Maksim Kuhlenko
Offer Maksim work on your next project.
Private message 
Dnepr,
Ukraine
6 days 15 hours back
available for hire
3 Safes completed
1 year back
3 clients
on the service 2 years
Rating
Average rating
Language proficiency level
Українська: fluent
Русский: advanced
English: upper-intermediate
Polski: pre-intermediate
Skills and abilities
Design & art
Photo, Audio & Video
Promotion
Administration
Writing
Portfolio
-
Portfolio of SMM Specialist
Social Media Marketing (SMM)
Hello!
I am Maxim, an SMM specialist with over 3 years of experience.
I work at the intersection of content, strategy, and process automation, actively using AI tools for analytics and content creation to enhance SMM effectiveness.
…
In my portfolio, I have gathered projects from various niches:
— A network of beverage dispensing stores (Hop Hey),
— Medicine and charity (Artesans-ResQ Ukraine),
— A group of digital agencies (NML Digital Agency, NeedMyLink).
In my portfolio, you will find:
— case studies of social media promotion with specific growth metrics;
— examples of content strategies and their impact on reach and engagement;
— results from Meta Ads with clear figures for interaction costs, views, and followers;
— examples of content (posts, Reels, stories);
— testimonials from clients and colleagues that confirm my approach to work.
Write to me, and let's start creating the brand story together.
-
68 USD Automation "Analysis of Requests from Leads" (Make.com)
AI & Machine Learning
The internet marketing agency receives service requests through the Tally form, where potential clients describe their business and needs (advertising setup, SEO, promotion strategy). Previously, the manager manually checked each application, sorted it by categories, and wrote clarifying questions to clients.
Problem:
Slow processing of applications — the manager could respond several hours after receiving the form. There was a lack of basic analytics about the client's request, so often a short briefing needed to be conducted before the call.
…
Solution:
Automation was created in Make, which:
- Tracks new requests from the Tally form;
- Sends the information to OpenAI, which analyzes the content of the application (type of business, request, goals, desired services);
- Forms a short AI summary of the client's request in a convenient format (name, niche, need, budget, urgency);
- Sends a Telegram notification to the manager containing all the collected and analyzed information for a quick response.
Result:
- Response time to a new lead decreased from 1-2 hours to 10-15 minutes;
- The manager receives all basic information without the need for clarifying briefings;
- Applications are not lost — each new lead is immediately recorded in Telegram;
- Increased speed of first contact with the client, which directly affects conversion to sales (plus 15-20%).
Effect of automation:
- Saving up to 2 hours daily on initial processing of applications;
- Improved quality of communication — the manager immediately understands the client's need;
- Higher response speed → more closed deals;
A simple solution that does not require CRM but provides the effect of a "smart assistant" for the sales team.
-
135 USD Automation "Analysis of Sales Department Work" (Make.com)
AI & Machine Learning
The internet marketing agency receives many incoming inquiries every day. Managers make an average of 35–45 calls per working day, with the average duration of a call being 9 minutes and 38 seconds. The quality controller physically could not listen to all the conversations, which led to uneven assessment and the loss of important moments.
Problem:
The quality control manager could only check a portion of the calls (selectively).
… The evaluation was not systematic — different managers received feedback irregularly.
Communication errors were identified with a delay, which reduced conversion to sales.
Solution:
Automation was created on Make.com, which fully covers the quality control process:
It tracks the appearance of new call recordings in the storage system.
- It performs automatic transcription of audio.
- It sends the transcript to a special AI assistant for analysis and evaluation.
- The AI evaluates the call according to an internal system.
- It generates a structured evaluation report with scores and recommendations.
- It sends the report via Telegram to the quality control specialist or the sales department manager.
Result:
- 100% of calls are now evaluated, not just selectively.
- The controller receives a complete analysis of each conversation immediately after completion.
- Managers receive regular feedback and quickly correct communication errors.
- Conversion to consultation appointments and sales increases due to improved scripts.
Effect of automation:
- On average, over 6 hours of audio comes in daily, which was previously impossible to analyze manually.
- Automation has saved up to 5 hours of work for the quality control specialist daily.
- The standardized scoring system has improved the transparency of team evaluations.
- The quality of calls and adherence to scripts has increased → conversion has risen by 12–18%.
- A knowledge base has been formed for training and upskilling new sales managers in the B2B segment.
