AI Support Assistant with Retrieval Augmented Generation for Faster Resolution
A SaaS company wanted to reduce support load while keeping response quality high. We built an AI assistant using retrieval augmented generation, with guardrails, evaluation, and analytics. The assistant resolved common issues, escalated safely, and improved time to first response.
Confidential engagement. NDA available upon request.
58%
Ticket Deflection
35s
Median First Response
22%
CSAT Increase
6
Weeks to Pilot
About the Client
Industry
SaaS
Company Size
80 to 140 employees
Background
A SaaS provider with a growing customer base and a support team under pressure. Knowledge lived across docs, tickets, and internal notes with inconsistent structure.
What Needed to Change
High volume of repetitive tickets
Many tickets covered the same setup and configuration issues, consuming agent time.
Knowledge fragmentation
Docs were spread across multiple tools and were not consistently updated.
Safety and accuracy concerns
The system needed guardrails to avoid hallucinations and handle sensitive requests safely.
Production integration requirements
The assistant had to integrate with existing workflows and reporting.
The Mission
Build an AI assistant that answers common questions accurately, reduces support load, and escalates safely with measurable quality.
How We Approached It
01. Data and evaluation setup
Week 1 to 2- Content ingestion and normalization
- Chunking and retrieval strategy selection
- Evaluation set creation from historical tickets
- Guardrail rules definition
02. Implementation and integration
Week 3 to 5- RAG pipeline implementation with citations
- Escalation and handoff workflows
- Telemetry and quality metrics tracking
- Internal testing and iteration
03. Pilot launch
Week 6- Limited rollout to a subset of customers
- Ongoing evaluation and prompt tuning
- Feedback loop with support team
- Decision framework for broader rollout
Vulnerabilities Discovered
0
CRITICAL
1
HIGH
2
MEDIUM
1
LOW
Outdated documentation caused retrieval conflicts
Multiple versions of docs led to inconsistent answers without document freshness controls.
Multiple versions of docs led to inconsistent answers without document freshness controls.
Ambiguous product terms reduced precision
Synonyms and inconsistent naming reduced retrieval quality for certain topics.
Synonyms and inconsistent naming reduced retrieval quality for certain topics.
Escalation criteria needed tightening
Some billing and account change requests required stricter handoff rules.
Some billing and account change requests required stricter handoff rules.
Analytics event naming inconsistencies
Event naming needed standardization to compare channels accurately.
Event naming needed standardization to compare channels accurately.
How We Fixed It
RAG with quality controls
Implemented retrieval with freshness and source prioritization to reduce conflicting answers.
Guardrails and escalation
Added safe handling policies and automatic escalation for sensitive requests.
Evaluation and monitoring
Built dashboards for answer quality, deflection, and failure modes to guide iteration.
Measurable Outcomes
The assistant reduced repetitive support load while maintaining quality through evaluation and safe escalation.
58%
Ticket Deflection
35s
Median First Response
22%
CSAT Increase
6
Weeks to Pilot
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