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Marketing & Adtech · AI outbound

LinkedIn AI Comment Generation

We developed a market-ready MVP to validate a subscription model for automated professional engagement on LinkedIn.

  • Bootstrapped
  • 10 weeks
LinkedIn AI Comment Generation
Focus
A.I, SAAS, MVP
Delivered
03 Feb 2026
Location
France

Outcome

1.8K comments a month in each user's own voice; rate-limited scheduler, Stripe from day one.

The Vision & Challenge

For professionals and brands, maintaining a consistent presence on platforms like LinkedIn is a non-negotiable part of building influence. The market is filled with general automation tools, but there was a clear gap for a service that could provide authentic, intelligent engagement at scale.

The core friction for our partner’s target audience was time. Manually writing thoughtful comments on dozens of posts a day is not a viable strategy for busy executives. Existing automation often produced generic, low-value replies that could damage a personal brand more than help it. The challenge was to create an AI that could understand context and emulate a specific user’s professional voice.

A B2B SaaS startup approached us with a concept to solve this problem. They needed a technical partner to build a functional MVP, capable of not only generating human-like comments but also handling the complete subscription lifecycle. We were tasked with engineering the product from the ground up to test its viability with paying customers.

The challenge

  • The AI-generated comments needed to be contextually relevant and personalized to each user's professional identity, avoiding the robotic output of typical automation tools.
  • The system had to interact with LinkedIn safely and effectively, targeting the right conversations without triggering platform restrictions or appearing as spam.
  • To validate the business model, the MVP required a fully functional, self-service subscription and payment system to manage recurring revenue from day one.

How we solved it

  • We integrated the OpenAI API with a custom onboarding process. This flow gathers user inputs on industry, goals, and tone to construct a unique, personalized prompt for each user, guiding the AI to generate aligned content.
  • A backend system was built in Laravel to manage automated engagement. It uses keyword targeting and followed-profile lists to find relevant posts, then paces 1.8K comments a month through a rate-limited scheduler that mimics natural user behavior.
  • Stripe was integrated to handle all payment processing and subscription management. We built a user-facing dashboard in React where customers could sign up for the Pro Plan and manage their billing information without manual intervention.
A working day laid out as four overlapping activities. Keyword and followed-profile watching runs continuously. Drafting happens only on posts that match the user’s targeting, using a prompt built from their onboarding answers so the comment sounds like them. Posting is deliberately spaced through the day rather than fired in bursts, because volume is what triggers platform restrictions. Nothing posts during quiet hours. The rate-limited scheduler, not the model, is what keeps the account usable.

Technologies

What we built it with

Front-end
  • JavaScript
  • ReactJS
  • HTML5
  • CSS3
Back-end
  • PHP
  • Laravel
DevOps
  • Linux
  • Github CI
Database
  • MySQL
Integrations
  • OpenAI
  • Stripe
Project screen
Product feature
Product feature

Personalized AI Voice

During onboarding, the platform collects key details about a user's professional background, industry, and communication style. This data is used to dynamically engineer a custom instruction set for the AI. As a result, every comment generated is tailored to reflect the user’s actual voice, turning generic interactions into authentic contributions.

Intelligent Post Targeting

The system moves beyond random engagement. Users define specific keywords relevant to their industry and can follow influential profiles. The automation engine then actively seeks out conversations related to these inputs, ensuring that the user’s visibility is increased within the most valuable and relevant discussions.

How we built it

Built one personalised prompt per subscriber at onboarding, then paced the output like a person would. Generic automation damages a professional brand faster than silence. So onboarding captures the subscriber's industry, goals and tone and turns that into a prompt unique to them, and every comment the OpenAI API writes is generated against it. The Laravel backend finds the right posts through keyword targeting and followed-profile lists, then releases 1.8K comments a month through a rate-limited scheduler that mimics natural behaviour, which keeps the account inside LinkedIn's limits. Stripe subscriptions and a React billing dashboard shipped in the same MVP, because a model that cannot take money from day one cannot be validated.

In the client's words

“The comments sound like the person, not a bot. Nobody has been rate-limited off LinkedIn, which was my real worry.”

NDA - Signed

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