Вероніка

MOROZ
VERONIKA
CONTACTS EDUCATION
Lviv
Lviv Polytechnic National University 2021-2025
[відкрити контакти](див. вище в блоці «контактна інформація») Bachelor's degree in software engineering
Linkedin: Veronika Moroz

CAREER SUMMARY
LANGUAGE
All listed experiences are part of my volunteering work within the organization
English - Upper Intermediate (B2) BEST, which provided me with valuable opportunities to grow professionally and
Ukrainian - native personally.
Content responsible for Capture The Flag (CTF) 2024, BEST
Lviv
HARD SKILLS June 2024 - November 2024.

Tools: Ahrefs, Google Trends, Google Writing detailed task descriptions for competitors, and step-by-step
Sheets/Excel, ChatGPT, Trello, Notion, instructions
Google Meet Creating texts for Telegram bots
Copywriting and content creation Writing content for the website
tailored to target audiences Drafted and customized letters for email campaigns
SEO writing (keywords, structure, Drafted texts for posts and videos on cybersecurity topics
headings optimization) Providing regular feedback on social media texts to improve clarity and
Editing and proofreading texts engagement
Information research and fact-checking Editing, proofreading, and adapting texts for SEO (keywords, structure,
Basic knowledge of HTML and headings)
WordPress Collaborating with organizers and team members in weekly online/offline
Ability to write concisely and clearly meetings
Coordinating a working team, delegating tasks, and ensuring deadlines
were met
Preparing materials, test tasks, and evaluation criteria to maintain high-
SOFT SKILLS quality standards
Supporting and guiding team members, helping them improve content
Creativity creation skills
Responsibility and adherence to
deadlines
Proactivity Fundraising responsible for BEST Engineering Competition
Time management (BEC) 2023, BEST Lviv
Formal communication June 2023 - December 2023.
Problem-solving
Writing clear, step-by-step guidelines with referral links for training
Delegation skills
fundraisers
Leadership qualities
Drafting and customized letters for email campaigns
Hardworking and detail-oriented
Creating surveys for companies to identify their goals and expectations
Drafting call scripts for outreach to potential partners
Writing content for the website
Drafting personalized cooperation proposals tailored to companies’
interests
Preparing reports for partner companies, ensuring transparency and
trust
Contributing to grant writing and application preparation
Supporting branding and communication efforts to increase visibility of
events
Organizing and coordinating a fundraising team of ~30 members
Maintaining and updating a database of 200+ potential and barter
partners
Collaborating with organizers and team members in weekly online/offline
meetings
Developing general fundraising planning and coordinating task
execution
Why AI matters for brand leaders
Meta title: AI and Branding: Balancing Personalization and Authenticity.​
Meta description: Discover how AI transforms branding through personalization and
efficiency — while keeping authenticity at the heart of customer experience.​
Keywords: AI and branding, AI personalization, human-centered AI, AI marketing strategy,
AI business optimization, authenticity in branding​
Target audience: US professionals 25–35, who are into business and tech.

AI (ML models, NLP and LLMs) lets brands scale personalization,
automate creative workflows and run real-time experiments. Research and
market coverage show companies that use AI for smart targeting can lift
marketing ROI — but results depend on design and measurement.

Keep authenticity, not algorithms, at the center
The strongest brands treat AI as an enabler — not the story. A recent
Forbes piece argues that customers want to feel understood, not analyzed;
brands win when AI works quietly to deepen real human connection.

The Duolingo case: product + personality
Duolingo moved to an “AI-first” approach and has rolled out AI conversation
features and large-scale experimentation to scale content and engagement
— a useful case of product-driven branding where tech supports the user
experience (and invites debate about headcount and trust).

Tactical playbook for business leaders
AI is powerful, but it can feel abstract. Here’s how to make it practical for
your brand:

1. Build smarter customer profiles

Customer Data Platforms (CDPs) can collect and unify customer behavior.
Combine them with A/B testing to learn what really works. Instead of
guessing, you can base your campaigns on real evidence.

2. Make AI invisible
Your customers don’t care if your algorithm is cutting-edge — they care
about convenience and relevance. Use AI to remove friction: faster
recommendations, helpful content, or personalized service. Keep the
technology in the background and let the experience shine.

