Oleg M.
I select only effective solutions for clients
Offer Oleg work on your next project.
Private message 
Offer work 
Lvov,
Ukraine
currently online
responds within a day
available for hire
21 Safes completed
2 months 11 days back
14 clients
18 proposals made
age 23 years
on the service 2 years
Rating
Language proficiency level
Українська: fluent
English: upper-intermediate
CV
🔧 I am engaged in:
1️⃣ CRM configuration
2️⃣ Selection and implementation of CRM systems
3️⃣ Automation and construction of sales funnels
4️⃣ Integrations with CRM systems
5️⃣ Lead generation through LinkedIn: creating target lists, scripts, and outreach campaigns
💼 3+ years of experience working with CRM systems:
Kommo, Keepin, NetHunt, Zoho, Pipedrive, KeyCRM, OneBox, Perfectum, Uspacy
📦 Niches I have worked with:
✔ Car services/car sales
✔ Warehouse equipment sales
✔ Consulting services
✔ Developers
✔ E-commerce
✔ Dentistry
✔ B2B services where LinkedIn plays a key role
🧠 What I can do on a turnkey basis:
— CRM: selection, implementation, adaptation to business
— Funnels: logic building, automation, quality control
— Integrations: websites, messengers, services (Tilda, Albato, Make, etc.)
— Staff training
— Launch of systematic B2B lead generation through LinkedIn + Apollo + Lemlist
📩 Write to me privately — I will be happy to advise you, even if you are not planning to implement anything right now. I will give you practical advice on CRM or lead generation based on your niche.
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Tag:
CRM systems, CRM implementation, CRM configuration, process automation, Bitrix24, Kommo CRM, Zoho CRM, NetHunt CRM, CRM integration, business processes, sales funnel, Albato, Make, Zapier, LinkedIn outreach, LinkedIn Sales Navigator, lead generation, customer search, B2B lead generation, Apollo, Hunter, PhantomBuster, lead database creation, LinkedIn scraping, email address verification, email marketing, email campaigns, Lemlist, Smartlead, Instantly, letter writing, mailing automation, outreach personalization, lead segmentation, Google Sheets, project management, lead analysis, Tilda, Telegram integrations, CRM documentation, team training, CRM analytics, customer management, status control, letter templates, customer communication, CRM reports, database systematization, digital funnels, consulting, sales automation.
Skills and abilities
Programming
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AI & Machine Learning
from 16 USD for project
- Bot Development
- Content Management Systems
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Data Parsing
from 16 USD for project
- HTML & CSS
- Javascript and Typescript
- Python
- Web Programming
Services
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AI Consulting
from 16 USD for project
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Content Management
from 16 USD for project
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Customer Support
from 16 USD for project
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Data Processing
from 16 USD for project
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Enterprise Resource Planning (ERP)
from 16 USD for project
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Information Gathering
from 16 USD for project
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Online Stores & E-commerce
from 16 USD for project
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Payment Systems Integration
from 16 USD for project
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Tuition
from 16 USD for project
- Website Development
- Website Maintenance
Promotion
- Email Marketing
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Lead Generation & Sales
from 16 USD for project
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Marketing Research
from 16 USD for project
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Social Media Advertising
from 16 USD for project
Administration
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DevOps
from 16 USD for project
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Software & Server Configuration
from 16 USD for project
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System & Network Administration
from 16 USD for project
Outsourcing & consulting
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Client Management & CRM
from 16 USD for project
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Consulting
from 16 USD for project
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Project Management
from 16 USD for unit
Portfolio
- All works 36
- Website Development 1
- Web Programming 1
- Email Marketing 1
- Client Management & CRM 14
- Lead Generation & Sales 2
- AI & Machine Learning 10
- Enterprise Resource Planning (ERP) 7
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451 USD AI search for B2B companies and relevance verification
AI & Machine Learning
Task
The client maintains a database of potential clients in Google Sheets and uses Apollo to find contacts, but managers were spending time on manual duplicate checks, company enrichment, and prioritization. It was necessary to automate: filtering new companies, enrichment through Apollo, prioritization using AI, and creating clear tasks in the form of updated rows in the table and notifications in Telegram.
