Budget: 10000 UAH Deadline: 7 days
Hello. I have experience with n8n/Make. I am ready to implement automation. Feel free to contact me.
Budget: 4500 UAH Deadline: 3 days
Hello.
I can do automation for you. Write to me, and we will discuss.
Budget: 26650 UAH Deadline: 10 days
We can implement it in stages: first a stable pipeline from Calendly to ClickUp/Google Sheets with AI summaries and statuses, then Gmail drafts and follow-up logic depending on the client's response. To reduce risks, I would suggest starting with an MVP on Make/Zapier + OpenAI + Gmail/ClickUp, and after testing on real leads, refine the status rules and email templates.
Budget: 5000 UAH Deadline: 6 days
Hello! My name is Nikita. I have been implementing AI solutions in paid advertising and automating marketing processes for over 2 years, working with Google Ads, Meta Ads, and TikTok Ads.
✅What you get when working with me:
— AI-enhanced advertising strategy instead of chaotic launches
— automation of analytics and project economic control
— systematic scaling based on data and AI tools
📈I work with projects of various scales and use AI for faster analysis of results, finding growth points, and optimizing advertising processes without unnecessary costs.
I am ready to discuss your tasks and offer a practical plan for implementing AI in your project's advertising.
Budget: 3499 UAH Deadline: 5 days
I have reviewed your task. I will implement it on n8n — this is an alternative to Make/Zapier, but without limits on the number of operations.
What I will specifically do: Calendly sends a webhook → n8n parses the data → OpenAI generates a short summary and determines the lead status → a lead is automatically created in ClickUp (or Google Sheets) → AI prepares a draft email in Gmail for your review before sending → follow-up triggers not by timer, but depending on the client's response.
CRM statuses: New Lead / Waiting Reply / Hot Lead / Closed — are updated automatically.
Timeline: 5–7 days. Budget: 3,000–4,000 UAH.
An example of your work is already in my portfolio, the first job Freelancehunt
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Budget: 25000 UAH Deadline: 7 days
Hello, Serhiy!
To gain a detailed understanding of your project, please clarify:
1. Do you already have integrations set up between Calendly, ClickUp/Google Sheets, and Gmail, or does everything need to be done from scratch?
2. What specific data from the application should be analyzed to create a summary and determine the lead status?
3. Are there already prepared templates for follow-up emails, or do they need to be developed?
4. What volume of leads is planned to be processed monthly?
My approach to implementation:
1. I will set up the integration between Calendly, ClickUp/Google Sheets, and Gmail using Make or Zapier.
2. I will use OpenAI to analyze applications, create summaries, and determine lead status.
3. I will configure the system to automatically create draft emails in Gmail so you can review the emails before sending.
4. I will set up the logic for automatic follow-up emails based on the client's response, not just on a timer.
5. I will implement basic CRM logic for managing lead statuses.
For examples of similar automations and an estimated tech stack, please write to me in private messages. After clarifying the details, I will be able to propose exact timelines and budget.
Budget: 3000 UAH Deadline: 4 days
Hello! I am ready to complete your task, I have experience working on similar projects.
- Projects -
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- Rating 496
Budget: 10000 UAH Deadline: 1 day
Hello!
We are dZENcode – a full-cycle digital solutions development company: from design and programming to integrations and post-release support. We take on projects from scratch and also engage in the refinement of existing solutions.
We can create AI automation for lead processing and follow-up tailored to this task.
Which service is more convenient for linking leads – ClickUp or Google Sheets? Is automatic email sending needed after manually checking the draft?
You can find detailed information about our services and rates on our website: Freelancehunt
Take a look – after that we can discuss the details and agree on the next step.
⚠️ After clarifying all the details, we will determine the scope, suitable format of cooperation: task-based, outsourcing, or outstaffing, and the final cost.
Why projects with us are guaranteed to reach release:
💎 10+ years providing IT services;
🔥 90+ in-house specialists;
🚀 250+ public reviews since 2015;
⚙️ We support the product under SLA after launch;
✅ We work under NDA and a contract with the company!
Budget: 4200 UAH Deadline: 3 days
Good day.
I specialize in no-code automations and have practical experience setting up various types of automations in Make.com, including connecting Calendly, Clickup, OpenAI, Gmail, and various CRMs via API.
