AI Support Insights Assistant for Freshdesk Ticket Data
Bussines Type
Key Results
The project delivered a 25–40 percent uplift in operational efficiency by improving FAQ accuracy by over 35 percent, increasing canned message quality by 40 percent, reducing handling times by more than 20 percent, boosting proactive support by 50 percent, strengthening SLA compliance by up to 20 percent and raising customer satisfaction by 10–15 percent.
Executive Project Summary
Genistar created an AI-driven Support Analysis Assistant that processed years of Freshdesk ticket data to uncover patterns, identify bottlenecks and improve customer support quality. The tool provided metrics, insights and recommendations that helped the team develop better FAQs, clearer canned responses and a deeper understanding of recurring customer issues. The project delivered immediate value and empowered the support team with data they previously struggled to access.
The Problem
Genistar’s support team had accumulated a large volume of Freshdesk tickets over many years. However:
- The team lacked a clear picture of long-term trends
- Outliers—such as extremely long resolution times—were hard to identify
- Recurring issues across customers were not easy to group
- Canned responses and FAQs were based on intuition rather than data
- Legacy tickets contained inconsistent formatting
- The team had no simple way to analyse or segment past support behaviours
Support leadership needed a unified system that could process all historical ticket data and turn it into actionable insights.
The Solution
Once the team realised how much insight was buried inside their Freshdesk history, they aligned around building an AI-powered analysis tool. But the internal support engineers were already focused on day-to-day operations, so they needed an external partner who could move quickly and handle the complexity of the data.
“This was when it became clear we weren’t lacking data. We were lacking visibility. All the answers were already there—we just had no way to surface them.”
To jumpstart the process, the support team provided a detailed export of Freshdesk tickets along with business rules about how the data should be interpreted. What impressed them early on was how accurately the development partner mapped this data to real-world customer journeys.
“They understood not just the numbers but what those numbers meant for our team. They immediately saw which metrics mattered, which formats were problematic and how this data could improve our FAQ and canned responses.”
Development began with a Python-based anonymisation layer to ensure customer privacy while maintaining analytical integrity. The initial prototype quickly highlighted key metrics including:
- An average support resolution time of 25 hours and 9 minutes
- A major 165-hour outlier that revealed a workflow breakdown
- Recurring themes across ticket categories
- High-frequency issues suitable for new canned responses and FAQs
The UI mirrored the Wiki Assistant interface for consistency, and included enhancements such as:
- Clear data-source indicators
- Mode switching between ticket insights and knowledge search
- Streaming response delivery
- Improved charts and summaries for non-technical users
Communication remained smooth throughout. Weekly updates included recordings, explanations and recommendations for refining the data pipeline.
“You could tell the team really understood our support workflows. Every suggestion—from grouping ticket themes to refining metrics—felt practical and aligned with what our agents deal with every day.”
By the end of development, the Support Analysis Assistant provided the team with visibility they had never previously achieved.
The Results
1. Better FAQs and Knowledge Articles
By identifying the most common recurring customer themes, the team created clearer, more relevant FAQs.
Result:
- FAQ accuracy and relevance increased by 35–45 percent
- Self-service success improved by 25 percent, reducing inbound queries
2. Stronger Canned Messages
AI-guided analysis produced improved canned responses based on real historical ticket patterns.
Result:
- Canned message usage increased by 40 percent
- Error-prone or inconsistent replies dropped by 30–35 percent
3. Faster and More Consistent Support
Agents no longer relied solely on memory. They responded using data-backed insights.
Result:
- Average handling time reduced by 18–22 percent
- Response consistency improved by 30 percent, regardless of agent seniority
4. Improved Customer Understanding
Ticket clustering and theme detection uncovered underlying issues customers repeatedly faced.
Result:
- Proactive support actions increased by 50 percent
- Repeat queries from customers reduced by 20–25 percent
5. Visibility into Operational Bottlenecks
Outlier detection surfaced process gaps and unusually long resolution times.
Result:
- SLA compliance improved by 15–20 percent
- Cases exceeding standard resolution time dropped by 30 percent
6. Higher Quality Support Interactions
Clearer, more accurate explanations provided agents with the confidence to communicate effectively.
Result:
- Field satisfaction (CSAT) increased by 10–15 percent
- Second-touch or follow-up tickets declined by 20 percent
Overall Impact
The project positioned Genistar’s support team to deliver faster, more consistent and more customer-centric service across the entire support lifecycle.
On average, this projects deliver a 25–40 percent uplift in operational efficiency.
"Incredibly gifted and insightful! So glad that we met Dan and his team in 2024. What amazes us most is their depth of knowledge across so many areas. They quickly understood our business, broke down challenges, and helped us find and implement the right solutions."
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