-
68 USD Automation "Lead Validation" (Make.com)
AI & Machine Learning
SMM agency receives 15–30 incoming requests daily through the lead form: SMM promotion orders, budget inquiries, consultations, and portfolio reviews. The manager manually checked the data, determined the type of lead, formulated a response for the client, and structured the message for the salesperson. Due to the routine, the time for the first contact increased, and warm leads were lost.
Problem:
- The manager spent 10–15 minutes processing each request.
… - Leads were processed unevenly — warm inquiries sometimes "hung" in the queue.
- There was no single structure for internal messages and responses to clients.
- Some requests were lost or duplicated when entered into Google Sheets.
Solution:
An automation was created on Make that fully takes over the first line of processing requests:
1. Tracking a new request in the lead form
Make waits for a new form submission and instantly retrieves all filled fields.
2. Validation of the request and determination of the "type of lead"
AI assistant: reads the selection in the "Your question" field, assigns the type of lead exclusively based on mapping (warm/cold), does not use other fields to change the category.
3. Formation of a service message for the manager (7 lines)
AI generates a structured message with fixed data:
- Name
- Phone
- Telegram
- Inquiry
- Budget
- Type of lead
- Link to the page
The structure is always the same, which simplifies the quick work of the manager.
4. Generation of a response to the client in Ukrainian
AI formulates a ready response based on the type of lead:
warm lead → invitation for a short call + 2 time slots
cold lead → clarification of details + proposal for a call
Always personalization by name, if available.
5. Recording in Google Sheets
The request is automatically recorded in the table:
- all data from the form
- determined type of lead
- time of receipt
- current status
6. Automatic status update upon manager's request
The manager can update the status of the request via a Telegram command or form — Make instantly makes the change in the table.
Result:
- Processing time for requests reduced from 10 minutes to 40 seconds.
- Managers receive unified messages, saving time on "manual translation" of the form.
- Warm leads are processed faster → increase in conversion to calls.
- All requests are stored in the table without losses and duplications — an ideal structure for analytics.
- Automation freed up ~10–15 hours of managerial time weekly.
-
90 USD Automation "Task manager" (Make.com)
AI & Machine Learning
The team of freelancers (SMM, designer, targeting specialist) works with several brands simultaneously. Due to a limited budget, the team did not use paid task managers (Trello, ClickUp, Asana) and coordinated work only in a Telegram chat.
Problem:
Discussions of tasks in the chat led to confusion, loss of important messages, missed deadlines, and inconsistencies among performers. Some tasks were duplicated, and some were simply forgotten due to the lack of centralized control.
…
Solution: A task management system was created based on Google Sheets and Make.
- The Team Lead adds tasks to the Google Sheet, specifies the responsible person, status, and deadline;
- Automation in Make checks for new entries;
- A separate tab is created for each specialist with their current tasks;
- Make automatically sends individual messages in Telegram to each team member — with a list of new or updated tasks, deadlines, and additional comments.
Result:
- Complete abandonment of paid task managers → budget savings of $20–30/month;
- Reduction in the number of overdue tasks by over 50%;
- Communication in Telegram became clear, without lost messages;
- The SMM Team Lead gained full control over execution without additional tools;
- Thanks to the structuring of processes, the team was able to simultaneously manage 2 more projects without increasing the number of people.
Effect of automation:
- Time savings: up to 4 hours per week for each specialist;
- Increase in team productivity by approximately 25–30%;
- Improvement in the quality of execution and predictability of deadlines;
A noticeable reduction in the manager's workload due to transparent task control.
-
158 USD Automation "Telegram Channel Moderator" (Make)
AI & Machine Learning
Author's Telegram channel in the niche of films and art with an audience of 10K+ subscribers publishes movies, cinema premieres, sports events, and broadcasts. The team wanted to maintain a lively, loyal atmosphere without spam, advertising, and toxic behavior, but manual moderation took too much time.
Problem:
Due to audience activity, moderators could not keep up with rule violations. At the same time, spending $150 - $200 on moderator services was not allowed by the budget. Advertising, profanity, and conflicts between users reduced engagement and the channel's reputation.
…
Solution:
An automatic moderation system was developed based on Make.com using the Telegram Bot API and AI modules. The automation performs the following actions:
- Monitors comments under all new posts in the chat.
- Deletes comments if they are sent by a bot or another Telegram channel.
