Lisnen App
LANCER
Lancer :
Ideal for quick, focused engagements
Team: ≤ 2 resources
Duration: ≤ 3 weeks
Indicative Budget: ≤ $3,000 USD*
Use Cases: MVPs, prototypes, simple websites, bug-fix sprints
Indicative Pricing: ~$15–$25/hr per resource*
Growth :
Best suited for growing products or structured builds
Team: 2–5 resources
Duration: Up to 4 months
Indicative Budget: Up to $30,000 USD*
Use Cases: Feature-rich web apps, integrations, custom dashboards
Indicative Pricing: ~$15–$35/hr per resource*
Enterprise :
Tailored for high-scale, complex or long-term initiatives
Team: 5+ resources
Duration: More than 4 months
Indicative Budget: Starts from $30,000+ USD*
Use Cases: End-to-end platforms, SaaS products, compliance-heavy systems
Indicative Pricing: ~$25–$50/hr depending on roles*
(Development)
Project Description
A Canada-based assistive technology firm focused on accessible innovation partnered with Oodles to enhance its mobile app with AI-driven audio detection. The client needed a seamless way to process live microphone input and detect contextual audio patterns in real time. Oodles enabled this through deep learning model integration within a React Native environment, ensuring smooth, responsive user journeys.
Scope Of Work
The client sought Oodles to translate a pre-trained audio analytics model into a mobile-ready format while enabling real-time detection. The project covered model conversion, mobile app integration, audio streaming, real-time inference, and user navigation based on model output—all within a React Native framework tailored for accessibility and performance.
Our Solution
To align with the client's goal of real-time accessibility support, Oodles implemented a robust machine learning pipeline within the mobile ecosystem. The team converted the existing Python-based audio model into a TensorFlow Lite format, enabling efficient on-device inference.
Key Features Delivered:
- TensorFlow Lite Model Conversion: Enabled mobile compatibility and reduced processing latency.
- Live Audio Capture Pipeline: Integrated continuous microphone streaming and pre-processing to match model input structure.
- Real-time Detection Engine: Analyzed live audio data and returned contextual results instantly without external API calls.
- User Flow Automation: Dynamically navigated users through the app based on detection events, enhancing interactivity.
- Result Formatting and Filtering: Added output layers to assess, log, and react to specific detection criteria in real time.
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