Project summary

The project addresses the digital divide by focusing on offline-first technology, ensuring the AI runs smoothly on low-end smartphones (2GB-3GB RAM) without an internet connection. By capturing the unique psychological archetypes of local youth, we aim to provide a private, non-judgmental mentor that helps teens navigate insecurities and grow into their best selves.

We are developing a "Digital Best Friend": a personalized, empathetic AI companion designed specifically for Indonesian teenagers. Unlike general AI, our solution is powered by a Small Language Model (SLM) fine-tuned on a massive, hyper-local dataset of 2,000+ teen surveys and 120 parent interviews conducted face-to-face in a single Pilot City.

What are this project's goals? How will you achieve them?

• Deep Psychological Alignment: We aim to prove that AI can provide high-quality emotional support when trained on specific local behavioral patterns rather than generic global data. We will achieve this through intensive face-to-face fieldwork in our Pilot City to capture authentic dialects, social pressures, and parent-child dynamics.

• Digital Equity and Accessibility: We will achieve accessibility by using Quantized Small Language Models (SLMs) designed to run locally on-device, catering to teens in low-connectivity areas using budget smartphones.

• Privacy-First Mental Health Support: By processing all data 100% offline, we eliminate the risk of sensitive personal data ever leaving the user’s device, creating a truly safe space for self-expression.
  

How will this funding be used?

• 36% ($6,200) - Intensive Field Research

• 29% ($5,100) - Technical Infrastructure

• 22% ($3,700) - Stipend

• 10% ($1,500) - Operations & Administration

• 5% ($800) - Professional Fees

Our Minimum Funding will focus on a "Technical Lean" approach, scaling the research to under 1,000 teenagers to prove the core feasibility of an offline, privacy-first SLM on low-end hardware.

Full Funding enables a "High-Fidelity" launch, expanding the dataset to 2,000+ teenagers and 120 parents to capture a complete spectrum of psychological archetypes.

Who is on your team? What's your track record on similar projects?

This is our first independent initiative as a collective, we operate under a Strategic Alliance Model. We have secured advisory commitments from:

• Professional Psychologists with years of experience in adolescent clinical behavior.

• Senior IT Professionals specializing in mobile architecture and machine learning.

• Our core team brings a lean, execution-focused mindset, prioritizing data density over geographical spread by focusing on a single Pilot City to ensure the PoC’s success.
  

What are the most likely causes and outcomes if this project fails?

Likely Causes of Failure:

• Hardware Bottlenecks: Despite optimization, the model might still experience latency on extremely outdated chipsets.

• Engagement Gap: The "personality" of the AI might not resonate with the teens if the cultural nuances are lost during the model training process.

Expected Outcomes if the Project Fails:

• Valuable Data Asset: We will still produce one of the most comprehensive datasets of Indonesian teen psychology, which can be open sourced for academic and public policy use.

• Technical Benchmarking: We will provide a "failed-fast" case study on the limits of running SLMs offline on low-end hardware, which is valuable for the global AI-for-good community.
  

How much money have you raised in the last 12 months, and from where?

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