- Filters messages — removes advertising, offensive or discriminatory language (based on nationality, race, gender, religion).
- Blocks users who violate the rules and sends a notification about the removal with a brief explanation.
- Automatically responds to comments that do not violate the rules, maintaining the author's Tone of Voice.
- At the administrator's request, the bot generates new posts (text + photo) and publishes them in the Telegram channel.
Result:
- Complete automation of moderation without the need for constant manual oversight;
- Reduction of spam comments by 90%;
- User blocking occurs instantly, without administrator involvement, eliminating the risk of spam;
- The channel maintains a friendly atmosphere and a high level of audience engagement;
- Administrators have more time for content creation instead of moderation.
Effect of automation:
- Saving up to 4–5 hours daily on moderation;
- Increase in comment activity by 30% due to a positive environment;
- Increased trust from subscribers and quality of discussions;
- Reduction of costs for manual work (no moderator needed → savings of about $150–200/month).
-
135 USD Automation "Direct manager" (Make.com)
AI & Machine Learning
The company providing crowd-marketing services (crowd links) receives a large number of inquiries via Instagram Direct and Telegram: from clarifications about services to questions about orders. The manager was not always able to respond quickly, and some inquiries were repetitive, distracting the team from fulfilling orders.
Problem:
- Slow response to incoming messages in Direct \ Telegram \ Comments.
… - The average response time to inquiries was 30 - 45 minutes.
- A large number of similar questions about services and working conditions.
- Lack of a clear system distinguishing informational inquiries from order requests.
- Managers spent time on simple responses instead of working on projects.
Solution:
Three automated AI support bots were created in Instagram Direct \ comments \ Telegram through Make.com, operating on a unified principle based on a special prompt and the company's internal knowledge base.
The bot automatically performs:
- Monitoring new incoming messages in Direct and Telegram.
- Analyzing the content of the question using OpenAI according to the internal knowledge base.
- Automatic response using verbatim quotes from the knowledge base if an answer exists.
- If the question relates to an order, the bot immediately connects the manager.
- If the answer is not available in the documents, the bot notifies the manager for manual inclusion.
- Records data from all categories of inquiries to Google Sheets for analytics on the accuracy of responses from the assistant.
- The bot fully adheres to the company's Tone of Voice and prompt requirements:
- responds in Ukrainian,
- does not invent information,
- does not use external sources.
Result:
- Response time in Direct has been reduced to 15–30 seconds regardless of the managers' workload.
- Up to 70% of informational inquiries are automatically handled by the bot based on the knowledge base.
- Managers only engage with inquiries in the format of “I want to order” or those where there is no ready answer for the assistant in the FAQ.
- Reduction of missed and “lost” messages in Direct to 0%.
- Increased customer loyalty due to fast and structured communication.
Effect of automation:
- Savings of up to 2 hours daily for the support and sales team.
- Slower responses no longer affect the number of orders — “hot” leads receive instant contact.
- Reduced workload on managers → more time for order fulfillment and quality control.
- Clear separation of inquiry types (information / sales) has reduced chaos in Direct and increased lead processing efficiency.
-
135 USD Automation "Reels Scenario Generator" (Make.com)
AI & Machine Learning
Client-business description:
A medical organization that conducts training and publishes expert videos for staff training. Content managers manually tracked new recordings, transcribed videos, and converted text into scripts for short reels.
Problem:
… - Manual work with videos takes a lot of time (1-2 hours per video).
- It is difficult to maintain quality and a consistent style of scripts.
- A large volume of videos leads to delays in content creation.
Solution:
Automation is set up on Make with the following process:
- Training and expert video recordings are automatically sent to the system.
- An AI tool transcribes audio into text.
- The transcribed text is analyzed by AI and converted into a ready script for reels: key messages are highlighted, CTAs and hooks are used, video duration is 40-50 seconds.
- Scripts are stored in an internal database (Google Sheets / Airtable) for further use and editing by the content team.
Result:
- The time to prepare a script has been reduced from 2 hours to 5-10 minutes per video.
- A consistent style and structure of scripts for all reels have been ensured.
- Average views on videos increased by 1000-1200 due to hooks and content regularity.
-
135 USD Automation "Content Factory" (Make.com)
AI & Machine Learning
Client-business description:
An SEO agency that receives a large flow of news, cases, and trends in SEO and Link Building every day. Content managers manually convert news into posts for Telegram, Threads, and LinkedIn, maintaining the Tone of Voice (ToV) and creating image designs for Telegram.