Solution
… I built a multi-step workflow in n8n that responds to any changes in Google Sheets. The automation checks whether the company is new or already in the database, calls the Apollo API for data enrichment, iterates through each record, and passes the information to the AI model (via OpenRouter) along with a prompt instruction. The AI determines the priority and generates a structured response, which is transformed into clear fields for updating the second sheet through the Structured Output Parser. After updating the row, the system sends a ready message with key information and status to Telegram, so salespeople can immediately see whom to prioritize.
Result
The client received a fully automated cycle: added/updated a company in Google Sheets — the system automatically checked for duplicates, extracted data from Apollo, evaluated the lead through AI, updated the structure in the table, and sent a clear task to Telegram. This eliminated manual routine, reduced the number of errors, sped up the response to new leads, and provided a transparent, reproducible process for prioritizing B2B contacts.
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564 USD Auto-posting to 16+ social networks based on n8n
AI & Machine Learning
Task
It was necessary to automate the regular auto-posting of content on social media to avoid manual uploading of posts every day. It was important to pull content from a table/drive, check that the post had not yet been published, adapt it for different platforms, and have a transparent history of executions.
Solution
… A multi-step workflow was assembled in n8n, which is triggered on a schedule (Schedule Trigger) and goes through the full cycle from selecting posts to their publication. At the start, the script pulls a list of scheduled posts from a table/Google Sheets, filters records by publication date and status, and checks for the presence of media files in cloud storage. Next, branching is set up: separate branches are created for each post for different platforms (for example, LinkedIn, Facebook, Instagram), where text formatting, addition of UTM tags, and image uploads take place. At the final nodes, publication is decided: n8n sends the post to the corresponding social media API, changes the post status in the table to "published," and logs the execution result (success/error) for further analysis.
Result
The content plan is now executed completely automatically — it is enough to add a new entry to the table, and the post enters the auto-posting queue according to the specified schedule. The team has stopped wasting time on manual publication, and the risk of "forgetting to post" specific material has been reduced to almost zero. All posts now have a uniform structure, correct links, and UTM tags, and through the log in n8n, it is easy to track what exactly was published and where errors might have occurred.
In numbers
- 1 universal workflow in n8n covers auto-posting to several platforms at once.
- Up to 90% of routine operations for posting have been automated.
- Savings of up to 10 hours per week on manual uploading and formatting of content.
- 100% of scheduled posts are published according to the content plan, with no omissions due to human factors.
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226 USD This scenario connects Lemlist, Pipedrive, and analytics in Looker.
Client Management & CRM
1. Event Source: Lemlist - Router
Automation starts from Lemlist:
- the "Watch Activities" module tracks events in email campaigns (sending, opening, clicks, replies, unsubscribes);
- each event goes to the Router, which distributes it into two main branches:
… - synchronization of leads/deals through Pipedrive;
- updating campaign statistics in Google Sheets.
Value: the script reacts specifically to the actions of email recipients, rather than working on a cron schedule "blindly."
2. Branch 1: Lemlist - Pipedrive - Google Sheets (deals)
At the top of the script, a chain of several Pipedrive nodes and Google Sheets is built:
- the first Pipedrive "Search for Items" finds the necessary deals or contacts using email/ID from Lemlist;
- subsequent Pipedrive modules receive details: funnel, stage, deal name, responsible manager;
- the Router divides this data into 2–3 branches based on logic: for example, "deals in active stage," "deals in success," "closed/lost";
- for each branch, a separate Google Sheets module "Add a Row" creates a row in the corresponding sheet (for example: Active Deals, Won Deals, Lost Deals), where key fields are recorded: Lemlist campaign, email, status, stage, date, manager.
Value: you get a live funnel for outbound campaigns in the form of tabular reports, where for each contact you can see both the deal status in Pipedrive and activity in Lemlist.
