Net Coach
An AI-powered assistant simplifying effective networking on LinkedIn.
Problem Statement
"How might we equip LinkedIn users with an AI-driven networking assistant that reduces the friction of finding and contacting relevant people, offers guidance on outreach and follow-ups, and boosts user confidence, so they can consistently build meaningful professional connections?"
Problem Background
“Many LinkedIn users, especially job seekers and career changers, struggle to identify and approach the right professional contacts. They often feel anxious about reaching out, unsure of how to start or follow up, and discouraged by low response rates. This results in missed opportunities, inconsistent outreach, and lower confidence, ultimately slowing career growth and making LinkedIn feel intimidating rather than empowering.”
Research Insights
User Pain Points
- Difficulty identifying and filtering relevant connections.
- Emotional hesitation and fear of judgment when initiating conversations.
- Low confidence in crafting outreach messages.
- Frustration from the experience of being ignored after sending messages.
Supporting Data
- Interviews with 6 participants showed that 83% experienced significant difficulty identifying relevant contacts.
- 57% of survey respondents reported low confidence in their networking outreach.
- Only 29% had previously tried any AI tools for networking.
- 100% expressed openness and interest in AI-driven tools to streamline LinkedIn networking.
- 86% wanted more advanced targeting and filtering capabilities.
Feedback:
"Participants consistently emphasized their interest in personalized AI guidance to improve their networking experience, highlighting the need for solutions that address both practical and emotional barriers to effective networking."
Landing on the Solution
Based on identified user needs, NetCoach was designed to provide a personalized roadmap for LinkedIn networking, including:
- Clear, actionable steps for initiating and maintaining contacts.
- Context-specific recommendations and message templates.
- Adaptive follow-up assistance, ensuring timely and appropriate interactions.
User Flows/Mockups
Future Steps
- Expand user testing internationally and add multilingual support.
- Continuously enhance AI-driven recommendations using additional engagement data.
- Implement deeper feedback integration to refine user networking strategies.
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Learnings
Product Manager Learnings:
Abbas Soltani
Through Co.Lab, I learned to design MVPs quickly, prioritize learning over perfection, and turn user interviews into clear product decisions. Sharing my journey on LinkedIn also taught me the power of vulnerability and authentic storytelling.
This experience reinforced how technology empowers anyone with curiosity and determination to turn ideas into real impact, making meaningful creation more accessible than ever.
Designer Learnings:
Designer Learnings:
Jo Sturdivant
- Adapting to an Established Team: Joining the team in week 6 of 8 was challenging, as I had to quickly adapt to existing workflows, dynamics, and goals. This mirrors real-world situations where you often integrate into teams mid-project, and flexibility is essential.
- Work-Blocking for Efficiency: With only two weeks to complete the project, I learned the importance of a structured work-blocking system. This approach allowed me to manage my time effectively and meet deadlines under pressure.
- Making Data-Driven Design Decisions: Unlike my past projects, I had to rely on research conducted by others. This was a valuable experience in using pre-existing data to guide design decisions, helping me focus on the core insights without starting from scratch.
Developer Learnings:
Developer Learnings:
Vanady Beard
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As the back-end developer, I learned how important it is to create efficient and reliable systems that support the entire application. This experience also taught me the importance of optimising the database and ensuring the backend is scalable and easy to maintain.
Developer Learnings:
Stephen Asiedu
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As a back-end developer, I've come to understand the importance of being familiar with various database systems and modules. This knowledge enables me to build diverse applications and maintain versatility in my work. I've also learned that the responsibility for making the right choices rests on my shoulders, guided by my best judgement.
Developer Learnings:
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Developer Learnings:
Maurquise Williams
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- Process of Creating an MVP: Developing a Minimum Viable Product (MVP) taught me how to focus on delivering core functionalities balancing between essential features and avoiding scope creep.
- Collaboration in a Real-World Tech Setting: This experience taught me how to collaborate efficiently in a fast-paced tech environment, keeping the team aligned and productive, even while working remotely across time zones.
- Sharpening Critical Thinking and Problem-Solving Skills: This experience honed my ability to think critically and solve problems efficiently. By tackling challenges and finding quick solutions, I sharpened my decision-making and troubleshooting skills in a dynamic, real-world setting.
Developer Learnings:
Jeremiah Williams
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All in all this experience was very awesome I learned that in coding with others being transparent is key
Developers Learnings:
Justin Farley
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I learned how important communication is when working with a team. Communication provides understanding, advice, ideas, and much more. While working with the product team, I’ve found that communication keeps everything flowing smoothly. Working with a team also showed me that every member brings something different to the table and we all have to work together in order to align and meet our end goal.