

Customer

•Brand Score - Real-Time & Trending Media Presence and Authority
•Media Sensitivities, Reputational Risk Guardrails
•PR Strategy, Plan & Goals

+67 more parameters tracked

Press Release

•Newsworthiness Score - Multi-Factor Pick-Up Prediction
•Distribution Strategy & Timing
•Story Angles / Ideas (Tuned to Goals & Reporters)

+51 more parameters tracked

Reporters

•Reporter Preferences - Story Fit by Sentiment & Nuance
•Reporter Sensitivities - Do's, Don'ts & Triggers
•Editorial Changes - The Edits a Reporter Made

+37 more parameters tracked

Agency

•HITL Corrections
•Industry Players, Trends & News Benchmarks
•Engagement Drivers & Churn-Risk Signals

+30 more parameters tracked

26/Marketplace

EMILY.ai · Pre-seed

A one-sided push, rewired into a marketplace

TodayOne-sided: companies push out, hoping to get published

1.5M

Companies

buying PR

Pay to be told

~150K

PR agencies

fragmented

Pitch & hope
1 release blasted to
hundreds of reporters

~1M

Reporters & outlets

gatekeepers

With EMILYTwo-sided: story supply meets story demand

1.5M+ opportunity market

Companies

+ their AI agents

Stories to tell
what's wanted

EMILY.ai

The marketplace

matches & runs it

operated by PR professionals

Stories they want
matched

~1M

Reporters & outlets

+ their AI agents

The marketplace unlocks in year 2-3: once hundreds of customers are live, their stories make supply liquid enough to match reporter demand at scale.

27/What is PR

EMILY.ai · Pre-seed

What is PR?

Public relations is how a company earns its reputation - credible, third-party coverage in the press that money can't simply buy.

What does success look like?

The right reporters, at the right outlets, telling your story to the right audience - consistently, and on message.

Why now?

In the AI era, the PR market is growing 7–10%. As media is flooded with fake news, both people and LLMs (GEO) seek reliable sources of truth.

PR flow, for every client

Media profiling

Media coverage history · Sensitivities · Competitors · Reporter matching

PR planning

Strategy · Plan · Gantt

Running releases

Generate · Evaluate · Predict · Improve

Distributing

Match reporters · Distribution plan · Customized engagement

Reporting

Coverage results · Audience reach · Metrics

Learning loop

Wins, misses, signals - per company + reporters

DATA FLYWHEEL · Industry, customer, reporter & orchestration

28/What is PR · Examples

EMILY.ai · Pre-seed

bloomberg.com

Bloomberg - NBA Invests in AI-Driven Video-Tracking Firm

venturebeat.com

VentureBeat - Why in-fiber photonics may outperform current semiconductors

Coverage Report

Leona.AI - Platform Launch

Generated on March 1, 2026 at 7:40 AM ET

Articles

Total engagement

5,040

Avg. engagement

Journalist shares

Journalist reach

1.1M

UVM

31,200,000

Insights by similarweb

29/Product · What & How

EMILY.ai · Pre-seed

EMILY is an AI-enabled services firm that runs your PR end-to-end, from strategy to publication.

Data-driven and predictable, built to maximize press results, fast and at scale.

You pay for outcomes, not effort.

How

Deep media profile per client
Reporter & outlet digital twins, matched to every press release
Press release generation, newsworthiness scoring, and insights for outcome maximization
Personalized reporter engagement until coverage lands
Two agentic operating systems: customer-facing and back-office
Powered by PR big data and AI, built, trained and orchestrated by PR professionals

New Chat

Chat

Press Releases

Company Profile

Dashboards

Notifications

Industry Insights

Recents

Initiating Press Release for…

Getting started with Tutorials…

Welcome Mindy!

MMindy

30/Product · Media Profile

EMILY.ai · Pre-seed

The media profile is the context behind every action EMILY takes.

EMILY builds a living media profile for each client - coverage history, sensitivities, competitors, and reporter matching - 60+ parameters.

Media coverage over time and recent coverage

31/Product · Reporters and Distribution

EMILY.ai · Pre-seed

Matched reporters · profiled live · personalized outreach per reporter

Distribution · strategy & execution

32/Product

EMILY.ai · Pre-seed

Two operating systems. One EMILY.

EMILY · Back Office · Agency OS

EMILY · for Customers · Client OS

33/Real Coverage Examples

EMILY.ai · Pre-seed

No coffee. No warm intro. We clear the reporter's threshold, and they publish.

Coverage isn't built on relationships. It's earned by clearing the reporter's bar, and clearing it again, until trust compounds.

01 / THE APPROACH

A pitch built to read personal

Cold outreach, tuned to their beat, written to feel one-to-one.

cold openper-beatreads 1:1

02 / THE BAR

We clear the newsworthiness bar

No rapport needed, just the right hook, fit, and timing.

hookfittiming → yes

03 / THE COMPOUNDING

Repeat hits become trust

Clear it again and the yes shrinks to a reflex. No coffee, ever.

faster yesstanding invite

Trust compounds · real thread

CXOToday

Raj Narayan · 5 straight bylines in 5 months

Pitch 1

“Yes please do send me the article.”

Pitch 2

“Yes. Please do share the same.”

Pitch 3

“Sure please share.”

Pitch 5

“As always, bring it on. All good stuff, giving us good readership.”

