Moving customer support from reactive ticket queues to instant, contextual resolution—handling 28,000+ support conversations monthly with 91% first-contact resolution and sub-3-second response times.
First-Contact Resolution
Escalation to Human
Response Time
Key Outcomes
91% first-contact resolution achievable with RAG grounding and confidence gating
Resolution confirmation loop is critical—don't assume delivery equals resolution
Closed knowledge loop (resolved tickets → training data) compounds improvement over time
Agent satisfaction improves significantly when AI filters out repetitive work
Handoff quality to human agents is as important as AI resolution rate
Brainfish uses a retrieval-augmented generation (RAG) system that combines a fine-tuned language model with a live knowledge base of product documentation, past resolved tickets, and policy FAQs. When a customer asks a question, the system retrieves the most relevant context from all knowledge sources, generates a precise answer grounded in that context, and resolves 91% of tickets without human involvement. For the remaining 9%, it generates a detailed handoff summary for the human agent.
Brainfish is an AI-powered customer support platform used by B2B SaaS companies processing millions of customer interactions. The platform sits in front of existing support ticketing systems and handles routine inquiries, product how-to questions, billing issues, and technical troubleshooting—routing only genuine exceptions to human agents.
In B2B SaaS, the same questions get asked thousands of times. 'How do I export my data?', 'Why was I charged twice?', 'Why is the integration not syncing?'—these account for 70%+ of all support volume. Every ticket is handled identically by a human agent who has answered the same question 500 times before. The work is repetitive, agents burn out, and customers wait.
73%
Repetitive Ticket Rate
Proportion of support tickets that ask questions with existing, known answers in product documentation or past tickets.
6.5 min
Average First Response Time
Time customers waited for initial response before AI, including queue time and agent reading time.
42%
Agent Annual Turnover
High turnover driven by repetitive, low-engagement work—costing companies 6–9 months salary per replacement.
AGIX Technologies built a retrieval-augmented support system that ingests the company's entire knowledge base—documentation, FAQs, past tickets, policy documents—and uses semantic search plus a generative model to compose accurate, contextual answers. The system handles the full resolution flow: understand intent, retrieve context, generate answer, confirm resolution, escalate if needed.
Intent Classification Engine
Classifies incoming queries into intent categories (billing, how-to, technical, account) to route to the appropriate knowledge domains and response templates.
Multi-Source Knowledge Retrieval
Semantic search across product docs, FAQ articles, and 24 months of resolved tickets to find the most relevant context for each specific query.
Answer Generation with Grounding
GPT-4 class model generates answers grounded in retrieved context, with citations to source documents and step-by-step instructions where applicable.
Resolution Confidence Scoring
Each generated answer receives a confidence score based on retrieval quality and answer coherence. Low-confidence responses are flagged for human review before delivery.
Seamless Escalation Handoff
When escalation is needed, the system generates a complete case summary—conversation history, extracted entities, attempted solutions—so agents have full context immediately.
Continuous Learning Pipeline
Every resolved ticket (human or AI) enriches the knowledge base. CSAT scores on AI-handled tickets drive targeted knowledge gap identification and resolution.
First-Contact Resolution
Tickets resolved by AI without any human agent involvement across all intent types
Median Response Time
vs 6.5 minutes average before AI implementation across all channels
Human Escalations
Reduction in tickets requiring human agent involvement since deployment
AI CSAT Score
Customer satisfaction rating on AI-handled conversations vs 4.2 for human-only
"Our support team used to handle 3,000 tickets a week. Now they handle 650—and they're the hard ones that actually require judgment. Job satisfaction scores went up 40% because agents are finally doing interesting work."
VP of Customer Success
Series C SaaS Company
Receive query and identify what the customer needs
Incoming queries arrive via chat widget, email integration, or API. The intent classifier identifies the query type (billing, how-to, technical, account management) and extracts key entities: product names, error codes, account identifiers, specific features mentioned.
Grounding Prevents Hallucination
By constraining the generative model to only use retrieved context, the system cannot fabricate answers—every response traces back to a verified source document.
Confidence Gating Maintained Quality
Not delivering every answer automatically—routing low-confidence responses through human review—protected customer experience during the transition period.
Resolution Confirmation Loop
Asking customers to confirm resolution rather than assuming success identified a 9% gap between 'answer delivered' and 'issue actually resolved', driving targeted improvements.
Continuous Knowledge Base Enrichment
Every resolved ticket becoming a training example meant the system improved fastest in exactly the areas customers asked about most frequently.
Agent Handoff Quality
Agents receiving AI-generated handoff summaries resolved escalated tickets 45% faster than cold transfers, creating a strong incentive for agents to trust the system.
Every AI system has constraints. Here's what to know before building something similar.
Complex Multi-System Issues
When a customer's problem spans multiple product areas, integrations, or requires account-level investigation, the system often cannot resolve without human access to internal tools.
Emotionally Escalated Customers
Customers who are angry or distressed need human empathy. The system detects emotional escalation signals and routes proactively, but some cases arrive already too elevated for AI handling.
Knowledge Base Quality Dependency
The system is only as good as the documentation it retrieves from. Companies with outdated, inconsistent, or sparse documentation see lower resolution rates until they invest in knowledge management.
Product Changes Require Knowledge Updates
New product features or pricing changes must be reflected in the knowledge base before the AI can handle related questions. There's typically a 24–48 hour lag between a product change and AI coverage.
Explore the services, industry solutions, and intelligence types that power this system.
Common questions about building ai self-service resolution systems like the one deployed at Brainfish.