Budget: 27000 UAH Deadline: 2 days
Hello! I haven't worked with artificial intelligence, but I am very interested in it. I will do the task for free.
Goal:
Reduce the workload on the HR department, optimize communication with candidates and employees, and improve the efficiency of recruitment and onboarding processes.
Candidate selection:
- Automatic analysis of resumes and formation of a shortlist of candidates based on specified criteria (experience, skills, language, etc.)
- Conducting initial "AI interview" in the format of chat or voice dialogue
Employee onboarding:
- Providing new employees with basic information about the company, policies, contacts, internal instructions
- Forming a personal adaptation plan
Internal HR support:
- Answers to typical employee questions (vacations, sick leaves, corporate rules)
- Integration with Slack for convenient access
Analytics and reporting:
- Collecting statistics on candidate feedback, communication effectiveness, hiring, and employee retention
For clarification of data - join in the comments and add relevant work
Thank you for your attention!
Budget: 27000 UAH Deadline: 2 days
Hello! I haven't worked with artificial intelligence, but I am very interested in it. I will do the task for free.
Budget: 27000 UAH Deadline: 14 days
Greetings!
We are a team of 4 developers from The Dev Company, specializing in Fullstack development (TypeScript, JavaScript, Node.js), as well as creating AI solutions using OpenAI (GPT-4), integrations through Slack, Firebase/Supabase, and web frameworks Next.js and Vite.
We can develop an intelligent HR bot that automates recruiting, onboarding, and internal HR support – from resume analysis to employee consultations in Slack.
How we propose to implement the project
– Analysis and architecture — we describe the scenarios of HR processes, the structure of the knowledge base, and the logic of the bot's communication.
– AI module (GPT-4 or another model) — training the bot on your documents, job templates, and company policies.
– Candidate selection functionality — processing resumes, preliminary assessment, AI interviews (chat or voice).
– Onboarding — personalized adaptation plan, answers to frequently asked questions, access through Slack.
– HR analytics — collecting data on the effectiveness of communication, engagement, and employee retention.
– Integrations — Slack, Google Workspace, CRM, or the company's internal portal.
Collaboration options
–> Option 1 — MVP (quick launch):
Development of a working prototype of the HR bot with basic functions (candidate selection + answers to typical inquiries + Slack integration).
Timeline: 2–3 weeks
Cost: from $1800
–> Option 2 — Full system:
Development of a scalable HR platform with analytics, onboarding of new employees, a knowledge base, and individual AI modules for each stage of the HR process.
Timeline: 6–8 weeks
Cost: from $3500
We have experience creating similar AI bots for businesses, as well as developing voice AI bots that operate through regular telephony (see past projects).
We are ready to engage in discussion, share the architectural scheme, and assist in forming the project roadmap.
Best regards,
The Dev Company
Budget: 27000 UAH Deadline: 12 days
Good day.
I am ready to take your project into work.
I can develop such a bot for you using n8n.
Write to me privately, we will discuss all the details and can start the implementation.
Budget: 27000 UAH Deadline: 15 days
Hello, Sergey. I have experience setting up bots for various tasks (Make.com service). AI assistants can be connected to conduct resume analysis and interview processes according to your instructions. This saves a lot of time and filters out candidates who do not meet your criteria. In the second stage, the bot works with employees as support 24/7. It can connect various services, databases, and spreadsheets for reporting. My profile: Freelancehunt
Budget: 26998 UAH Deadline: 4 days
Hello! I am ready to complete this project and have extensive experience in developing various applications.
Budget: 27000 UAH Deadline: 10 days
Good day. I have a ready solution, or rather several solutions from third-party developers that I am helping to implement.
I can provide more information in personal communication.
Budget: 27000 UAH Deadline: 21 days
Hello.
I can develop a similar bot based on n8n or PHP, there are options for implementing your ideas.
Feel free to reach out, we can discuss :)
Budget: 27000 UAH Deadline: 30 days
Good day, Serhiy!
Your request to create an intelligent HR bot is exactly the level of architectural tasks I specialize in.
If you are looking for not just a developer, but a systems integrator capable of building a full-fledged AI ecosystem for HR, this aligns 100% with my expertise.
I see this project not as the creation of a single monolithic bot, but as the development of a system consisting of several interconnected AI agents, each responsible for its area, but working within a unified logic.
My vision for implementing your tasks:
Recruitment Agent:
This is the most complex and valuable part. I propose to implement it in two stages:
Automatic resume screening: We will create a system based on RAG (Retrieval-Augmented Generation). Simply put, we will upload all resumes into a special vector database. After that, the HR manager will be able to make queries in natural language (e.g., "find me all candidates with 3 years of Python experience and English knowledge B2+"), and the AI will instantly generate a shortlist.
AI interview: For the selected candidates, we will launch a state-driven dialogue agent (built on n8n), which will conduct an initial structured interview, checking basic skills and motivation.
Onboarding & Support Agent:
This will be an AI agent trained on your internal knowledge base (policies, instructions, rules). It will be integrated into Slack, where new employees can ask it any questions ("where to find the vacation template?", "who is responsible for procurement?") and receive instant answers 24/7.
Analytics Agent:
All interactions with candidates and employees will be automatically logged in a structured format (e.g., in Google Sheets or Airtable). This will allow for building reports and dashboards to analyze recruitment effectiveness and identify problem areas in onboarding.
