Client Background
DateIntel is a wellness and safety-focused startup in the online dating industry. Their target audience is people who use dating apps and want an extra layer of safety when meeting new matches.
As a small but fast-growing venture, DateIntel wanted to disrupt the space by blending AI insights with crowdsourced reviews. Their goal was to give users a privacy-conscious way to verify identities, assess potential risks, and make smarter dating decisions.
The Challenge
The team identified several problems in the dating safety space:
- No reliable way to verify dating profiles before meeting
- Limited tools to evaluate behavior beyond anecdotal reviews
- Users wanted privacy, but not invasive background checks
- No system to track and reflect on personal decisions (proceed, cancel, unsure)
To stand out, DateIntel needed an app that could combine AI technology with community input, while keeping everything simple and ethical.
Key Features Developed
Identity Lookup Engine
Matches names, photos, emails, and phone numbers with public data
AI Risk Analysis
Generates identity confidence scores and color flags
Decision Tracker
Lets users log their choices and see trends
Community Review System
Star ratings, emojis, and short reviews moderated by AI
Attribute Ratings
Feedback on punctuality, communication, consent awareness, hygiene, and emotional maturity
Mobile-First UX
Dark mode, swipe gestures, smooth onboarding
Admin Dashboard
Moderation tools and reporting system
Our Solution
- We partnered with DateIntel to design and develop a mobile-native app that focuses on privacy, trust, and safety.
- For users, we built an Identity Lookup Engine that checks public information and social profiles. This is paired with an AI Risk Analysis that generates a confidence score and color-coded flags (Green, Yellow, Red). Users can also read and contribute community reviews, rating attributes like respectfulness, consent awareness, and hygiene. A Decision Tracker lets them log outcomes from their dates and see overall patterns.
- For admins, we developed AI-assisted moderation tools and dashboards to ensure reviews remain safe, fair, and compliant.
- The result was a balanced platform that empowers users to make smarter decisions without sacrificing privacy.
Technologies Used
- Mobile App Development: React Native (Expo) / Flutter
- Backend & AI Integration Node.js (Express) + Python
- Machine Learning & Risk Scoring: OpenAI, HuggingFace, Scikit-learn
- Database: PostgreSQL (Supabase)
- Authentication: Firebase / Supabase Auth
- Media Hosting: Firebase Storage / Cloudinary
- Notifications: Firebase Messaging / OneSignal
Tangible Results
- AI analysis delivers results in under 7 seconds
- Over 80 percent accuracy in risk flagging during closed beta testing
- 2,500 beta signups in the first 3 weeks after launch
- 40 percent higher trust and retention compared to existing safety forums
- Flagged content moderation time reduced by 65 percent with AI filtering
Team Structure
- 1 Technical Project Manager
- 2 Mobile Developers
- 1 Backend Developer
- 1 AI/ML Engineer
- 1 UI/UX Designer
- 1 QA Engineer
- We followed an agile process, releasing early features to beta testers and improving the app based on feedback. This approach ensured the platform balanced accuracy, privacy, and usability.
Development Process
- Timeline: 4 Months (v1.2 release)
- PHASES:
- Month 1: Planning, design, backend setup, scraper MVP
- Month 2: AI risk engine, review system, decision tracker
- Month 3: Moderation tools, reporting, admin panel
- Month 4: QA, beta testing, app store release
— Founder, DateIntel
"DateIntel is exactly what we envisioned, a smart, user-friendly, and ethical dating safety app. The development team helped us balance powerful AI-driven features with compliance, privacy, and inclusivity. The response from early users has been phenomenal, and we now feel confident scaling this vision worldwide."
Our Solution
- We partnered with DateIntel to design and develop a mobile-native app that focuses on privacy, trust, and safety.
- For users, we built an Identity Lookup Engine that checks public information and social profiles. This is paired with an AI Risk Analysis that generates a confidence score and color-coded flags (Green, Yellow, Red). Users can also read and contribute community reviews, rating attributes like respectfulness, consent awareness, and hygiene. A Decision Tracker lets them log outcomes from their dates and see overall patterns.
- For admins, we developed AI-assisted moderation tools and dashboards to ensure reviews remain safe, fair, and compliant.
- The result was a balanced platform that empowers users to make smarter decisions without sacrificing privacy.
Technologies Used
- Mobile App Development: React Native (Expo) / Flutter
- Backend & AI Integration Node.js (Express) + Python
- Machine Learning & Risk Scoring: OpenAI, HuggingFace, Scikit-learn
- Database: PostgreSQL (Supabase)
- Authentication: Firebase / Supabase Auth
- Media Hosting: Firebase Storage / Cloudinary
- Notifications: Firebase Messaging / OneSignal
Development Process
- Timeline: 4 Months (v1.2 release)
- PHASES:
- Month 1: Planning, design, backend setup, scraper MVP
- Month 2: AI risk engine, review system, decision tracker
- Month 3: Moderation tools, reporting, admin panel
- Month 4: QA, beta testing, app store release
Team Structure
- 1 Technical Project Manager
- 2 Mobile Developers
- 1 Backend Developer
- 1 AI/ML Engineer
- 1 UI/UX Designer
- 1 QA Engineer
- We followed an agile process, releasing early features to beta testers and improving the app based on feedback. This approach ensured the platform balanced accuracy, privacy, and usability.
Tangible Result
- AI analysis delivers results in under 7 seconds
- Over 80 percent accuracy in risk flagging during closed beta testing
- 2,500 beta signups in the first 3 weeks after launch
- 40 percent higher trust and retention compared to existing safety forums
- Flagged content moderation time reduced by 65 percent with AI filtering
