Fragmented conversations
Feedback spread across email, chat and the help desk.
(07)Case study · Concept build
B2B SaaS
AI Customer Support Intelligence
A hundred support tickets can be one product bug in disguise. We designed a system that sees the pattern.
Concept build
- Industry
- B2B SaaS
- Services
- AI Development, NLP, Semantic Search, Support Analytics, Workflow Automation
(01)Context
Context
Niasus is a hypothetical enterprise SaaS company with a fast-growing support team handling tickets across email, live chat and a help desk. It had plenty of customer feedback, but no way to see the big picture in it.
(02)The challenge
The challenge
Managers could read individual tickets but not the patterns across thousands of them. Customers described the same problem in different words, so keyword search missed connections. A single defect affecting hundreds of customers looked like hundreds of unrelated requests. Niasus didn't need another ticketing tool; it needed an intelligence layer on top of the one it had.
Repeat requests
The same issues, described differently every time.
Slow root-cause hunting
Managers reviewed tickets by hand to connect the dots.
Knowledge trapped in tickets
Workarounds lived in old conversations nobody could find.
(03)At a glance
Illustrative pilot targets
- 12 → 5 min
- Time to find related tickets
- 90 → 25 min
- Recurring-issue summary
- 100% → 35%
- Manual categorization
- 8 → 2 hours
- Time to spot an emerging issue
Illustrative pilot targets for a proposed implementation, not measured results.
(04)Our approach
Our approach
- 01
Add intelligence, don't replace tools
Integrate with the existing help desk and keep current workflows.
- 02
Numbers from data, words from AI
Ticket counts and trends come from the database; the AI handles meaning and summaries.
- 03
Evidence on every insight
Each finding links back to the real tickets behind it, and patterns are presented as leads to investigate, not verdicts.
(05)What we built
What we built
01
AI ticket classification
Category, product area, sentiment, urgency and escalation risk tagged on arrival, with low-confidence cases sent for review.
02
Search by meaning
“Customers who lost access after enabling SSO” finds related tickets even when the wording differs.
03
Related-ticket clusters
“Dashboard won't load”, “analytics page is blank” and “charts disappeared” are grouped as one potential issue.
04
Root-cause summaries
The recurring problem, sample reports, timing, affected features and suggested next steps, all with sources.
05
Emerging-issue alerts
An early warning when complaints about one area suddenly spike.
06
Ask your support data
Managers ask “Why are customers contacting us about integrations?” and get an evidence-backed answer.
(06)In practice
When a release goes wrong
Complaints about data sync rise after a release. The system spots the increase, groups related tickets, compares them with historical patterns, notes the timing and alerts engineering. It flags the release as a correlation to check, not a confirmed cause.
(07)Showcase
Showcase
(08)Under the hood
Tech stack and architecture
Six layers, from the help desk to the dashboard.Show the architectureHide
- 01Help-desk integrationsWith personal-data redaction
- 02Cleaning pipelineFor noisy email threads
- 03Structured database plus vector index
- 04AI orchestrationRoutes questions to analytics or semantic search
- 05Clustering and trend detection
- 06Intelligence dashboard
Search quality is measured with
- Precision
- Recall
- Time-to-relevant-ticket
- Citation accuracy
(09)Designed to achieve
What it's designed to achieve
Faster discovery of past cases, less manual issue analysis, more consistent categorization and earlier warning of problems, plus product, engineering and customer-success teams working from the same customer evidence.
Niasus
From ticket management to support intelligence.
Niasus is a hypothetical client. The architecture, workflows and metrics describe a proposed reference implementation, not a verified client deployment.
(→)Next case study
Client · Bimaline
One connected hospital
A complete hospital information system of 32 connected subsystems, in use across multiple hospitals.
(11)Contact
Let's buildyours next.
Tell us about your product, timeline and constraints. You’ll hear back from a senior engineer within one business day — with questions, not a sales deck.
contact@overc.io