I can set up automatic AI qualification and lead processing for you.
Examples of similar automations are in my portfolio, and I can provide more in private.
Feel free to reach out!)
Vitalii Karasov
Winning proposal- Projects -
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- Rating 501
Budget: 4500 UAH Deadline: 7 days
Hello! I regularly build such pipelines — Make + OpenAI + ClickUp/Sheets + Gmail drafts. MSc in Strategic PM (Lazarski), PRINCE2, 4 years of experience in product — helps to set up the CRM logic correctly, not just the technical integration. Detailed examples of similar lead qualification systems are in my profile)
I can do end-to-end in 7 days with testing.
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- Rating 457
Budget: 5000 UAH Deadline: 7 days
I see that the main problem here lies not only in integrating Calendly with ClickUp, but in building a system that automatically qualifies leads and takes into account the context of further inquiries, instead of launching template sequences.
I have created similar marketing ecosystems based on artificial intelligence, using Make.com, OpenAI, CRM, and automated email flows. In one project for an electrical services company, I built a fully automated lead pipeline with instant lead capture, CRM routing, follow-up inquiries using AI, and analytical dashboards, which reduced response time from 18 hours to less than 1 hour.
For your workflow, I would structure it like this:
• Calendly → Make.com → ClickUp Automation with configured lead qualification stages and AI-based summaries
• Lead qualification based on GPT + personalized Gmail drafts based on booking data
• Smart follow-up logic that adapts to responses, lead status, or inactivity instead of fixed timers
• Centralized tracking so your team always sees lead intentions and conversation history
One thing I would pay special attention to is preventing duplication of automation between ClickUp and Make.com — this is where workflows usually become unstable over time.
Do you already have a defined lead qualification structure, or should the AI-based evaluation logic be developed from scratch?
Budget: 1000 UAH Deadline: 1 day
I am among the top 10 developers in the category of "Artificial Intelligence and Machine Learning" among ~2100 specialists on the platform.
I guarantee:
- Fast and quality task execution
- Strict adherence to deadlines
- Regular communication throughout the entire process
I would be happy to discuss the details of your project in private messages.
Budget: 2000 UAH Deadline: 3 days
Hello! Your project looks very interesting. I am ready to start working immediately and ensure high quality.
Budget: 4500 UAH Deadline: 14 days
Hello, Serhiy!
I will build a system on Make that will turn Calendly into an active sales tool.
AI will prepare drafts based on requests, leaving you with full control before sending. Special attention will be given to smart follow-ups: the system will analyze the context of the client's response, rather than just counting time. This will allow you to focus only on hot leads.
Deadline: 10-14 days.
I am ready to discuss the details and propose the optimal implementation option.
Budget: 5000 UAH Deadline: 14 days
Good day
Although I have more experience in n8n, I am ready to take on your task.
Regarding the timeline - up to 2 weeks.
Proposals are currently absent
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It is necessary to develop a service based on Claude or another suitable AI model that can automatically find competitor companies, contact them via email and phone, gather necessary information, and enter the results into a single table. Main task of the service The user specifies a specific request, for example: - to find out the price of a certain type of meat; - to find out the price of a specific type of wood; - to clarify the cost of a product or service; - to check the availability of a product; - to find out the delivery times; - to get the terms of cooperation. After that, the system should: 1. Find suitable companies. 2. Collect their contact details. 3. Send them personalized emails. 4. Call the companies using an AI bot. 5. Get answers to the questions asked. 6. Save all results in Google Sheets or another table. For each new task, the user should be able to change search queries, selection criteria, email text, and phone call script. Stage 1. Company database collection Stage 2. Email distribution and response collection Stage 3. AI calling of companies --- Important technical requirements - Ability to use Claude for generating emails, analyzing responses, and managing dialogue. - Ability to replace the AI model without a complete system overhaul. - Integration with Google Sheets. - Integration with Gmail or another email service. - Integration with an AI telephony service. - History of all emails, calls, and changes. - Ability to stop or pause a task. - Control of expenses for calls, emails, and AI requests. - Limitation on the number of calls per day. - Protection against resending emails and making repeated calls to the same company. - Compliance with legislation on phone calls, recording conversations, email distributions, and the use of personal data. --- Expected result As a result, there should be a service in which the user creates a task, specifies what information needs to be obtained, selects geography and sources, after which the system independently: 1. Collects a database of companies. 2. Finds contact details. 3. Sends personalized emails. 4. Calls companies according to the specified script. 5. Analyzes responses. 6. Forms a single table with results. 7. Shows from which source each piece of information was obtained. For the first discussion with the developer, it is also worth asking to separately estimate the cost and timelines for MVP, automatic parsing, AI telephony, and monthly infrastructure.
