The Challenge
Customer expectations are outpacing support team capacity
Ticket volume growth, channel proliferation, and churn risk visibility gaps are compressing CX team performance and retention outcomes.
Support Volume Scaling
Customer support ticket volumes grow linearly with revenue, but headcount can't keep pace without destroying unit economics. Legacy chatbots deflect 20–30% of tickets but frustrate customers with poor resolution quality.
Resolution Quality Inconsistency
Support quality varies dramatically by agent, shift, and ticket type. Senior agents carry disproportionate load on complex issues while junior agents struggle, creating NPS variance that's hard to manage at scale.
Churn Signal Detection
Customers who are about to churn show behavioral signals weeks before cancellation, support ticket patterns, usage decline, NPS score drops. Most CX teams lack systems to synthesize these signals at scale.
Voice of Customer Fragmentation
Customer feedback lives in support tickets, NPS surveys, app store reviews, social media, and community forums. Without AI synthesis, product and operational insights buried in this data are never surfaced.
Education AI Solutions
AI systems for support, retention, and customer insight
AI Support Agent
Conversational AI that resolves Tier 1 and Tier 2 support requests across web chat, email, and voice, with access to account data, documentation, and resolution history to solve problems completely.
Agent Assist AI
Real-time AI copilot for human support agents, surfacing relevant knowledge base articles, suggested responses, and next-best-action recommendations during live customer interactions.
Churn Risk Intelligence
ML models that synthesize support ticket patterns, usage signals, payment history, and NPS responses to score churn risk by customer, triggering proactive retention interventions.
Voice of Customer AI
AI analysis of support tickets, reviews, surveys, and community posts to surface recurring themes, product pain points, and operational failures, delivered weekly to product and leadership teams.
Sentiment & CSAT Prediction
AI that predicts customer satisfaction scores from interaction transcripts in real-time, flagging low-CSAT interactions for manager review before the survey response is submitted.
Customer Journey Intelligence
AI mapping of customer touchpoints, friction points, and drop-off moments across the full customer lifecycle, identifying the highest-impact experience improvements systematically.
End-to-End Implementation
End-to-end AI implementation for customer experience
Solnix delivers the full lifecycle, from opportunity mapping through production deployment and continuous improvement. The same proven methodology powers every customer experience engagement, tailored to your systems, data, and workflows.
Discovery & AI Opportunity Mapping
We start by understanding your operations, data landscape, and goals, then map where AI delivers measurable value and where it does not. Every engagement begins with a prioritized opportunity backlog, not a technology pitch.
Data Foundation & Readiness
AI is only as good as the data behind it. We assess data quality, connect fragmented sources, and build the secure, governed pipelines that production AI depends on, with privacy and compliance designed in from the start.
Model & Agent Development
We build the models, retrieval systems, and AI agents tailored to your use cases, selecting the right approach (fine-tuning, RAG, multi-agent orchestration) for accuracy, cost, and latency, and validating against your real-world edge cases.
Integration & Workflow Embedding
AI only creates value when it lives inside the tools your teams already use. We embed models and agents into existing systems, surfaces, and workflows, so adoption is natural and human-in-the-loop controls stay in place.
Deployment, Security & Compliance
We deploy to production with the security, monitoring, and compliance controls enterprises require, including bias and fairness testing, audit logging, and the observability needed to operate AI responsibly at scale.
Optimization & Continuous Improvement
AI systems improve with use. We measure outcomes against the goals set in Phase 01, retrain and tune from live feedback, and expand to the next set of use cases, turning a single deployment into a compounding capability.
Methodology
How Solnix Builds CX AI
01, Support Workflow Assessment
01, Support Workflow Assessment
We analyze your ticket taxonomy, resolution patterns, and escalation flows, identifying the ticket types with highest deflection potential and the agent workflows where AI assist delivers most value.
02, Knowledge Base & Resolution History Ingestion
02, Knowledge Base & Resolution History Ingestion
CX AI is trained on your specific product documentation, resolved ticket history, and macros, so it gives contextually accurate answers to your customers, not generic responses.
03, Customer Data Integration
03, Customer Data Integration
We integrate with Zendesk, Salesforce Service Cloud, Intercom, and Freshdesk. AI agents have access to account data, subscription status, and interaction history to resolve issues completely on first contact.
04, Escalation Logic Design
04, Escalation Logic Design
AI support agents need clear escalation protocols, when to hand off to a human, what context to pass, and how to manage the transition gracefully. We design escalation logic that preserves customer experience quality.
05, NPS & CSAT Impact Measurement
05, NPS & CSAT Impact Measurement
We establish CSAT, NPS, and resolution rate baselines before deployment and measure impact at 60 and 120 days. AI-handled interaction quality is compared to human agent baselines across all key metrics.
FAQ
Questions from CCOs, VP CX, and support operations leaders
Get Started
Resolve More. Retain More. Understand Your Customers Better.
Solnix builds CX AI that lets support teams scale without proportional headcount growth, and gives product and operations teams the customer intelligence they need to improve what matters.
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