AI • AUTOMATION • CONTENT

Automation that works as a system.

I design AI integrations, content pipelines and personalized outreach systems with n8n, Make and OpenAI.

7detailed case studies10+connected workflowsHITLquality control
ORCHESTRATIONn8n / AI / Logic
AIAI analysisOpenAI / GPT
TXTContent generationPosts / Email
@Email outreachPersonalization
INInbound leadsForms / Webhook
DBCRM / databaseLeads / Companies
ICPSegmentationAudiences / ICP
AnalyticsReports / KPI
DATAStorageFiles / Data
Apify
n8n
OpenAI
Airtable
Evgeniy • AI Automation
VERIFIED ON UPWORK100% Job Success5.0 rating16 jobsView profile

SELECTED WORK / 2026

Projects where AI becomes part of the process

Not a standalone button or chatbot, but a working system with data, checks and measurable outcomes.

01AI PRODUCT / CONTENT
Real admin dashboard of an AI advertising video platform
TIMELINE2.5 weeksOUTCOMEMVP ready
Concept
UX
AI tools
MVP

MVP for an AI advertising video platform

A production-ready MVP for a US AI startup with multi-role access, payments and asynchronous advertising video generation.

Concept → architecture → AI generation → interface → working MVP
01 / CHALLENGE

Validate an AI marketplace concept quickly, without a long and expensive conventional development cycle.

02 / SOLUTION

Lovable powers the interface, n8n orchestrates the backend, and Supabase handles data, authentication and storage. Stripe, Veo, NanoBanana and KIE are integrated.

03 / OUTCOME

A fully functional MVP with roles, payments, asynchronous media generation and scalable SaaS architecture.

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02SYSTEM INTEGRATION
Integration diagram for Salesforce, Make, Google Sheets and Agile CRM
SYSTEMS2 CRMsTIME SAVEDup to 40 h/mo
Salesforce
Mapping
Sync
Agile CRM

Salesforce → Agile CRM synchronization

A complete integration synchronizes companies, contacts and deals between two CRMs, removes manual entry and creates one reliable data flow.

Salesforce → processing and mapping → Agile CRM → monitoring
01 / CHALLENGE

Eliminate manual transfers, unsynchronized contacts and companies, duplicate records and errors between sales and marketing systems.

02 / SOLUTION

Implemented API integration, optional two-way logic, field mapping, duplicate detection, deal-stage synchronization, logging and error monitoring.

03 / OUTCOME

Data now synchronizes near real time, manual entry is nearly eliminated and the system can save up to 40 hours per employee each month.

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03LEAD GENERATION / CRM
Partner leads and contacts inside a CRM
TIMELINE4 weeksLEADS150+ / week
Maps
Website
AI review
HubSpot

Partner CRM for a manufacturing company

A B2B system finds prospective partners, analyzes their websites and pre-qualifies companies before sending them to HubSpot.

Google Maps → websites → AI analysis → qualification → HubSpot
01 / CHALLENGE

Scale B2B partner discovery across Central and Eastern Europe without reviewing every company manually.

02 / SOLUTION

The system collects companies from Google Maps, analyzes their websites with AI and sends qualified records to HubSpot.

03 / OUTCOME

A flow of more than 150 pre-qualified partner leads per week.

04INVESTOR OUTREACH
Real B2B lead generation project with the technology stack and working spreadsheet
MAKE14 scenariosSCRIPTS20+
Collect
Analyze
Email
HubSpot

B2B lead generation, email marketing and CRM

The system collects investor data, analyzes their companies and industries, selects relevant prospects and moves them into email campaigns and HubSpot.

Investors → collection and analysis → email campaign → HubSpot
01 / CHALLENGE

Help a startup collect information about prospective investors, assess relevance and build a repeatable communication process.

02 / SOLUTION

Crunchbase, Apify and Google Maps supply the data; 14 Make scenarios and 20+ Apps Scripts process it, Snov.io runs campaigns and HubSpot stores the outcome.