Same reporter. Same PR rep. Never met. The yes decays to one line.

34/Proof · Real Placements

EMILY.ai · Pre-seed

Six cold pitches. Six published stories. Zero meetings.

First email to a stranger, through to the live article, every quote verbatim.

VBVentureBeat

Tier-1 · tech

Cognifiber · first glass-based photonic chip

Cold pitch

“Meet the first glass-based chip!”

→

Routed in

“Copying Dan Muse & Arne Verheyde, who cover this.”

→

Published

Ran days later, byline & all.

venturebeat.com/ai/cognifiber-miniaturizes-photonics

Cognifiber miniaturizes photonics for edge computing

Arne Verheyde · Apr 1, 2022Live

siliconANGLE

Major · tech news

SentinelOne · guest column, elder cyber-fraud

Cold pitch

“New column, Cyber Against Granny.”

→

Replied

“Not 100% sure it's a fit, but I can take a look.”

→

Published

“It's published with minimal changes, thanks!”

siliconangle.com/2020/07/28/cybercriminals-target-elderly

Guest column

As cybercriminals target the elderly, here's how to stop their attacks

Yotam Gutman · Jul 28, 2020Live

Axios

Tier-1 · business

Sensos (Sony spin-off) · $20M Series A, exclusive

Cold pitch

“$20M round transforms supply management.”

→

Replied

“We write full stories for exclusives, can I have it?”

→

Published

“That's a great article, Kimberly. Thank you!”

axios.com/pro/retail-deals · exclusive

Sensos snags $20M Series A for smart labels for the supply chain

Kimberly Chin · Feb 7, 2024Live

Sportico

Tier-1 · sports business

Videocites × NBA · $10M round + partnership

Cold pitch

“The NBA's new investment & partnership.”

→

No reply, nudged

Followed up once. Silence.

→

Published

“We published a story on it yesterday.”

sportico.com/business/finance/2023/nba-videocites

NBA, Velocity Capital Invest in Social Analytics Firm Videocites

Brendan Coffey · Mar 1, 2023Live

The Economic Times

Major · India · ETCISO

Radiflow · maritime cyber byline

Cold pitch

“Analysis: safeguarding ships from cyber risks.”

→

Replied

“I went through this, we can take this up.”

→

Published

“Please find the link to the article here.”

ciosea.economictimes.indiatimes.com

Byline

Protecting Ships from Cyber Terrorism

Ilan Barda, CEO Radiflow · Apr 2024Live

EE Times Europe

Top trade · electronics

Radiflow + Cisco · reporter-written feature

Cold pitch

“Cisco-run OT facilities just got more secure.”

→

Replied

“I can write a piece, answer my questions?”

→

Published

“The article is now live on EETimes.eu.”

eetimes.eu/cisco-radiflow-team-on-intrusion-detection

Cisco, Radiflow team on intrusion detection in data centers

Anne-Françoise Pelé · Oct 2022Live

35/Inside One Reporter

EMILY.ai · Pre-seed

This is how deeply we know a single reporter.

Most PR guesses. We model each reporter, our internal data plus their published record, then predict who runs it, before we send.

David MarshallVMblog · covers cyber, cloud & AI securityone of 172 reporters profiled this deeply

What our internal data reveals from every exchange

Warm for years, first-name, high trust.

Replies in hours, his interest runs hot.

Rarely pushes back, we've earned it.

“Sure, send it” once it fits his lane.

Amplifies our coverage to his followers.

A quiet heads-up plays as his exclusive.

Reviews fast, edits almost nothing.

Wants a byline, never straight news.

Ignore personnel & appointments.

Range: cyber, IoT, chips, telematics.

None of this appears in a single thing he publishes, and no one else has it.

What his record reveals dozens of parameters scored

Company sector5.7

Product name5.5

Competitors named5.4

Trend alignment5.2

Edge over rivals5.2

Problem solved5.2

Impact method5.0

Product description4.7

Each number = how much that signal drives whether he covers you.

The prediction

For a given release, before we send:

89.4

predicted fit / 100

WILL PUBLISH

0publish line 55100

So we know how to pitch him: lead with company sector, his top signal.

36/In Action

EMILY.ai · Pre-seed

It doesn't just score a reporter. It tells us the exact fix.

We score the release for each reporter before sending, and when it's too weak, name the exact fix that wins them.

The release, scored for one reporter

Rick Whiting · CRN

57 / 100 · too weak. He'd pass.

What he wants that the release is missing

×A market-size number, the scale he cares about most.

×A competitive angle, who this beats.

×A named customer.

The system's fix, three additions, from the client's own facts

+Name the scale. “Guards the identity layer behind 9 of 10 enterprise breaches.”

+Add the rival frame. “Where scanners only find the gaps, this closes them.”

+Name a customer. The Fortune-500 logo the client can share.

57→78now he runs it.

The honest question

Does a system behind this bother reporters?

Not at all. This is already automated, we've run it hundreds of times. The system does the work; a person stays in the loop to sign and check every email. As it gets stronger it needs fewer people, but every send is still signed by a real one. And in the replies, reporters love it.

37/Vision

EMILY.ai · Pre-seed

EMILY.ai - Public Relations made perfect.

We believe every company will run its own PR with EMILY.ai - at any scale, in any language, on any medium - getting press coverage that beats any agency, paying for results, not effort.