Midistax
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
Midistax, a rising startup in the creator economy space, aimed to build an interactive media platform combining YouTube video streaming and Spotify-powered playlists. They sought a scalable Django-based web and app solution with intelligent search, user-driven content curation, and conversational engagement. The project involved integrating third-party APIs, real-time user flows, and Rasa-powered chatbot features.
Scope Of Work
The client needed a unified platform with the help of Oodles for community engagement and personalized media discovery. The goal was to enable dynamic content curation using YouTube and Spotify APIs, supported by automated conversations and multilingual input handling. Key areas included chatbot setup, media integration, custom workflows, and backend automation for content and user management.
Our Solution
To build a flexible and smart community platform for Midstix, the development focused on combining Django with intelligent NLP via Rasa.
Key Features Implemented:
- Rasa Chatbot Configuration: Rasa was initialized using CLI tools and set up for shell-based testing and action execution.
- Custom NLP Pipeline: The pipeline was tailored with Spacy, Regex, Lookup, and Synonym-based entities.
- Conversational Workflows: Designed dynamic conversation flows using Rasa Forms, Conditional Rules, and Checkpoints.
- Automated Testing: Deployed NLU-only tests and story-based test automation.
- Multilingual Support: Enabled the chatbot to understand and respond to multiple languages.
- Database and Indexing: Used MySQL for storing user data and Elasticsearch for fast search, recommendation, and analytics.
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