3. Branch 2: Lemlist - Google Sheets - Router - Google Sheets (campaign statistics)
The lower part of the script is responsible for aggregated statistics:
- the Lemlist module "Export Statistics / Leads of a Campaign" uploads metrics for a specific campaign (sent, opened, replies, positive/negative replies, etc.);
- Google Sheets "Search Rows (Advanced)" checks if there is already a record for this campaign/lead in the table;
- the Router analyzes the search result:
- if a row is found — the Google Sheets branch "Update a Row" updates the statistics (for example, increments the number of opens/replies, updates the last activity);
- if a row is not found — the second Google Sheets branch "Add a Row" creates a new record with all metrics and links to the campaign and manager.
Value: a dashboard for Lemlist campaigns is formed in one table: conversions, openings, replies, which can be immediately used for analytics or connected to BI.
4. Role of Router nodes
Router nodes in the center of the script act as the "brain" of routing:
- they separate the flow of events by type (reply, click, unsubscribe, bounce);
- they send different types of events to different tables/sheets or to different update branches.
- For example, customer replies can go to a separate sheet "Replies," while clicks are only for CTR analytics, without creating deals.
5. What the business receives
A single contour: Lemlist - Pipedrive - Google Sheets work as an interconnected system, without manual CSV export.
Relevance: deal statuses, lead activity, and campaign statistics are updated automatically with each event, rather than once a week.
Transparent analytics: in Google Sheets, it is clear which campaigns yield the most replies, at which stages leads "get stuck," and which managers close deals after outbound emails.
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180 USD Automatic lead qualification in Google Sheets via Make
AI & Machine Learning
Task
It was necessary to automate the processing of incoming leads so that they immediately entered a structured Google Sheet and received basic qualification. An important condition was to avoid manual data copying, have a single source of truth for leads, and the ability to further work with them in other scenarios.
Solution
… A scenario was set up in Make, which receives new leads from connected sources and transfers them to Google Sheets. The scenario takes raw lead data (contacts, channel, type of request) as input, cleans it, and applies simple qualification rules (for example, by source, country, budget, or type of service). Then Make records the information in the main Google Sheet: if the lead does not exist yet, a new row is created; if a match is found by email/phone, the scenario updates the existing row without creating duplicates. Within the sheet, the data is distributed across the necessary columns (status, segment, priority), allowing for immediate filtering and passing them to the next stages (funnels, mailings, processing in CRM).
Result
All new leads automatically enter Google Sheets in a structured and usable form, without manual entry and errors. Managers see already qualified leads with statuses and priorities, can quickly filter the necessary segments, and pass them to the further process (for example, to CRM or to a mailing scenario). The system has become a single entry point for the entire lead database, and changes to the qualification logic can be made without the involvement of developers — it is enough to edit the scenario or the structure of the sheet.
In numbers
- 1 scenario in Make that covers the collection and qualification of all incoming leads in Google Sheets.
- Up to 100% of new leads automatically enter the sheet without manual copying.
- 0 duplicates thanks to checks on key fields before creating a new row.
- Reduction of time for initial lead processing to a few seconds instead of minutes for each contact.
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180 USD Automation of cold emailing using n8n
Enterprise Resource Planning (ERP)
Case: Automation of cold email campaigns using n8n
Problem: Mass and personalized B2B email campaigns without paid services.
… Solution: A self-built system on n8n, integrated with Google Sheets for lead management, template randomization, and delivery status tracking. The emails are sent via Gmail/SMTP with scaling limitations.
Result: Mass, free, flexible, and scalable automation of email campaigns for lead generation and sales, fully under your own control (100% ownership), with rapid scaling by adding new accounts.
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68 USD Automated processing of the table using make.com
Enterprise Resource Planning (ERP)
1. Google Sheets: Search Rows
Function: searches for a row (or rows) in Google Sheets that match a given criterion.
Typical task: find data by email, date, or another unique key for further processing.
Input: search condition (e.g., email, date).
… Output: found rows from the table for the next module.
2. Perplexity AI: Create a chat completion
Function: sends the received data (e.g., event description or application text) to the AI model for processing.
Typical task: extract structured information from unstructured text, such as e-mail, name, other details.
Input: text from the found row (description, summary, etc).
Output: text response or semi-structured JSON containing extracted values.
3. Text Parser: Match Pattern
Function: parses a specific pattern (regexp, pattern matching) from the text returned by AI.