Problem:
… - Manually adapting one piece of news for three platforms takes a lot of time (2–3 hours per post).
- It is difficult to maintain a consistent Tone of Voice across all channels.
- There is a lack of quick image generation for Telegram.
Solution:
Automation has been set up on Make.com with the following process:
- News is received by a Telegram bot.
- An AI assistant analyzes the ToV and structures the content for three platforms:
Telegram — informally, actively engaging the community.
Threads — briefly and ironically.
LinkedIn — analytically, focusing on expertise and cases.
- The Telegram post receives an automatically generated image via AI.
- All three posts maintain a consistent Tone of Voice and structure in an internal database (Google Sheets / Airtable).
Result:
- The time to adapt one piece of news for three platforms has been reduced from 2–3 hours to 10 minutes.
- Posts across all channels have a consistent style and ToV.
- Images for Telegram are automatically generated, increasing audience engagement.
- All channels are regularly updated and do not lose content relevance.
-
113 USD Automation of analytics collection Goal ADS (Make)
AI & Machine Learning
The Ukrainian automotive accessories brand "Stingray" actively uses Meta ADS as a channel for traffic and retail sales. Every day, the targeting specialist manually collected analytics statistics on campaigns, ad sets, and creatives, created a report in Google Sheets, and sent it to a Telegram chat with the marketer, SMM specialist, and business owner.
Problem:
The process of collecting analytics took up to 1.5 hours daily. Due to manual work, inaccuracies in the data occurred, and the exceeding cost per conversion was often noticed with a delay.
…
Solution:
A scenario was implemented in Make, which automatically retrieves data daily from Facebook Ads Campaign Management (active campaigns);
- Collects key metrics from Facebook Insights — expenses, clicks, CTR, interactions, conversions, the number of messages in Direct and Messenger, purchases on the website;
- Stores all data in Google Sheets broken down by days;
- Forms a brief daily report in Telegram for the targeting specialist and client with the main KPIs;
- Sends a separate message in Telegram, generated by the assistant, with an analysis of key metrics and projected results.
Result:
- Complete automation of daily monitoring of advertising campaigns;
- Time savings of up to 7 hours per week;
- Relevant data every morning without manual updates;
- Instant notifications about deviations from KPIs and quick responses;
- A 10% increase in conversion to sales due to an increase in targeted applications.
Thanks to the time freed from routine tasks (approximately +30 hours per month), the targeting specialist can focus more on ad optimization, testing creatives, and setting up audiences;
A reduction in cost per lead by 15-25% due to prompt responses to ineffective campaigns and increased working time for optimizing existing advertising connections.
Reviews and compliments on completed projects 3
1 February 2025
11 USD
Promotional video for yoga course, dynamic editing, 9:16 (Instagram story/reels)
Quality
Professionalism
Cost
Contactability
Deadlines
The freelancer worked well. Quickly integrated the edits.
|
29 January 2025
11 USD
Need to create two creatives for the conference on YouTube.
Quality
Professionalism
Cost
Contactability
Deadlines
The project was urgent, they completed it for me in 1 year. Thank you for such promptness. The quality did not suffer at all. The work was done at a high level. Therefore, we will continue to cooperate in the future.
28 January 2025
11 USD
Video editing of a reel / shorts — personal project for Maksym Kukhlenko
Quality
Professionalism
Cost
Contactability
Deadlines
the performer is completely inattentive and as a result unprofessional
Yaroslav Riabukha
| Personal | Response review
Activity
| Latest proposals 10 | Budget | Added | Deadlines | Proposal | |
|---|---|---|---|---|---|
|
Automation of outbound lead generation for B2B (Apollo + CRM + data enrichment and validation)
203 USD
|
|||||
REELS turnkey
|
|||||
|
Find footage and edit 5 short videos
23 USD
|
|||||
|
Video editing for targeting
27 USD
|
|||||
|
Video editing of advertising creatives
113 USD
|
|||||
|
Creation of TikTok videos (15 per month)
35 USD
|
|||||
|
SMM manager for a network of meat shops
338 USD
|
|||||
|
Creation of video/animation/static content for Meta ads (English language)
23 USD
|
|||||
|
Video editing for a YouTube channel
16 USD
|
|||||
|
Advertising video for META targeting
23 USD
|