ConversAI: Your LinkedIn AI Assistant
ConversAI is an AI-powered networking assistant for LinkedIn that helps students and early-career professionals build meaningful connections. It crafts personalized connection requests, suggests conversation starters, and provides follow-up reminders to keep interactions engaging and relevant.
Problem Space
Problem Discovery
Networking plays a vital role in career success, yet many students and early-career professionals struggle with initiating and maintaining meaningful connections on LinkedIn. While LinkedIn provides powerful discovery tools, it lacks features that help users confidently craft messages, follow up, and sustain professional relationships. This gap discourages users from fully leveraging LinkedIn’s potential.
Problem statement
How might we enhance user engagement on LinkedIn by helping students and early-career professionals overcome networking challenges, craft impactful messages, and sustain authentic professional relationships?
Research Insights
Through user interviews and market research, we identified several key pain points:
- Difficulty in crafting personalized and impactful connection requests.
- Anxiety about initiating conversations and keeping them going.
- Time-consuming process of maintaining networking relationships.
Landing on the Solution
ConversAI: Revolutionizing Networking on LinkedIn
ConversAI is an AI-powered networking assistant designed to help students and early-career professionals build meaningful connections on LinkedIn. By integrating seamlessly into LinkedIn’s ecosystem, ConversAI personalizes outreach, suggests conversation starters, and provides follow-up reminders—removing the friction and anxiety from professional networking.
What ConversAI Does:
- Crafts personalized connection requests based on user profiles, shared interests, and mutual connections.
- Generates AI-driven conversation starters to help users engage meaningfully with new contacts.
- Sends intelligent follow-up reminders to ensure relationships don’t fade over time.
How ConversAI Revolutionizes Networking:
ConversAI empowers users to network with confidence by reducing the effort needed to create thoughtful interactions. It helps eliminate the fear of reaching out, ensuring that users not only grow their networks but also maintain valuable professional relationships.
Impact on LinkedIn Engagement:
- Higher Connection Acceptance Rates: Well-crafted, context-aware invitations make users more likely to receive positive responses.
- More Meaningful Conversations: AI-powered prompts keep discussions engaging and relevant.
- Increased User Retention: Consistent follow-ups encourage ongoing engagement, turning cold connections into warm relationships.
By enhancing networking efficiency and personalization, ConversAI transforms LinkedIn from a passive job-searching platform into an active relationship-building tool, giving users the confidence and structure they need to succeed in their careers.
Mockups & User Flow
ConversAI_DTTP – FigJam
Prototype
Future Steps
Possible Additional Problems to Address:
- Content Relevance & Quality: Users desire more tailored messaging based on their specific industry and career goals.
- AI Flexibility: Some users prefer manual customization of AI-generated responses before sending.
- Deeper LinkedIn Integration: Expanding features to include job application tracking and professional updates.
- Privacy & Data Security: Users need assurance that AI-driven interactions maintain strict privacy and security measures.
Next Steps:
- Refining AI Algorithms: Enhance AI personalization using advanced natural language processing techniques.
- Improving Follow-up Mechanisms: Develop context-aware follow-up suggestions based on past conversations.
- Expanding Features for Job Seekers: Introduce AI-assisted job application messaging and recruiter engagement tools.
- User Testing & Iteration: Continue gathering feedback to optimize user experience and refine ConversAI’s features.
Images (A snapshot of the homepage)
Learnings
Product Manager Learnings:
Angel Obi
My experience developing ConversAI has been incredibly rewarding. One of the biggest lessons was the importance of aligning product development with user needs from the outset. Conducting user research, synthesizing insights, and testing early prototypes reinforced the necessity of an iterative, feedback-driven approach in product development.
Additionally, I gained valuable experience in feature prioritization, balancing user impact and business goals while ensuring ConversAI remained simple yet effective.Overall, this project solidified my passion for AI-driven digital products and deepened my understanding of the intersection between AI, user experience, and product strategy. I’m excited to apply these learnings as I continue growing in product management.
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.