Typical task: extract required fields from the Perplexity AI response (e.g., highlight email, date, name).
Input: text or array of values from the previous step.
Output: found subarrays/values that match the pattern.
4. JSON: Parse JSON
Function: converts text (or structured piece) from the previous step into a JSON array/object for further automated work.
Typical task: obtain a valid dataset from AI/parser for transfer to the final system.
Input: text in JSON.
Output: structured JSON (in key-value format), ready for writing to the table.
5. Google Sheets: Update a Row
Function: updates the found row (or multiple rows) in Google Sheets based on new content.
Typical task: write/update data during lead enrichment, add parsed or found AI values to the table (e.g., new email, status, name, formatted date).
Input: row identifier for update + new data.
Output: table with updated row.
Overall logic of the scenario:
- Search for the required row in Google Sheets (by email, name, date, or another variable).
- AI processing of the received text/description to structure the required data.
- Parsing the result through a pattern to highlight specific values.
- Converting into valid JSON for automated work.
- Updating information (adding/correcting required fields) in an already existing or found row in the table.
Use cases
- Automatic enrichment of contacts in Google Sheets through AI.
- Extraction of additional data from the "Description" or "Summary" field and transferring it to separate columns.
- Automatic processing of new entries and saving them in a standardized format.
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113 USD Automated extraction of events from the calendar and their processing with AI
Enterprise Resource Planning (ERP)
1. Google Calendar: Search Events
Function: retrieves all (or selected by filter) events from Google Calendar.
Typical task: find new, upcoming, or specific events for further processing.
Input: search settings (date, filters, calendar).
… Output: array of events from the calendar.
2. Perplexity AI: Create a chat completion
Function: sends event details or description from Google Calendar to AI for parsing.
Typical task: extract structured emails, names, guests, date, etc. from description/summary.
Input: event description or all important fields from the Scheduled event.
Output: structured text or JSON with a list of participants, emails, dates, and more.
3. JSON: Parse JSON
Function: converts the structure of the received text from AI (if it is in JSON format) into a standard array/object that Make can work with.
Typical task: create an array for further splitting.
Input: text in JSON (for example, a list of participants).
Output: array of elements [{name, email}, ...].
4. Iterator (Flow Control)
Function: breaks the array into separate "packages" for further individual processing of each participant.
Typical task: check or write each person/email to Google Sheets one by one.
Input: array of objects from the previous module.
Output: separate objects (name + email) for the next steps.
5. Google Sheets: Search Rows
Function: searches for whether such an email or another unique parameter already exists in Google Sheets.
Typical task: check for duplicates before adding.
Input: email (or another key).
Output: number of found rows (array of found, or “Total number of bundles”).
6. Filter (Duplicate)
Function: passes only those data that have not yet appeared in the table.
Typical task: make a record in Google Sheets only if the data is not already present (duplicates are filtered out).
Condition: Total number of bundles = 0
7. Google Sheets: Add Row
Function: adds a new row with data to Google Sheets.
Typical task: enter a new participant/contact/guest into the table if they have not been added yet.
Input: name, email, date, any additional data from iterator/AI.
Output: new row in the table.
Overall logic of operation:
- Get events →
- Parse description/details through AI →
- Convert to JSON array →
- Split each participant (iterator) →
- Check in the table — whether such an email exists →
- Filter only unique →
- Add only new rows (Add Row)
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113 USD Automation of data collection from Apify using make.com and AI
Enterprise Resource Planning (ERP)
1. Apify: Make an API Call
Function: Requests data from Apify (Scraper, agent, parser, or custom integrator).
Input: API settings and required workload (e.g., list of pages, data, or configs).
Output: Array of data (e.g., json objects with information about leads, pages, contacts).
…
2. Iterator
Function: Sequentially splits the received array from Apify into separate "packages" — each element of the array becomes a separate loop for further processing.
Input: Array of objects.
Output: One object (array element) per iteration.
3. Tools: Text aggregator
Function: Collects certain text/required fields into one text block, for example, concatenates several elements into one line for sending to AI.
Input: Data from Iterator.