3. Stay alert to risks

AI can overstep. Over-personalized offers sometimes feel “creepy.” Data
privacy is under growing regulation. Monitor these risks, set internal
guidelines, and make sure your AI doesn’t harm trust.

4. Keep the human touch

AI is great for scale, but your team is what gives the brand its unique voice.
Always review AI-generated content for tone, humor, and cultural
relevance. The human layer is what makes the difference between
automation and true connection.

Summary
AI is transforming branding by enabling personalization, speed, and scale.
Yet, technology alone cannot replace human connection. The brands that
win are those that use AI as a quiet partner — making products smarter,
experiences smoother, and campaigns sharper — while keeping
authenticity, creativity, and empathy at the center.

In other words: algorithms may optimize, but people remember how your
brand makes them feel.
Employer Branding for Young
Professionals: How Tech Firms Win
Talent
Meta title: Employer Branding for Tech: How to Attract & Keep Top Talent​
Meta description: Practical employer branding tactics for tech companies. Learn EVP,
storytelling, tech-led culture, metrics, and SEO tips to attract business-minded talent.​
Target audience: US professionals aged 25–35 interested in business and tech.

Introduction — why this matters now
If your company wants top tech talent, employer branding is no longer optional. Employer
branding is how people see your company as a workplace — its culture, values, benefits,
and daily life. Employers that get this right attract better candidates faster and keep people
longer. Recent analyses show employer branding is now a C-suite priority: talent sits at the
top of leaders’ agendas.

This guide explains simple, practical steps that tech companies can use to build a strong
employer brand that appeals to people who care about business, growth, and meaningful
work.

What is employer branding
Employer branding = your company’s reputation as a place to work. It includes what current
employees say, what candidates find on your career pages, and how your company appears
on social media and review sites. A clear Employee Value Proposition (EVP) — the “what’s
in it for me” for employees — sits at the center of a good employer brand.

Core elements of a strong employer brand
1. Employee Value Proposition (EVP)

EVP answers: Why should someone join and stay here? It should be specific, honest, and
tied to business strategy (career paths, learning, mission). Case studies from reputable
outlets show that companies with clear EVPs convert more applicants into hires.

2. Real stories

Share short employee stories: day-in-the-life videos, short interviews about projects, and
internal mobility wins. Authentic employee content trumps polished corporate messaging
alone. Research and practical guides recommend video and peer storytelling as high-impact
tactics.
3. Technology and the digital employee experience (DEX)

For tech talent, the tools you give employees matter. If your DEX is poor (slow systems,
clunky workflows), candidate interest drops even if perks look nice on paper. Thought pieces
on tech and culture highlight that workplace tech shapes employee perceptions.

4. Purpose and development

Younger professionals care about growth and purpose. Show clear learning budgets,
mentorship programs, and how the company’s product matters. Reports and rankings of
attractive employers show students and early professionals prefer companies with clear
development paths.

5. Trust & transparency

Be upfront about hybrid policies, diversity efforts, and career maps. Transparency builds
trust and reduces early turnover — which improves brand word-of-mouth on sites like
Glassdoor and LinkedIn.

Practical 7-step playbook
1.​ Audit your touchpoints. List every place candidates see you: job posts, careers
page, LinkedIn, Glassdoor, Twitter/X, product blog.​

2.​ Define a tight EVP. One sentence + three proof points (growth, impact, perks). Use
that across job ads and social.​

3.​ Create 30-60 second employee videos. Keep them honest: daily tasks, team
rituals, a quick “why I stay.”​

4.​ Optimize your careers page for search (SEO). Use job-relevant keywords, clear
headings and fast load times.​

5.​ Leverage internal advocates. Encourage employees to share openings and stories
— organic posts outperform pure ads.​

6.​ Measure simple KPIs. Time-to-hire, offer acceptance rate, candidate NPS, and
employee retention at 6-12 months. Tie changes to cost per hire.​

7.​ Iterate with feedback. Use exit interviews and candidate surveys to improve the
candidate journey.​

Short summary (for busy readers)
Employer branding is the company’s reputation as an employer. Build it by defining a clear
EVP, sharing real employee stories, improving the digital employee experience, and
measuring hires and retention. Use SEO best practices on careers pages, encourage
employee advocacy, and iterate with data. Start with a careers page audit, one EVP
sentence, and employee videos — those three steps alone will improve candidate interest
quickly.
Humanizing AI in Marketing Automation
with GPT-Realtime
Meta title: Humanize AI with GPT-Realtime: Case Studies in Marketing Automation for
Business Growth

Meta description: Discover how GPT-Realtime and GPT-4.1 are transforming marketing
automation. Explore real-world case studies like Genspark’s Super Agent, learn how AI can
humanize customer interactions, and see how U.S. business owners can optimize
operations with real-time AI voice technology.

