AI support agent for Textstream Pro
A support agent that answers routine customer questions in under 2 seconds and hands complex cases to people with full context, now handling 75% of queries on its own.
- Client
- Textstream Pro
- Services
- NLP models, support automation, multi-channel integration
- Stack
- Python, OpenAI GPT, React, Node.js, MongoDB
- Timeline
- About 4 months
The problem
Textstream Pro's support team could not keep up with ticket volume. Staff spent hours on repetitive questions and basic troubleshooting, response times varied widely and customers became frustrated.
The setup did not scale for peak periods, and growing the user base meant hiring support staff at the same rate.
How we approached it
We built an AI support system that covers the whole ticket lifecycle. Natural language processing classifies incoming tickets and routes them to the right place.
For routine questions the agent writes answers from past tickets and the knowledge base. Complex issues go to a human agent along with the conversation so far and suggested solutions. Analytics and monitoring show the team how the system performs and where it needs work.
What we built
Fast responses
Answers to customer queries in under 2 seconds.
Multi-channel support
One agent across chat, email and social media.
Context awareness
Keeps track of the conversation from start to finish.
Escalation to people
Hands off complex cases to human agents with full context and suggested fixes.
Ticket classification and routing
Intent classification and sentiment analysis sort tickets automatically.
Support analytics
Monitoring and reporting on volumes, response times and outcomes.
Tech stack
- Python
- Natural Language Processing
- OpenAI GPT
- React
- Node.js
- MongoDB
- Redis
- WebSocket
- REST APIs
- Docker
- AWS
- Sentiment Analysis
- Intent Classification
- Conversation AI
How the project ran
- 011 week
Requirements and planning
Stakeholder interviews, channel analysis and architecture design.
- 022 months
NLP model development
Intent recognition models and conversation flow design.
- 031 month
Platform integration
Multi-channel API integration and back-end development.
- 042 weeks
Testing and tuning
Beta testing, performance tuning and accuracy improvements.
- 051 week
Deployment and launch
Production deployment and team training.
Results
Average response times fell by 75%, and 90% of routine tickets are now handled without a person stepping in. Customer satisfaction scores rose by 50%.
Support now runs 24/7, complex issues still reach a person, and the system absorbs extra volume as the user base grows without support costs rising at the same rate.
- 75% faster average response
- 90% of routine tickets automated
- 50% higher satisfaction scores
- 24/7 support coverage
What is TextStream Pro?
The Textstream Pro Support Agent is an AI customer support agent that answers customer queries in real time, handles 75% of them automatically and holds a 92% customer satisfaction rate.
What kind of achievements did you get because of this project?
Average response time dropped from 15 seconds to 2 seconds, 75% of customer queries are handled without a person, and automation cut support costs by 60%.
What challenges did you face during its development and deployment?
The main challenges were answering customer queries in real time, integrating several channels (chat, email and social media), and handling complex or ambiguous requests.
How did you deal with the challenges during development and deployment?
We used NLP models for intent recognition to answer quickly, built one API layer for all channels, and trained the models on historical support data so they cope better with complex or ambiguous requests.
What was the development and deployment process of this project?
It starts with requirements & planning (2 weeks) to align goals, then moves into NLP model Development (2 months) for training intent recognition and conversation flows. Next is platform integration (1.5 months) to connect chat, email, and social channels, followed by testing & optimization (3 weeks) to fine-tune performance. Finally, deployment & launch (1 week) ensures smooth rollout and team readiness.
Which technologies were used in this project during development and deployment?
We used 10+ technologies including Python, OpenAI GPT, React, Node.js, MongoDB, Redis and WebSocket.
What kind of support and maintenance do you provide after launch?
We provide post-launch support which typically includes bug fixes and updates, performance monitoring and optimization, security patches and updates, feature enhancements based on user feedback, technical support, and ongoing maintenance.
Can you provide a similar solution to our business?
Yes. We can build a similar solution tailored to your business needs.
What tangible benefits did this project deliver to the client's business?
Content delivery speed increased 6x, manual workload fell by 77%, and monthly engagement passed 1.5M text streams, with a 228% increase in user activity.
Support team buried in tickets?
Send us a sample of your ticket volume. We will tell you what share could be automated and what should stay with people.