Output: String for AI (e.g., the entire description of a lead/client in one field).
4. Perplexity AI: Create a chat completion
Function: Sends the collected text to Perplexity AI for analysis, extracting structure, or additional enrichment (e.g., for contact recognition, content analysis, summary).
Input: String from the previous block.
Output: Structured block (text/JSON with found key fields).
5. Text parser: Match pattern
Function: Parses the AI response according to the specified pattern (regular expression, template, etc.).
Input: AI response.
Output: Array of found objects that match the pattern.
6. Array aggregator
Function: Collects all received subarrays or individual elements back into a single array (reverse-iterator) for batch adding data to Google Sheets.
Input: Elements received after the parser (there can be many packages).
Output: Updated array for bulk addition.
7. Iterator (again!)
Function: New loop — expands the aggregated array and prepares each individual row for entry into Google Sheets.
Input: Array from Array aggregator.
Output: Separate element per iteration.
8. Google Sheets: Add a Row
Function: Adds the object received in the previous step to the required sheet.
Input: Data of the element (contact, email, date, anything).
Output: New row in Google Sheets.
Why this structure:
This allows for bulk processing from Apify and enrichment through AI for complex unstructured data, resulting in a perfectly prepared structure for Google Sheets. Two chains of Iterator/Aggregator are needed for arrays: first, we parallelize the array into packages-for-AI, then we collect the batch result and expand again for quick addition to the table.
Typical use case:
LinkedIn/email/web scraping → enrich + clean data through AI → parsing and adding a structured list to Google Sheets for further work by sales, marketing, or analytics teams.
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113 USD Create, edit, and manage meetings in Notion
AI & Machine Learning
How it works:
- The user creates or edits a meeting record directly in the Notion working database (for example, fills out a form for the meeting).
- The integration module automatically "reads" new/edited records from Notion and creates the corresponding event in Google Calendar with all the details (time, participants, description).
… - At the same time, a Zoom link for the online meeting is generated.
- Any further changes or deletions of the meeting are made in Notion — and are synchronously reflected in Google Calendar and Zoom, without the need to duplicate actions or manually update information in different systems.
- All information about upcoming and past meetings can be conveniently managed, filtered, and supplemented directly in a single working database.
Advantages:
- All work with scheduling and Zoom links is done in one window — Notion, which significantly saves time and eliminates routine.
- The ability to centrally manage the team's schedule without leaving Notion.
- Automatic synchronization of events and Zoom links, minimizing the risk of "human factor" (errors or missed changes).
- Updates occur in real-time: immediately after edits, creation, or deletion of a task in Notion, everything changes in Google Calendar and Zoom.
This approach is especially useful for teams that systematically work in Notion, value simplicity, and save time in organizing meetings.
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90 USD Integration of meetings and transcriptions from Google Meet / Zoom with Google
Enterprise Resource Planning (ERP)
- Integration of Google Meet / Zoom meetings and transcriptions with Google Sheets: automatic accounting, participants, protocols -
1. The Google Calendar module automatically scans your calendar and looks for new events (meetings), specifically from Google Meet or Zoom;
2. Then, a request with event information is sent to Perplexity AI (for analysis or data enhancement, for example, to identify the type of meeting or obtain additional context);
… 3. The received response is converted by the JSON module from text or API into structured objects;
4. The iterator splits the data array by each meeting participant;
5. Through Google Sheets (Search Rows), it is determined whether this event/participant is already in the table (to avoid duplicates);
6. New unique events and participant data (name, email, time, meeting, transcribe link) are added to the Google Sheet (Google Sheets Add a Row).
The Fireflies.ai service automatically adds a link to the meeting transcription along with its identifier to the same table. This creates a simple and effective system for organizing and storing transcribed online meetings.
Main functionality:
- Event data (Google Meet/Zoom) is collected automatically – no need to copy manually;
- The table contains the names of all participants of each meeting, dates, times, links to videos, and transcriptions;
- Duplicates will be avoided through checks before adding;
* Summary: the entire history of meetings, protocols, and participants is accumulated in Google Sheets, allowing for easy searching, analysis, and use of this data for further analytics or documentation.