03 / OUTCOME

The client received one automated system for lead collection, email marketing and ongoing CRM work.

View on Upwork
05LEAD OPERATIONS / AI
A team working with a lead enrichment and personalization system
SYSTEM10+ workflowsCONTROLHuman-in-loop
Import
Enrich
AI signals
Review

AI lead enrichment and personalization system

An end-to-end pipeline imports leads, collects signals from LinkedIn, websites, news and ads, generates personalization and checks quality before outreach.

Leads → enrichment → signals → AI personalization → review
01 / CHALLENGE

Combine data from many sources and generate personalization without sending weak or risky variants.

02 / SOLUTION

An orchestrator manages separate enrichment workflows, signal analysis, text generation, statuses and quality checks.

03 / OUTCOME

One scalable pipeline with logs and quality control that can safely process leads in batches.

06CONTENT AUTOMATION
Video production process inside an automated content factory
CONTENTPhoto + videoFORMATPipeline
Media
Script
Voice
Publish

Social media content factory

Photos and videos from supplier websites and Google Drive are prepared, enhanced with AI, edited, voiced and assembled into publication-ready assets.

Media → script → editing → voiceover → publishing
01 / CHALLENGE

Turn fragmented supplier assets and original footage into a consistent stream of social content.

02 / SOLUTION

Airtable stores assets and statuses, AI assists with scripts, Vizard processes video, ElevenLabs creates voiceovers and FFmpeg builds final versions.

03 / OUTCOME

A repeatable content pipeline with one database, transparent stages and assets prepared for automated publishing.

07DATA PIPELINES / PARSING
Heavy equipment at an industrial site
SOURCESCompany sitesDATABASEAirtable
Supplier
Apify
Normalize
Airtable

Heavy equipment catalog parsers

A set of workflows collects specifications, prices, descriptions, photos and videos from supplier websites, normalizes the data and updates the catalog.

URL → page parsing → normalization → deduplication → Airtable
01 / CHALLENGE

Automatically collect inconsistent equipment listings from multiple websites, including images and embedded videos.

02 / SOLUTION

Each supplier has a dedicated extraction workflow; shared logic then maps fields into one structure and removes duplicates.

03 / OUTCOME

An up-to-date equipment catalog without manual copying, with consistent data and source tracking for every record.

CLIENT FEEDBACK

What changed after the systems went live

These testimonials relate to the client projects above and show the impact from the customer team's perspective.

The idea was quickly turned into a test-ready product without a long and expensive conventional development cycle.

YL

Yevhen L.

Founder

01 / AI advertising video platform

Automated data transfer reduced manual work and allowed the team to focus on the most valuable leads.

OM

Octavian M.

VP of Business Development

02 / M&A boutique

The system created a focused process for finding and qualifying partners for the CEE growth program.

TF

Tony F.

Head of Partnerships

03 / Manufacturing company

Automated outreach helped us find suitable investors and approach them personally at the right moment.

JS

James S.

Co-Founder

04 / Pre-seed startup

WHAT I BUILD

From one integration to a complete pipeline

01

Automation architecture

System design, connections, queues, statuses and safe error-handling workflows.

02

AI and personalization

Signal extraction, content generation, quality scoring and human-in-the-loop review.

03

Integrations and data

CRMs, spreadsheets, APIs, webhooks, parsers, synchronization and data normalization.

04

Content pipelines

Automated photo and video processing, voiceover, assembly and publishing.

APPROACH

Working logic first. Scale second.

Every project starts with a simple, testable workflow. Automation expands only after the core process works reliably on real data.

01

Define the problem

I clarify the goal, data sources, constraints and the points where a person should make the decision.

02

Build the prototype

I launch a minimum working workflow on real data and test the edge cases.

03

Turn it into a system

I add logs, statuses, error handling, documentation and a safe path to scale.

NEXT SYSTEM

Is there a process ready to be automated?

Describe the task in a few sentences and I will propose the structure of the first working prototype.