Target audience: U.S.-based business owners aged 30–40 who want to integrate AI into
their operations, especially marketing automation.

Primary keywords:

●​ gpt-realtime
●​ humanize AI
●​ marketing automation with AI
●​ GPT-4.1 for business
●​ AI voice agents
●​ real-time AI

AI and machine learning have rapidly changed how businesses automate marketing and
operations. Now, a new wave of real-time, voice-enabled AI is emerging to humanize AI in
ways that surprise even tech-savvy entrepreneurs. Using cutting-edge models like GPT-4.1
and the OpenAI Realtime API, companies can build agents that interact just like people –
handling complex tasks, making phone calls, and engaging customers in natural
conversation. For example, OpenAI highlights in a case study: a no-code AI assistant that
can “make phone calls, design slides, generate videos, and more” simply from a text or voice
prompt [1]. By orchestrating multiple models and tools behind the scenes, Super Agent
automates entire workflows with a friendly, conversational interface. This case study-style
article explores how GPT-Realtime technology – the real-time voice AI leap – is transforming
marketing automation for U.S. business owners. We’ll cover real-world examples, break
down the technology in plain language, and show how brands can use it today.

Case Study: Genspark’s Super Agent
Consider Genspark, a startup whose “Super Agent” showcases real-time AI in action. In
April 2025, Genspark pivoted from a search engine to build this fully autonomous, no-code
assistant. In simple terms, Super Agent acts like a personal marketing assistant you can talk
to. You type or say something like “make me a slide deck” or “book a meeting with my
dentist,” and it handles all the steps. Under the hood, Super Agent “orchestrates nine
specialized large language models and more than 80 integrated tools”, dynamically
picking the right AI for each task. Crucially, it uses OpenAI’s GPT-4.1 for text reasoning and
a GPT-image model for graphics. GPT-4.1 brings 1 million-token context windows and
improved instruction-following, so the agent can process very long documents or
conversations without losing context. For output, it can even produce structured JSON,
making it easy to feed results into other software (like generating well-formatted slides or
database entries).

Genspark’s Super Agent generates AI-powered slide decks with a simple prompt (via a
no-code interface). In one example, a user asks for a “vaporware-style pitch deck,” and the
agent automatically drafts slide content, creates stylized cover images, and compiles
the final presentation. (The image shows the Super Agent UI crafting a slide from a user
prompt.) Likewise, for social media, you might prompt: “Make me a 30-second Instagram
video about our new product.” Super Agent will write a scene-by-scene script, generate
each scene’s visuals, and stitch together a ready-to-post video – all without any manual
editing. Because it’s fully no-code, business owners don’t need developers or IT: they just
describe the outcome they want, and Super Agent handles the execution.

In this setup, voice calls are perhaps the most striking feature. Super Agent’s “Call For Me”
service uses the Realtime API to place phone calls on your behalf. Imagine needing to
reschedule a dentist appointment or cancel a delivery – instead of calling yourself, you could
have Super Agent do it. It genuinely “holds a conversation” with no scripts or stiff menus,
sounding natural and responsive. A dual-layer design powers this: a live GPT-Realtime
model handles the actual speech-to-speech conversation, while a second “shadow” model
quietly listens and guides decisions. The result is smooth, back-and-forth dialogue, even
when background music or unclear responses occur. (OpenAI notes that Super Agent can
adapt even if the person on the other end laughs, interrupts, or speaks in another language.)
In one remarkable example from Japan, users actually asked Super Agent to handle
resignation calls to their employers – something that involves tone, timing, and empathy. The
OpenAI case study reports: “It’s the kind of deeply human interaction most people don’t
expect an AI agent to handle.” This shows how GPT-Realtime can humanize AI by giving it
voice and emotional nuance.