My relevant experience:
State-driven dialogues: My experience in creating AI agents for real estate companies and legal businesses includes developing complex dialogue scenarios, which are the foundation for the "AI interview."
I am ready not just to complete the task, but to act as your technical partner, helping to design and implement this system "turnkey."
I propose to hold a 30-minute architectural session where we can detail the candidate screening process and sketch out the workflow of the first AI agent.
Budget: 27000 UAH Deadline: 10 days
Good day, Serhiy
I can implement an HR bot for automating recruitment, onboarding, and employee support based on Make.com with integration to Telegram (or another convenient messenger) and Slack.
What I can offer:
• Automatic resume analysis through a form or API with entry into the database;
• Conducting AI interviews in chat format (via GPT or ready-made integration);
• Providing basic information to new employees (onboarding);
• Answers to common questions via the bot;
• Integration with Slack for the HR team;
• Collecting analytics: feedback, communication statistics, onboarding, effectiveness.
Technologies: Make (Integromat), Telegram Bot API, Slack API, Google Sheets / Airtable / Notion (as a database).
I work without code, but with the possibility to add custom logic if needed.
I would be happy to discuss the details of the task and help create a solution that reduces the workload of the HR department.
Hello everyone! There are currently many sellers on the market selling claude tokens through proxies. I need to test different sellers and understand which API works most reliably for coding claude code. You will need to authenticate through the terminal in claude code and monitor the stability of the API (run it under load) and find the most optimal one. Who can take this on right now?
I want a bot ready if there is a solution - write beginners - please do not disturb only experienced ones, those who have managed and know thank you.
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.
I'm looking for a performer to build an AI-agent system that automates the marketing and sales pipeline: from content generation to lead segmentation in CRM and hypothesis analytics. Below are the tasks grouped by functional purpose. How many services/agents will be in the final architecture and on which stack is up to you, based on your own experience. The main thing is that the solution covers all the tasks below, is functional, maintainable, scalable, and must have a convenient mechanism for review/approval of results by a person before publication or launching ads. Block 1. Content Generation (Multichannel Copywriting)One or several generating modules that produce texts for various formats based on project materials (course programs, interviews, broadcasts): Landing page texts Email sequences: warming chains, newsletters Posts in Telegram bot/channel Advertising texts for Meta, including variants for different hypotheses Content plan and posts for Facebook / Instagram / Telegram Blog articles (including based on transcribed videos — see Block 3)Block 2. Production of Final MaterialsTransforming the finished text into a final artifact ready for publication: Lead magnets — layout and assembly of a ready PDF (checklists, guides) with Canva/Figma integration for editing Meta advertising creatives — static visuals with resizes for campaign formats Landing pages on Framer — page structure, CMS filling, assembly of a ready page for launch.Block 3. Video → Blog Transcription of videos (broadcasts, interviews, workshops) Cutting the transcript into articles that lead into the funnel Publication in the blog on the main site (there is currently no blog — possibly a blog section needed on Framer CMS; open question: can the system create it itself, or is this a separate task)Block 4. Integration with Meta Advertising Cabinet Uploading finished texts and creatives to the cabinet Tagging ads/campaigns by hypothesesBlock 5. CRM and Lead Routing Automatic segmentation of leads in the Telegram bot (attended/did not attend the event, funnel branch, offer) Transferring segments to CRM Auto-tagging new leads in CRM when entering the funnel (through registration)Block 6. Hypothesis Analytics Formation of a table/dashboard of hypotheses: costs/results for each funnel, lead magnet, creative. Periodic AI analysis with recommendations: what to scale, what to stop Evaluation of landing page conversion ratesWhat We Expect in the Response Architecture Vision — how would you break these 6 blocks into agents/services, on which stack (orchestration, generation, data storage). Portfolio/Cases — examples of similar automation systems (marketing, leads, content generation). Estimated assessment of timelines and costs, preferably step by step. Questions about the Terms of Reference, if something is unclear.
We are looking for an experienced Python/AI developer (or a small team) to create an AI service based on a Telegram bot. The project automates the acceptance of orders from masters and service centers on an active B2B marketplace for spare parts for mobile devices and electronics (catalog of ~30,000 SKUs). What the bot should do:Multimodal input: accept text, voice messages (Whisper API), and photos of parts/markings (GPT-4o Vision), recognize nomenclature, revisions, and part numbers.Smart search: match masters' jargon (battery, accumulator, display, original) with official names through vector search (Vector DB).Interactive UI: when there are several quality options (Original, High Copy, etc.) — group them into one message, display inline checkboxes with prices and stock, recalculate the cart "on the fly".Cascading clarifications (Slot Filling): when faced with an ambiguous request, clarify parameters step by step (model → color → revision).Order processing: automatically send the completed JSON order package to the website via API after confirming the cart.Stack: Python (FastAPI), Aiogram 3.x, OpenAI API (GPT-4o + Whisper), Qdrant, Docker + docker-compose. Implementation period: 5–7 weeks.⚠️ Mandatory condition for response: To filter out auto-responses from spam bots, please indicate in your message: which embedding model you will choose for vector semantic search across 30,000 technical SKUs and why? Responses with template text that do not answer this question will be automatically rejected.