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Skype Bot on Python
LANCERLancer :
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*
Technologies Involved:
PYTHON
Area Of Work:
Web Development
Project Description
The project was about developing a chatbot capable of automating real-time interactions on Skype. The goal was to improve customer engagement, reduce manual communication load, and provide consistent support experiences. The solution needed to be scalable, secure, and easy to integrate with their internal systems.
Scope Of Work
The client needed a Python-based chatbot that could seamlessly integrate with Skype, support various conversation flows, and fetch data from third-party APIs when required. The bot was expected to handle predefined FAQs, escalate queries to human agents, and maintain conversation logs for analytics.
Our Solution
Before implementation, our team conducted a detailed feasibility study and designed a modular bot architecture.
- Developed a Python-based chatbot using Skype Bot Framework and Microsoft Bot SDK
- Enabled dynamic conversation flows with NLP using Azure LUIS for intent recognition
- Integrated REST APIs to fetch and display real-time data based on user queries
- Built a secure logging system to capture and archive conversation histories
- Implemented fallback handling and escalation logic for complex or failed queries
- Deployed the chatbot in Docker containers to ensure consistent performance across environments
- Conducted User Acceptance Testing (UAT) and optimized the bot’s response times
- Delivered comprehensive documentation along with a testing suite for ongoing maintenance