AI Marketing · AI outbound
LinkedIn AI Comment Generation
We developed a market-ready MVP to validate a subscription model for automated professional engagement on LinkedIn.
- Flagship build
- Bootstrapped
- 10 weeks
- France
- 2026
Outcomes
- Subscription MVP live at launch
- Business model
- Per-user professional voice profiles
- Voice matching
- 10
- Weeks to market-ready MVP
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 queues comments 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.
Technologies used
Front-end
- JavaScript
- ReactJS
- HTML5
- CSS3
Back-end
- PHP
- Laravel
DevOps
- Linux
- Github CI
Database
- MySQL
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.
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