CLIENT DETAILS
Leading 3D Printing Brand with a Global Customer Base
The client is a U.S.-based technology company that designs and sells consumer-grade 3D laser printers. Operating in the desktop manufacturing space, the company serves a diverse global user base of educators, hobbyists, small businesses, and makers.
Known for its sleek hardware, intuitive software, and strong community focus, the client offers end-to-end customer service through digital support channels, including email, web forms, and help center articles. As adoption grew rapidly, the support team began handling thousands of inbound queries each month, covering topics like product setup, warranty coverage, order tracking, and returns.
To maintain its high standard of service without increasing team size, the client sought a solution that could deliver instant, reliable, and brand-consistent responses without compromising accuracy or oversight.
BUSINESS NEED
Reduce Support Load by Automating Common Email and Webform Responses
The client's support team was spending too much time replying to repetitive queries — ranging from shipping delays to basic troubleshooting. Manual triage, inconsistent responses, and agent overload were affecting turnaround times and customer satisfaction.
The company reached out to Flatworld.ai to set up an automated system that could:
- Automate replies for all tier-1 text-based queries
- Maintain ≥90% accuracy and ≥85% customer satisfaction (CSAT)
- Enable automated actions (e.g., return initiation, order lookup)
- Improve efficiency by reducing agent review time
- Maintain full visibility into automation performance
Inputs Provided by the Client
- Categorized examples of common support queries
- Macro templates and tone-of-voice guidelines
- Internal knowledge base and business rules
- Archived Zendesk tickets and resolution data
- Public website content and help center documentation
Deliverables Requested
- Automated email and webform response handling
- Trigger-based workflow automation (e.g., refunds, order checks)
- Categorization logic updates and accuracy tracking
- Dashboards for performance monitoring
- Revamped Zendesk categorization with reporting layers
Timeline: The requirement gathering and proof-of-concept phase took two months, followed by three months of weekly releases to cover all scenarios within Telenok's scope.
PROJECT EXECUTION
Email Workflow Automation in Six Stages
The Telenok email automation solution was delivered through a well-orchestrated, collaborative process. With detailed planning, continuous feedback, and iterative development, the Flatworld.ai team provided consistent results across every phase of the project lifecycle.
1Scoping and Planning
Flatworld.ai began with a focused scoping phase that prioritized use cases based on their impact and mapped them to weekly release timelines. Input data was validated early to avoid rework, while historical ticket analysis and FAQ reviews helped identify edge cases. The scope evolved through weekly reviews, and communication was managed through daily updates, meeting notes, and a centralized tracker that flagged dependencies and blockers.
2Implementation and Development
The development phase focused on building logic modules in a modular, rule-based format, iteratively calibrated to the client's latest requirements. Documentation was created for each feature — process maps and detailed user stories — providing all team members with clear responsibilities. New methods were introduced as needed to optimize performance and handle complex queries efficiently.
3Operational Alignment and Execution
Coordinating across time zones made collaboration and approvals more complex. Transitioning from manual to automated responses required careful oversight. Despite limited time and resources, robust QA cycles were maintained. Clear documentation, frequent team syncs, and focused backlog management helped simplify execution and maintain delivery speed.
4Testing and QA
Testing played a central role throughout rapid development cycles. Regression testing was performed daily to ensure stable performance. Flatworld shared test cases with the client via Jira cards for complete transparency and faster approvals. Separate QA and UAT environments were maintained to ensure clean test runs and consistency across releases.
5Go-live and Post-deployment Support
Go-lives were preceded by final test runs during client working hours. Real-time support was provided during live operations with daily shift check-ins. As the system proved stable, the client began independently managing the platform. A custom dashboard offered complete performance visibility, supported by regular reporting on trends and system accuracy.
6Integration with Third-party Systems
Integrations with platforms like Zendesk, Chargebee, and Shopify required multiple rounds of access approvals. Due to restricted API privileges, Flatworld.ai engineered a backend-mimicking system to deliver the required functionality without admin access. Detailed logs and reports were shared for full transparency and auditability.
Measured Impact
While the system was deployed on time, the team encountered a few key hurdles.
- Shifting scope and frequent change requests: Flatworld.ai realigned goals and adjusted timelines to keep delivery on track despite evolving priorities and data-related delays.
- Rapid logic updates in a live build: Automation logic was remapped repeatedly, with tight testing cycles ensuring every release passed UAT on the first attempt.
- Addressing skill gaps and training needs: Focused training sessions and ticket audits aligned internal teams on accuracy measurement and scoring standards.
- Handling media-based tickets: A workaround was designed to allow Telenok to interpret and respond effectively to image and video-based queries.
- Navigating time zones and handoffs: Structured documentation, efficient backlog management, and frequent syncs kept cross-timezone collaboration smooth.
- Moving from manual to automated: Rigorous QA cycles ensured a smooth transition while preserving accuracy and consistency from day one.
- Integrating without full API access: A backend-mirroring setup preserved system logic and fidelity without requiring direct API access.
- Maintaining transparency with logs: Custom dashboards and logging tools made every Telenok action visible and reviewable.
- Cleaning up historical ticket data: Multiple review cycles extracted high-quality training examples from inconsistent legacy data.
- Filtering the knowledge base: Only the most current, relevant articles were selected — prioritizing clarity over volume for accurate responses.
- Automation coverage across 100% of tier-1 support categories
- 90%+ response accuracy verified through live tickets and CSAT surveys
- 85%+ customer satisfaction with faster resolution times
- Significant reduction in agent workload — most replies auto-drafted or sent without edits
- Dashboards showing clear sourcing for every automated reply, improving auditability
- Time saved by automating actions like refund initiation, order tracking, and status lookups
- Telenok: Workflow engine for response generation
- Zendesk: Ticket routing and historical training data
- Chargebee + Shopify: Action execution (e.g., returns, refunds)
- Jira: Test tracking and release management
- Draw.io: Process maps, logic flows, and scenario visualizations
Takeaway
Consistent Responses Without the Bottlenecks
Flatworld.ai helped the client remove friction from a high-volume part of their support process — without losing human judgment where it was needed. Agents now spend less time rewriting the same answers and more time solving problems that require attention.
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