Автоматизація flow обробки Lumen Database скарг та Google Counter notice form

Hello, Yelyzaveta! The categories are chosen absolutely correctly, as the task requires building a clear Backend automator. I am ready to implement a stable and transparent system for you in Python.

I suggest dividing the development into logical modules for maximum reliability:

Outlook Flow: Automatic email reading, extracting verification links, and authorization on Lumen using Playwright (it handles edge cases much better than Selenium).

Filtering and AI: The script will automatically filter only your domains based on the whitelist configuration and generate legally correct appeal texts through the AI API using a clear structured template (without any "creativity" from the neural network).

Control and Storage: We will create a centralized database (for example, SQLite or PostgreSQL) for tracking Report ID, logs, and statuses of each complaint.
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Submission: I recommend implementing a semi-automatic mode (the system prepares the case, and the manager makes the final click), which completely eliminates the risk of blocks from Google.

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We already have a nearly ready similar solution for processing complaints, which can be quickly adapted and launched; we can discuss it here now - I'm available ))
The benchmark for the first working module is from 120,000 UAH and about 20 working days.
I would do this as an operator panel with a queue of complaints, checking Outlook, extracting full URLs, filtering your domains, generating appeal text via AI API, logs, and statuses for each Report ID.
It's better to keep the AI API not fully autonomous at the submission stage, but with manual confirmation before the Google Counter Notice - there's a nuance here, as the legal text must be approved by you.
Please clarify how many domains and Outlook accounts need to be processed at the start.
Another question - should the Google Counter Notice be sent completely automatically, or is a draft mode with a confirmation button for the operator needed?
Similar cases in terms of automation logic and AI processes - https://business.ingello.com/fractal and https://business.ingello.com/vorfahr.
As an example of a corporate system with roles, data, and processes - https://business.ingello.com/platforma.
Our profile on such systems - https://systems-fl.ingello.com/ua.
… Overall, it's a normal task for a quick MVP, and then we can build up analytics, re-checks, and quality control of responses =/---

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Hello.

For this project, I would start with a stable semi-automatic MVP: the system collects the case, goes through the verification flow, extracts full URLs, filters the necessary domains, prepares the appeal text, and saves the entire chain in statuses and logs.

The key here is to make the process controllable: a separate state for each complaint, saving the Report ID, error history, reprocessing after a failure, and manual confirmation at the final step of the Google Counter Notice. For such forms, this is more practical, as the final submission has legal weight.

Technically, I suggest: Python, Playwright for the browser part, Microsoft Graph/MSAL for Outlook, PostgreSQL for cases/logs, AI API through a template with fixed mandatory fields.

Before starting work, I need to clarify:
… how many Outlook accounts and complaints are processed per day
whether full automatic submission is needed, or final confirmation by a person
which domains and exclusion rules we are using
what format of reporting is needed after processing

We can discuss the details in private messages.

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I understood the specifications: a complete flow for processing DMCA complaints through Lumen Database — we receive the Outlook verification email, go through the OAuth flow, parse the full URL from the complaint, filter only our domains, generate the appeal text using LLM, semi-automate the submission of Google Counter Notice through a form, and store the Report ID along with statuses and logs. All of this in one pipeline with centralized storage.

Regarding the components as I see them:

Outlook verification flow. Microsoft Graph API via MSAL — we obtain the verification email, parse the magic link, and automatically complete the verification. If Lumen requires a click through the browser with cookies — Playwright with persistent context, not Selenium (Playwright is more stable on CAPTCHA edge cases and has better network interception). The request to Lumen API for the full URL in the complaint is made with a verified session.

URL filtering. Tldextract plus your whitelist of domains. We will extract the filter into a separate config so you can add/remove domains without redeploying.

Appeal text generation. Claude API with a structured prompt, where the model receives the context of the complaint (claimant, type of content, URL) and returns a legally correct counter notice based on the DMCA section 512(g) template. It is important here not to give the model "creativity" — each counter notice must contain mandatory points by law (good faith statement, consent to jurisdiction, signature line). Therefore, this is not a free-form prompt, but a filled template with several dynamic fields, where the LLM describes only "why the content is non-infringing" for the specific case. This reduces legal risk.
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Google Counter Notice submission. Without an official API — only browser automation. Playwright with retry logic on reCAPTCHA (2Captcha as a fallback, but I consider it optional due to moral factors + cost). Or semi-automation: the bot prepares a filled form as a draft URL for a person for the final click. I would recommend the second option — a fully automatic submission of the Counter Notice without human review is legally slippery, as the notice includes your signature and testimony under penalty of perjury. Human-in-the-loop at the final step protects the company.

Storage. PostgreSQL with a complaints table (lumen_report_id, claimant, url, our_domain, received_at, verification_status, appeal_text, counter_notice_status, submitted_at, google_response). Logs in a separate append-only table with a complete audit trail of who did what and when, plus export to CSV/Sheets for legal reporting.

Architecture. FastAPI as a wrapper, Celery or APScheduler for cron processing of incoming emails every N minutes, a separate worker for Playwright tasks (as they are heavy and should not...

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Hello!

I have sufficient experience in browser automation, AI API integrations, email flows, Playwright/Puppeteer, form submission automation, and building centralized processing systems with logging and statuses. I have also worked with AI-generated workflows, Outlook automation, and moderation/appeal flow processing systems.

I suggest we discuss the details in private messages: the scope of the flow, the number of accounts, the current process, requirements for AI generation, and the level of automation needed.

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Good day. I am ready to complete this project as I have extensive experience in app development.

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Hello! I have carefully studied your project and am ready to start its implementation. Let's discuss the details for the best execution.

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Hello! Ready to collaborate. I have experience working with Lumen Database complaints and Google Counter. I offer a loyal price and quality work. Write to me)

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Welcome! We are a team of developers with 4 years of experience in process automation, parsing, and database optimization. Our main stack includes PHP, JavaScript, and TypeScript, allowing us to create reliable and flexible solutions for processing large amounts of information. We will set up an automatic complaint processing flow on Lumen, optimize database queries, and ensure a high level of cybersecurity to protect user data. The work will be done turnkey with strict adherence to deadlines. Let's discuss the logic of statuses and processing in the chat.

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Welcome! Our team has been specializing in complex backend development and business process automation for 4 years. We have a strong knowledge of PHP and experience in optimizing database performance, building queues, and configuring business logic. We understand how to properly build the architecture for handling complaints in Lumen so that the system operates quickly, without failures, and can handle the load. We will ensure code cleanliness, data security, and automate the entire flow according to your requirements. We are ready to discuss technical details in private messages.

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Hello! Are you already using any tool for Outlook verification, or does it need to be implemented from scratch?

I will clarify the details regarding the timeline and budget in personal correspondence.

I propose to execute this project as follows:
1. I will set up automatic Outlook verification to obtain full URLs.
2. I will implement browser automation to extract and filter your domains.
3. I will integrate an AI API for generating text for outreach and automate the submission of notifications to Google with status logging and ID preservation.

… Thank you for considering my proposal. I look forward to the opportunity to collaborate with you!

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