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We are looking for a specialist in LLM, RAG, and prompt engineering for auditing and improving an already created AI assistant for contact center operators of a network of medical centers. This is not a development from scratch. Currently, the assistant operates in the ChatGPT environment and uses: its own skill with instructions SKILL.md; a knowledge base in Project Sources; structured Markdown files automatically generated from CSV exports of the medical information system; separate indexes of prices, performers, departments, packages, equipment, and recommended service combinations. The database contains approximately: 2,700+ medical services; 90+ packages and complexes; 300+ recommended combinations; 600+ surgical interventions; prices by various departments; performers, addresses, preparation, equipment, and other reference information.What the assistant should do Upon the operator's request, the assistant should quickly provide a verified response: whether the required service is provided; the exact code, name, and price; in which departments it is available; which doctors or other specialists perform it; how to prepare; which package or complex is more advantageous to offer; which accompanying services are advisable to suggest; the sequence of comprehensive patient registration; what cheaper or alternative options are available; for operations — separately the base price and the estimated total cost of the surgical treatment case. The assistant should not invent prices, performers, preparation, medical indications, or transfer information between similar services.Existing problems The system is already operational but requires increased search stability and response quality. In particular: the model sometimes finds the main service but misses recommended combinations; does not always extract individual fields from large Markdown files; can find the base price of an operation but miss the total cost of the surgical case; results depend on the structure and size of the files in Project Sources; indexes, source routing, and search rules need optimization; it is necessary to ensure equally high-quality responses to short, inaccurate, and conversational operator queries. For example, a simple query "cholecystectomy" should immediately return available options for the operation, codes, base prices, total treatment costs, departments, performers, and related services.Specialist tasks Conduct an audit of the current SKILL.md, the structure of the knowledge base, and the search logic. Analyze the reasons for data omission during retrieval. Propose an optimal knowledge base architecture for ChatGPT. Improve or rewrite SKILL.md. Optimize the structure of Markdown files and compact indexes. Set up mandatory searches: packages and complexes; recommended combinations; prices by departments; performers; the cost of the surgical treatment case. Check the knowledge base generator from CSV exports and improve Python scripts if necessary. Create a set of control queries and criteria for evaluating responses. Conduct testing on real contact center scenarios. Provide final documentation for further updates and system support.Expected result We expect to have a stable assistant that: responds in Ukrainian; does not miss critically important data; returns only information confirmed by the knowledge base; correctly distinguishes between services, packages, and recommended combinations; shows code, name, price, department, and performer; for operations separates the base price and total treatment cost; offers the operator a specific scenario for further patient registration; works stably after subsequent updates of CSV exports; operates relatively quickly.Requirements for the performer A specialist with practical experience is needed: ChatGPT Projects, Custom GPT, or ChatGPT Skills; LLM, RAG, retrieval, and semantic search; prompt engineering; designing knowledge bases for language models; Markdown, CSV, JSON/JSONL; Python for data processing and transformation; testing the quality of LLM responses. Experience with medical information systems, contact centers, or large service catalogs will be an advantage. We are looking for not just a prompt author, but a specialist who understands the limitations of searching in large sources, context fragmentation, and ways to build reliable indexes.What to provide in the proposal Please briefly indicate: Your experience with ChatGPT, RAG, or corporate knowledge bases. Examples of similar implemented projects. How you would approach diagnosing the omission of individual fields in large files. Estimated timelines and costs for the audit and refinement. Whether you are willing to sign a confidentiality agreement. Personal data of patients will not be transferred within this project. The final cost of the work will be agreed upon after clarifying the Technical Task between the Customer and the Performer.
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