Automation of Instagram Reels analysis through n8n and LLM (Gemini)

We specialize in obtaining grants for existing companies that plan investments. We came up with the idea of creating/connecting available AI models to search for companies that are planning or are already in the process of investing and to offer them grants for this purpose (we mainly deal with grants for the purchase of machinery and equipment). The AI would search available websites and registries to find investors. Then, it would also handle the automation of mailing to these companies and would filter out companies that: - fit into the lead categories (I think, 5-6 parameters); - replied to the email and are interested in the grant; Of course, additional suggestions are welcome; I am also considering Voice Calling and SMS sending. I would like to emphasize that I am not only interested in finding LARGE or very LARGE companies, but also smaller ones, for example, employing 8-50 people. And not just large cities, but also smaller towns. Sample target for testing: SUBCARPATHIAN For micro, small, and medium-sized enterprises (SMEs) and large enterprises from the Subcarpathian Voivodeship that belong to the security and defense industry. An enterprise from the security and defense industry is understood as an enterprise that: develops or manufactures products, technologies, or systems intended for military applications, related to national security, critical infrastructure protection, or citizen safety or provides services supporting security and defense systems (e.g., in the field of cybersecurity) or conducts research and development in the area of military technologies and security systems and which achieves a significant share of revenue from the sale of products, technologies, systems, or services in the area of security and defense, i.e., above 5% of total net revenue (cumulatively over the last 3 closed accounting periods). Support available for companies that conduct business in an organized and continuous manner in the Subcarpathian Voivodeship for a period of no less than 24 months counting back from the date of the call for applications. We have proposals for websites to search. Ultimately, the program would prepare a daily summary of such investors for us. Can someone help us with this? I read that it is possible, but I don't have time to deal with it myself :)

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.

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'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.