This integration is a universal tool for teams that actively work with online meetings and value convenience and complete order in protocols.
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113 USD Automatic note collection in Google with AI processing based on make
AI & Machine Learning
Task
It was necessary to set up a system that automatically tracks new documents in a specific Google Drive folder, analyzes their content using AI, stores the results in Google Sheets, and sends notifications in Telegram. It is important to have a history of document processing in the table and not to duplicate already processed files.
Solution
… A scenario was created in Make, which starts with the Google Drive module (Watch Files in a Folder) and picks up each new document. Next, the Google Docs module retrieves the text content of the document and sends it to Perplexity AI for analysis/summarization in the required format (for example, JSON structure). The received response is parsed by the JSON module, after which the scenario accesses Google Sheets: first, it searches for the corresponding row, then records or updates the data (analysis results, status, processing date). After updating the table, the scenario sends a message to the Telegram Bot with a brief summary — for example, the document title and main conclusion — and ends through the Ignore module. For specific cases, there is a branch where the AI result is immediately sent to Telegram without being recorded in the table (quick notifications).
Result
All new documents that enter the selected Google Drive folder are automatically analyzed by AI without human involvement. Key information is stored in a structured format in Google Sheets, allowing for quick filtering, searching, and building simple analytics on the documents. The owner receives timely notifications in Telegram about each newly processed document, and the risk of missing an important file or processing it again is practically reduced to zero.
In numbers
- 1 scenario in Make that connects Google Drive, Google Docs, Perplexity AI, Google Sheets, and Telegram Bot.
- 100% of new documents in the target folder are automatically analyzed by AI and entered into Google Sheets.
- Processing each document takes from a few seconds to a minute instead of manual reading and data transfer.
- 0 duplications due to searching and updating existing rows in the table before recording new data.
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34 USD Automation of notifications for Telegram Bot + Gmail
AI & Machine Learning
Task: Synchronize data from Google Sheets → Telegram Bot → Gmail for notifications and mailing.
Architecture (Make.com):
… - Google Sheets (trigger: new/changed row).
- Router (branching by condition).
- Telegram Bot → send message to user in chat.
- Gmail → email notification.
Result:
- Automatic notification in Telegram upon new entry in Sheets.
- Parallel email for important events.
- Complete continuity without manual intervention.
Technologies: Make.com, Google Sheets API, Telegram Bot API, Gmail API.
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113 USD Audit of the Creatio CRM system for a training company
Enterprise Resource Planning (ERP)
Task
The company was working with CRM Creatio, but for a small team of 3-4 managers, the system seemed too complex and overloaded. The lack of a clear sales funnel, a confusing interface with unnecessary fields, and the absence of simple dashboards led to managers avoiding work in the CRM, while management did not receive timely analytics on leads, engagement channels, and sales department effectiveness.
Solution
… A comprehensive audit of the current Creatio configuration was conducted in four areas:
1. Structure and interface analysis: forms, fields, directories were reviewed, data duplication and unused elements were identified. A "what to keep / hide / redo" table was prepared to simplify daily work.
2. Sales funnel: the actual path of a lead from the first contact to the deal was tracked, the recording of sources (advertising, website forms, calls) was analyzed, and "gaps" in the data were identified where information was lost or stages were not recorded.
3. Dashboards and reporting: the current analytics capabilities of Creatio were checked, it was proposed to replace complex manual filters with ready-made views and add key dashboards (funnel over a period, leads by channels, manager effectiveness, contact statistics).
4. Recommendations for simplification and automation: a step-by-step change plan was formed with prioritization, including the use of basic automations (reminders, lead distribution, auto-tasks) and AI functions of Creatio (scoring, prioritization).
Result
The company received a detailed audit document with a visualization of the current and future funnel, specific recommendations for simplifying the interface, and a list of tasks for implementation. Management understood what changes needed to be made for the CRM to stop being an obstacle and to transform into a working tool with transparent analytics. Managers received a clear picture of their daily tasks without unnecessary fields and clicks, while the manager received dashboards with key metrics of the funnel, conversions, and team effectiveness.
In numbers
- 4 areas of audit: structure, funnel, analytics, automation.
