Mobile-First Investing Experience
React Native flows made onboarding, portfolio review, watchlists, and alerts fast enough for everyday retail investors.
SaaS / Fintech
Coneon
Coneon helps retail investors discover, compare, and act on portfolio opportunities through a mobile-first investing experience with AI recommendations and social trading workflows.
Year
2022
Team Size
8 engineers
Timeline
6 month MVP, 12 months to scale
Technology
React Native, Node.js, Python ML
The Challenge
Coneon entered a crowded investing market where retail users were frustrated by high onboarding friction, slow portfolio insights, and dated interfaces that treated first-time investors and active traders the same. The founding team had validated demand, but the MVP could not support real-time data, personalization, social features, or regulatory auditability at the pace investors expected.
The business challenge was trust. Users needed a platform that felt fast and approachable while still handling sensitive financial data, consent, transaction records, portfolio performance, and risk signals with enterprise-grade controls. The product also needed to support a Series A fundraising narrative with measurable traction, not just a polished prototype.
DigiteraX shaped Coneon as a mobile-first fintech platform: secure onboarding, portfolio intelligence, AI-powered recommendations, real-time market data, social investing workflows, watchlists, push notifications, and analytics that connected product engagement to assets under management.
Client Name
Coneon
Industry
Fintech / WealthTech
Engagement Type
Mobile-First SaaS Platform Build
Timeline
6 month MVP, 12 month scale-up
Team Size
8 engineers, 1 product manager
Budget Range
$300K-$400K
Status
Series A funded
Live Users
100K+ active users
100K+
Active Users
$50M+
Assets Under Management
4.8/5
App Store Rating
How We Solved It
The Solution
We designed Coneon as a secure mobile investing product with a modular backend for user profiles, portfolio aggregation, recommendations, notifications, social graph activity, compliance events, and analytics. The MVP prioritized onboarding, portfolio views, watchlists, and basic investing insights so the team could validate activation before scaling advanced AI features.
After launch, we added a Python ML service for portfolio recommendations, a social investing layer for watchlists and copy-style inspiration, and event analytics so product leaders could measure activation, retention, referral loops, and AUM growth. Delivery moved in two tracks: mobile product velocity and regulated backend hardening.
AI Recommendation Engine
Python ML services generated portfolio suggestions based on risk profile, holdings, goals, and market movement.
Social Investing Layer
Community watchlists, investor profiles, and social signals helped users learn from credible peers without turning advice into noise.
Months 1-6
MVP Launch
Complete
Mobile onboarding, portfolio view, watchlists, alerts, core APIs
First 10K users onboarded
Months 7-10
AI & Social Layer
Complete
Recommendation engine, social profiles, copy-style discovery, event analytics
Recommendation engagement above 35%
Months 11-12
Production Scale
Complete
Security hardening, observability, growth dashboards, investor reporting
Series A readiness and 4.8 app rating
Technology
Architecture & Tech Stack
Frontend
React Native
API Gateway
Secure APIs
Backend
Node.js
Data Layer
PostgreSQL
Integrations
Secure APIs
Frontend
- React Native
- TypeScript
- Redux Toolkit
- Mobile design system
Backend
- Node.js
- Python ML
- GraphQL API
- REST APIs
Data & Storage
- PostgreSQL
- MongoDB
- Redis
- S3
Infrastructure
- AWS
- Kubernetes
- Docker
- Terraform
Tools & Services
- GitHub Actions
- Datadog
- Segment
- Plaid-style account aggregation
Features
What We Built
AI Portfolio Recommendations
Personalized suggestions based on risk profile, holdings, market movement, and investment goals.
Real-Time Market Data
Streaming quote, watchlist, and portfolio changes with alerting for meaningful movement.
Social Trading Signals
Investor profiles, watchlists, and community signals help users discover strategies from credible peers.
Secure Onboarding
Identity, consent, risk profiling, and account setup flows designed for regulated fintech usage.
Portfolio Analytics
Performance, allocation, risk, and goal progress views built for quick mobile comprehension.
Push Notification Engine
Personalized alerts for market movement, portfolio health, and recommended actions.
Results
The Impact
Coneon moved from a fragile MVP to a production-grade investing platform in 12 months. The product reached 100K+ active users, crossed $50M in assets under management, and maintained a 4.8/5 app rating after the AI recommendation and social investing releases.
The strongest business outcome was funding readiness. The platform gave the founding team clear evidence of activation, retention, assets under management, and recommendation engagement, supporting a $5M Series A raise.
100K+
Active Users
12 months post-launch
$50M+
Assets Under Management
retail portfolios tracked
4.8/5
App Store Rating
after scale release
$5M
Series A Raised
funding milestone
Return on Investment
- Series A funding achieved: $5M after traction and platform maturity improved.
- Recommendation engagement created a measurable upsell path for premium portfolio insights.
- Event analytics reduced product guesswork and focused roadmap decisions on retention drivers.
- Cloud-native architecture avoided a full rewrite as the user base crossed 100K active accounts.
DigiteraX helped us turn a fintech concept into a platform investors trusted. The product, analytics, and architecture gave us the confidence to scale and raise our Series A.
Founder & CEO, Coneon
The Team
People Behind the Project
SC
Sarah Chen
Solutions Architect
Fintech architecture, regulated workflows
AG
Amit Gupta
Backend Lead
Node.js, GraphQL, secure APIs
PN
Priya Nair
Mobile Lead
React Native, design systems, activation flows
AH
Ahmed Hassan
DevOps Engineer
AWS, Kubernetes, observability
Challenges & Lessons
Overcoming Obstacles
Mobile Performance
Portfolio views, recommendations, and market alerts had to feel instant on mid-range phones.
Optimized API payloads, caching, and screen rendering around the most-used investor flows.
Fintech trust starts with speed and consistency.
Recommendation Explainability
Users needed to understand why the app suggested an action before trusting AI output.
Added reason codes, risk language, and portfolio context to every recommendation.
AI features need explanation, not just prediction.
Regulated Data Handling
Financial data, consent, and user actions required auditability from the beginning.
Built immutable event logs, RBAC, encryption, and monitoring into the core architecture.
Compliance architecture must arrive before growth.
Evolution
From Launch to Continuous Improvement
Month 6
MVP
Complete
Mobile onboarding and portfolio basics
10K initial users
Month 12
Scale
Complete
AI recommendations and social signals
100K+ active users
Series A
Funding
Complete
Investor dashboards and growth analytics
$5M raised
Future
Expansion
Planned
Premium insights and deeper brokerage integrations
Higher AUM per user
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