“OpenAI has been supporting Genspark from the beginning. Their APIs didn’t just power our
models, they helped our 20-person team build, launch, and scale faster than anyone thought
possible.”​
—Kay Zhu, CTO and Co-Founder, Genspark

The results speak for themselves: Super Agent hit $36 million ARR in just 45 days with
only 20 people and no paid marketing. Its growth was entirely organic – customers loved the
human-like, no-fuss AI assistant so much that word-of-mouth drove adoption. Zhu notes that
the key is accessibility: users “don’t have to build workflows or configure settings. They just
say what they need, and the agent handles the rest”. In short, Genspark’s success illustrates
how real-time AI can be embedded in marketing and operations to save time, cut costs, and
create a more personalized customer experience.

GPT-4.1 and the Realtime API: How It Works (in Plain
English)
To appreciate these breakthroughs, it helps to understand what GPT-4.1 and the Realtime
API bring to the table – without diving into code. GPT-4.1 is essentially the latest and most
capable version of OpenAI’s large-language models. Compared to its predecessors, it offers
a huge 1-million-token context window, meaning the AI can “remember” and reason about
very long documents or chats in one go. (For perspective, that’s like reading an entire novel
or research report at once without getting cut off.) It also follows instructions better and can
return data in precise JSON format for reliable integration with other systems. In Genspark’s
case, GPT-4.1 handles complex tasks like research, email drafts, and slide text, while
another model (GPT-image-1) creates images.

The Realtime API is what makes all this truly real-time and conversational. Traditionally, a
voice bot might first convert speech-to-text, then send that text to an AI, then convert the AI’s
reply back to speech. Each step adds delay and can make the voice sound robotic. By
contrast, OpenAI’s Realtime API can process and generate audio in one unified model,
without chopping it into pieces. As OpenAI explains, “unlike traditional pipelines, the
Realtime API processes and generates audio directly through a single model… reducing
latency, preserving nuance in speech, and producing more natural, expressive responses.”.
In practice this means the AI can respond immediately (no long “thinking” pauses) and with
human-like prosody. It can adjust tone, speed, and emotion on the fly – even subtle cues like
laughter or hesitation are handled.

The gpt-realtime model has been tuned for conversational quality. It can follow fine-grained
voice instructions: for example, you might prompt it to “speak quickly and professionally” or
“speak empathetically in a French accent”. It now supports two new high-fidelity voices
(Cedar and Marin) and upgrades the others, resulting in speech with realistic intonation and
warmth. Not only that, but it also performs better at understanding what you say – it can
recognize numbers, codes, or switches between languages mid-sentence. In benchmarks,
GPT-Realtime scored ~82.8% accuracy on Big Bench Audio tests (versus 65.6% for the
old model). In plain terms, it simply “gets” spoken words faster and more reliably.

Behind the scenes, the Realtime API also supports practical features. It can call external
“tools” or APIs by name (for example, looking up calendar availability or sending an email)
without interrupting the conversation flow. If a tool call took time, the agent can continue
talking instead of going silent. The platform even lets you hook into existing phone systems
(via SIP) or image inputs, so the voice AI can use visual context too. In sum, GPT-4.1 and
gpt-realtime give developers a powerful toolkit: large memory, multimodal input (text, image,
speech), and a single API that spits out human-like speech with very low lag. Crucially, for
business users, this all happens via simple API calls – developers at Genspark credit
OpenAI’s “clean API design” for their rapid progress.

Real-World Marketing Automation with Voice AI
Beyond Genspark, many organizations are betting on AI voice agents to boost marketing
and operations. The trend is already visible: companies can use voice AI to reach customers
at scale in a personal way. A recent Reuters analysis describes how businesses use
AI-powered voice calls and messages to streamline outreach. For example, marketing
teams can set up AI agents that dial prospects or customers, deliver a friendly personalized
pitch, answer questions, and schedule follow-ups – all without any human operator on the
line. Reuters notes: “Companies leverage synthetic speech to deliver personalized
messages en masse”. In other words, an AI can automatically make thousands of calls, each
tailored (name, details, offerings) to the person on the other end.

These conversational AI bots adapt on the fly. They don’t just recite scripts; they listen and
respond like humans. As Reuters explains, “These bots can engage prospects in human-like
dialogues by phone or text, adapting on the fly.”. For a business, this means 24/7 outreach
and support that feels personal. Imagine a voice agent that calls clients to confirm
appointments, upsell services, or handle support inquiries – learning from each response as
it goes. Startup tools like Awaz.ai report clients improving lead generation with voice AI,
while call-center platforms tout similar gains. Gartner and industry analysts have identified
voice agents as the next frontier of marketing automation, allowing brands to ‘speak’
with customers at scale. (LinkedIn research has noted that consumers often respond
positively to voice interactions if they sound natural.)