- 10-15 pages of detailed report with recommendations and visualization of the "was / will be" funnel.
- 5-7 slides of presentation for quick discussion with management.
- Up to 50% reduction in interface complexity due to hiding unused fields and simplifying filters.
- 3-5 ready-made dashboards for daily monitoring of key sales metrics and manager performance.
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113 USD Building a multi-project task management system in Notion
Client Management & CRM
The team was running several projects in parallel and kept tasks in different Notion pages/tables.
Problem: The manager found it difficult to see the overall workload across all directions, control deadlines and responsible parties — they had to "run" between projects manually.
… Goal: To create a single task dashboard that automatically collects and synchronizes tasks from all projects while maintaining the structure within each project.
Architecture in Notion
- A central database "Dashboard All Tasks" was created with main fields: task name, project, responsible person, deadline, description, creation date, status, priority, etc. [file:Screenshot_3.jpg]
- Each project (Project 1, Project 2, …) is a separate page with its own view of the same database, filtered by the field "Project = Project 1" and with its own filters/groupings (for example, by status or responsible person).
Thus, there is essentially one "single task table," and all sections "Project 1–8" are different windows into the same data.
Synchronization logic between projects
When a task is created in any project, it automatically goes into the main database, as it is created as a record in the central table (through a linked view).
- The "Project" field acts as a key that determines in which section of the dashboard it is displayed; changing the project automatically moves the task between sections without duplication.
- Updating the status, deadline, or responsible person in any of the views (in "Project 1" or in "Dashboard All Tasks") is instantly reflected everywhere, as it is the same record.
- If necessary, automatic filters and sorting can be added: for example, showing overdue tasks at the top or tasks of a specific manager for the current week.
Single dashboard for the manager
- The main page "Dashboard All Tasks" shows the overall picture across all projects: a list of tasks, responsible persons, and deadlines in one place. [file:Screenshot_3.jpg]
Here, several views can be configured:
- Kanban by status (To Do / In Progress / Done);
- table by deadlines;
- view "My Tasks" — filtered by a specific user.
This gives the manager a quick overview of the team's workload and critical tasks without switching between individual projects.
Result for the client
- One single task management center instead of scattered lists across projects.
- Automatic synchronization: any change to a task is reflected in all views, with no duplication or data discrepancies.
- A clear picture of the projects: easy to see who is busy with what, where there is a risk of deadline breaches, and which tasks are "hanging."
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Power BI dashboard for the manager
AI & Machine Learning
Task
Create a single sales management center to control revenue and the funnel in real-time, replacing manual reports from various sources.
Solution
… An architecture was developed in Power BI with automatic data updates.
The sales funnel (Conversion Funnel) was visualized from lead to successful deal.
Detailed filtering by suppliers, managers, and advertising campaigns was set up.
Result
Automation: The time for preparing reports has been reduced to zero.
Control: Instant monitoring of actual revenue against forecasted has been implemented.
Efficiency: Weak stages in the funnel were identified, allowing for the optimization of managers' work.
Reviews and compliments on completed projects 21
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Real expert -
Craft master -
Quick answers -
First-class quality -
Nice communication -
High responsibility -
Great price -
Lightning fast
23 March
38 USD
Audit and testing of basic CRM settings (WITHOUT implementation)
Quality
Professionalism
Cost
Contactability
Deadlines
This is not the first time we are reaching out. Completed on time and with quality.
Sergey V.
| Personal | Response review
10 March
79 USD
Аудит та тестування базових налаштувань CRM (БЕЗ впровадження)
Quality
Professionalism
Cost
Contactability
Deadlines
Done quickly and qualitatively according to the technical specifications. Can be recommended.
|
1 March
320 USD
N8n Workflow PRD: Automation of Incoming E-Mail Part 2
Quality
Professionalism
Cost
Contactability
Deadlines
Working with Oleg was comfortable: he quickly immersed himself in the project and was always available if something needed clarification — he explained step by step and in simple language. I also wanted to note that everything was completed within the planned deadlines. Overall, there is a feeling of a reliable specialist whom you can truly depend on. We will be happy to collaborate in the future!)
