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Technical specification (full functionality described) Project AI Sales Agent for automatic search of potential clients, personalized email distribution, and lead processing. The initial business for which the AI Agent is being developed is intended for the project "platform for builders and construction equipment in Germany and the EU. Website: https://prostobau.de/ The goal is to create a fully autonomous system that can operate almost without my involvement and easily switch to any other business niche if necessary. Overall architecture The system should be modular. Each module should operate independently. For example: company search; website analysis; email search; letter generation; sending; response processing; reports. If it is necessary to replace Apify or OpenAI with another service, this should happen without rewriting the entire system. Company search sources At the first stage, use: Google Maps via Apify. In the future, there should be an easy way to connect: LinkedIn; Facebook; Gelbe Seiten; Yelp; any other sources. Company search Every day, the system automatically retrieves about 100 new companies based on the specified search query. For example: Baumaschinenvermietung Bayern Bauunternehmen Nürnberg Trockenbau München The search should be fully customizable. I should be able to change: niche; country; region; language; search queries; number of companies. Without the help of a programmer. Duplicate check Before processing, the company is checked by: domain; email; phone; Google Maps URL; name. A company should never receive the first letter twice. Website analysis If a website is found, the agent must: study what the company does; what services it provides; what products it sells; operating region; contact details; possible owner name; email. After analysis, create a brief description of the company. Email search Priority: personal work addresses → sales → vertrieb → kontakt → info. If there is no email — skip the company. Letter generation Via OpenAI. Each letter must be unique. Do not use template-based word replacement. The letter should consider the content of the website. Style: brief friendly professional without pressure without fabricated facts maximum 120 words. Letter check Before sending, the letter must be automatically checked. If errors are found: mistakes; fabricated information; too long text; poor personalization, the letter must be recreated. Sending letters Do not use the project's corporate email. Create a separate new domain for outbound email. For example, a separate similar domain. Create new mailboxes. Set up: SPF DKIM DMARC It is essential to organize proper email warming. After warming, aim for about 100 new emails per day. Emails should be sent gradually throughout the working day. Not all at once. Follow-up After 5 days from the first letter. Only if: no response; no bounce; the company did not request to stop writing. Only one follow-up. After the second letter, the chain ends. Response processing The system must automatically: read incoming letters. Determine: interested; not interested; request to stop writing; automatic response; vacation; manual response needed. After a response, automatically stop any further letters. Database Store: companies; websites; emails; letter history; responses; statuses; search queries; errors; processing date; follow-up. Dashboard A convenient web interface (desktop) is needed, not just n8n. I want to see: Main is the agent working; current status; how many companies found; how many processed; how many sent; how many responses; how many errors; how many follow-ups; how many interested. Companies Table. Filters. Search. History. Search queries Add Delete Edit Priority Region Country Number of companies. Settings Without a programmer, the following should be changeable: OpenAI model; email account; domain; number of letters; limits; language; template; sending time; number of companies; follow-up period; business niche. Logs All errors. All actions. Work history. Emergency stop Large button STOP After pressing: immediately stop search; generation; sending; follow-up. Restart With one button. Operating modes Test Everything is executed except sending. You can check letters. Automatic Fully autonomous operation. Scaling In the future, there should be the ability to: add multiple email accounts; multiple domains; multiple projects; multiple niches; multiple countries; multiple languages. Without a complete system overhaul. Daily report (organize conveniently, either by email to the owner or in Telegram via a bot) At the end of the day, automatically receive: companies found; new; duplicates; emails found; letters sent; follow-ups; responses; interested; errors. Security Store all API keys securely. No keys inside the workflow. Documentation After project completion, provide: launch instruction; settings change instruction; architecture description; export of all workflows; access to all services. Important The project should be built not only for ProstoBau. I want to receive a universal platform for client search that can be used in the future for almost any business by simply changing: niche; search queries; letter templates; email; domain; country. Without involving a programmer.

A local Python script needs to be developed to automatically fill a Google Sheet with data from the company's internal service. Main logic: 1. Connect to the Google Sheet. 2. Find rows where the ID is filled but two target values are missing. 3. Form a link based on the template: https://internal-service.example/item/{ID} 4. Retrieve the two values (via API, if it exists, otherwise via Playwright). 5. Write the values back to the Google Sheet. 6. Mark the row as processed. 7. Continue processing the next rows. Requirements: • Python • Google Sheets API • Priority to use the official API • If no API — Playwright • No OCR, screen recognition, or mouse coordinates • Confidential data must not be logged • Configuration via .env • Test mode (without writing to the sheet) • Do not process already filled rows • Batch write changes to Google Sheets • Proper error handling and retries It is necessary to provide: - source code; - requirements.txt; - example .env.example; - installation instructions; - running instructions; - brief architecture description. Before starting implementation, please: 1. Suggest an architecture. 2. List the necessary accesses. 3. Ask clarifying questions. 4. Indicate the cost, deadlines, and estimated number of hours.

As part of enhancing the cybersecurity level of our infrastructure, we need to abandon the practice of storing "eternal" and static API keys, passwords, and integration tokens in the configuration files (.env, appsettings.json, config.yaml) of our microservices. Business Goal: Create a single secure storage point for confidential data (secrets) with a mechanism for their automatic updating (rotation) in external systems on a schedule. Our other services will request current tokens "on the fly" via API, which will minimize damage in case of compromise of any system component.Security Model and Encryption (Crypto Core) No secret should be stored in plaintext in the database. Upon application startup, a Master Key is passed to the environment variables. If the key is missing or has an invalid length, the service should fail at the initialization stage with a clear error in the logs. Each secret is encrypted before being written to the database using this Master Key. Upon request, it is decrypted in memory and returned in the response body.Audit Logging (Audit Trail) Any action with secrets (creation, reading by the service, successful or unsuccessful rotation) must be recorded in a separate log file audit.log (or a separate table in the database). Strict Taboo: It is strictly prohibited to record the actual values of secrets in the audit log (neither in plaintext nor in encrypted form).

Need a specialist for writing parsers who can bypass CLOUDFRAME. Parsing of products occurs from sites with authorization. There are 10+ donors of varying complexity, with different levels of protection. Parsing of products occurs from sites with authorization. Parses data into a ready-made Mysql database + photographs on the server. It is necessary to write a parser according to the tasks described in the technical assignment and adapt the data to the existing database for full functionality on the site. Technical assignment and example donor upon request. Desktop parsers and C# are not considered.

Bot for mirroring positions on Binance Futures (Python) A bot is needed that reads my positions on Hyperliquid (public API) and Bitget Futures (my read-only key) and proportionally replicates them on my Binance USDT-M Futures via API. Logic: opening, increasing, partial closing, full closing — everything is mirrored with a customizable size ratio. Polling every 5–10 seconds. Correct handling of partial closures and averaging is mandatory. Requirements: notifications in Telegram about trades and errors; config (pairs, ratio, limits); deployment on my VPS + instructions; source code is transferred to me. I will enter the keys myself. Stages: 1) Hyperliquid→Binance, test with small amounts; 2) Bitget→Binance. Payment through safe in stages. In your response, indicate your experience with exchange APIs and how you will handle a partial closure of 30% of the position by the leader.