Several big companies are already experimenting. For instance, Zillow is piloting a
voice-driven home search assistant. Josh Weisberg, Zillow’s Head of AI, said that the new
GPT-Realtime model could make searching for a home “feel as natural as a conversation
with a friend”. This means instead of clicking filters, a homebuyer could just say: “Show me
affordable homes near schools,” and the AI would ask follow-up questions and refine results,
all in real time. Similarly, T-Mobile is testing voice AI for customer service, aiming to replace
rigid IVR menus with an agent that understands customers naturally[2]. In both cases, the
companies highlight a shift from script-driven bots to flexible, domain-specific AI agents
that come across as helpful people rather than machines.

Other industries see benefits too: banks are exploring AI callers for appointment scheduling,
and insurers like Lemonade (one of the launch partners) can use voice AI to explain policies
or claim steps conversationally. Even small businesses can get on board: new no-code
platforms allow a local realty agent or retail shop to build a voice FAQ bot by simply
recording a script or uploading an FAQ document. The result is a digital assistant that picks
up the phone or chat instantaneously when a customer needs help, 24/7, sounding warm
and understanding rather than robotic.

●​ Lead generation: AI agents call cold prospects, qualify leads by asking key
questions, and update the CRM automatically.
●​ Personalized campaigns: Voice bots can ​ deliver targeted promotions (e.g.
appointment reminders, loyalty offers) and adapt messaging based on customer
responses.
●​ Customer support: Replacing hold-time menus with an agent that answers
questions, resets passwords, or ​ files tickets in natural language.
●​ Content creation: As Genspark shows, AI can draft marketing materials (emails,
slides, social posts) on demand, freeing teams to focus on strategy.

These applications are already ROI-positive. With repetitive tasks handled by AI,
businesses see faster response times and higher engagement. For example, a U.S. ad
agency using an AI voice assistant reports a 3× increase in cold-call conversions, since
prospects are more willing to talk to a “person” on the line. Similarly, companies using AI to
personalize follow-ups and newsletters see open rates rise, because the content feels
tailored. Analysts predict that by 2026, over 70% of mid-sized companies will deploy
AI-driven voice or chat agents in some part of their customer journey.

The Impact: Humanizing AI and Empowering Owners
The real power of GPT-Realtime and multimodal AI is humanization. For business owners,
these tools make AI feel familiar and trustworthy. Instead of reams of data or complex
software, they interact by speaking naturally. Think of it as AI with a friendly voice and
personality. OpenAI emphasizes this shift: GPT-Realtime lets agents “speak with the
intonation, emotion, and pace of a human”. They can sound empathetic, excited, or
professional based on context, which builds rapport with customers. A customer on the
phone or chat line won’t realize it’s an AI until maybe the end of the call, but they will
definitely notice if the AI uses their name, answers naturally, and doesn’t bore them with
robotic tones.

This human touch has marketing value. In focus groups, consumers report being more
receptive when a voice assistant sounds genuine. It also allows small teams to scale: your
5-person marketing team can suddenly “sound” like ten because an AI can handle routine
calls and content. Business owners can concentrate on high-level strategy while their AI
agents handle execution. Moreover, because models like GPT-4.1 understand context and
nuance, they can adapt on the fly: if a customer sounds confused, the agent can slow down
and re-explain, or if someone is in a hurry, it can skip pleasantries and get to the point. This
level of sensitivity was impossible with old-school automation.

“We built Genspark to be more than a chat interface, it’s an all-in-one AI workspace,” says
CTO Kay Zhu. “And with OpenAI APIs, we were able to make that real in record time.” This
encapsulates the promise: by leveraging GPT-Realtime technology, businesses can create
AI helpers that feel like real team members. No special coding skills are needed – the
investment goes into training the AI (often just by giving it example conversations or rules)
and integrating it with existing tools. Once set up, the AI lives in the cloud (via OpenAI’s
APIs) and can be updated or retrained at any time. For marketing and operations, this
means dynamically evolving automation: AI agents learn from feedback and can be tweaked
as markets change.

Implementation and Best Practices
For a U.S. s…