Dariya J.
| Personal | Response review
23 February
291 USD
N8n Workflow PRD: Automation of Incoming E-Mail Part 1
Quality
Professionalism
Cost
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Deadlines
You did an excellent job with the first stage - you deployed n8n and transferred the data to Airtable.
Dariya J.
| Personal | Response review
17 February
226 USD
N8n workflow
Quality
Professionalism
Cost
Contactability
Deadlines
Quality work done, thank you for the collaboration!
Suren B.
| Personal | Response review
Quality
Professionalism
Cost
Contactability
Deadlines
Everything is great - always in touch and constantly helps with all questions. I recommend.
25 January
45 USD
Set up work in Trello, create templates.
Quality
Professionalism
Cost
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Deadlines
Thank you for the completed work!
29 December 2025
25 USD
Discussion of the project technical specifications for the analysis of telephone conversation automation with Binotel
Quality
Professionalism
Cost
Contactability
Deadlines
I agree with the technical specifications and ask to take this into account in future work.
Dinara Akhmetova
| Personal | Response review
17 December 2025
56 USD
Make/Automation system for YouTube video analytics (Google Sheets + API)
Quality
Professionalism
Cost
Contactability
Deadlines
Quality work done, Oleg is a very knowledgeable and responsible specialist. Always happy to collaborate.
Sergey Sergey
| Personal | Response review
2 December 2025
23 USD
Make/Automation system for YouTube video analytics (Google Sheets + API)
Quality
Professionalism
Cost
Contactability
Deadlines
Thank you once again, it is very pleasant to work with you!
Sergey Sergey
| Personal | Response review
25 November 2025
56 USD
Create/Automation system for YouTube video analytics (Google Sheets + API)
Quality
Professionalism
Cost
Contactability
Deadlines
Professionally and responsibly. Thank you.
Sergey Sergey
| Personal | Response review
19 November 2025
56 USD
Make/Automation system for YouTube video analytics (Google Sheets + API)
Quality
Professionalism
Cost
Contactability
Deadlines
Thank you for the great work. Wait for the next order.
Sergey Sergey
| Personal | Response review
14 November 2025
34 USD
Make/Automation system for YouTube video analytics (Google Sheets + API)
Quality
Professionalism
Cost
Contactability
Deadlines
It is pleasant to work with a specialist in their field.
Sergey Sergey
| Personal | Response review
10 November 2025
23 USD
Make/Automation system for YouTube video analytics (Google Sheets + API)
Quality
Professionalism
Cost
Contactability
Deadlines
Well done, without unnecessary questions.
6 November 2025
23 USD
Website integration + Key CRM
Quality
Professionalism
Cost
Contactability
Deadlines
Professional approach, prompt work!
Nariman Suleymanov
| Personal | Response review
30 October 2025
30 USD
Project Dashboard in Notion
Quality
Professionalism
Cost
Contactability
Deadlines
Everything is great, thank you for the quick work.
6 September 2025
45 USD
Notion Automation - LinkedIn
Quality
Professionalism
Cost
Contactability
Deadlines
Great job! Thank you
8 July 2025
16 USD
Task: Setting up lead generation through a WordPress site
Quality
Professionalism
Cost
Contactability
Deadlines
Everything is good, thank you, I did the work perfectly.
Vladislav Dobrovolsky
| Personal | Response review
12 May 2025
113 USD
Crm notion
Quality
Professionalism
Cost
Contactability
Deadlines
Thank you Oleg for the quick and quality work. All comments have been addressed, we recommend!
Sergey Buziyan
| Personal | Response review
19 April 2025
68 USD
Set up CRM for Lead visibility
Quality
Professionalism
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Deadlines
Thank you. There was a delay on my part. But Oleg is a composed specialist and brought the project to completion. Thank you. I recommend.
26 January 2024
5 USD
Create location tag for Instagram
Quality
Professionalism
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Deadlines
Unfortunately it didn’t come out, but thank you for the work you did.
Activity
| Projects underway 1 | Budget | Added | Deadlines | Proposal | |
|---|---|---|---|---|---|
|
Set up KeyCRM
609 USD
|