SupportLogic

What SupportLogic does

SupportLogic delivers the world’s first support experience (SX) management platform that enables companies to proactively understand and act on the voice of the customer to build healthy relationships and maximize customer lifetime value. SupportLogic SX uses predictive and generative AI to extract and analyze customer sentiment signals from both structured and unstructured data and provides recommendations, content and collaborative workflows. It provides a purpose-built AI infrastructure for processing tickets, chat, voice, and every customer interaction, replacing traditional support CRM stacks and survey software. The platform features autonomous AI agents, a unified Data Cloud, and an MCP server to power various AI models and applications. SupportLogic's Cognitive AI Cloud extracts data and signals from post-sales systems, builds context across customer history, and exposes this through an MCP server, Data Cloud, and API, allowing various tools to access a single source of truth. The company is ISO 27001 and SOC II Type 2 certified, as well as GDPR/HIPAA compliant.

Products

• SupportLogic SX PlatformThe core platform for support experience management, leveraging AI agents, a Data Cloud, and an MCP server to process customer interactions and provide insights.
• SupportLogic Cognitive AI CloudAn intelligence layer for data that extracts customer signals using predictive and generative AI, integrating with existing tools without replacement. It normalizes data across CRMs, extracts signals, and stores them in a Snowflake AI Data Cloud.
• Model Context Protocol (MCP) ServerExposes SupportLogic's AI agents, signals, and customer context to AI assistants like Claude, ChatGPT, and Gemini, providing grounded, persistent, and permissioned access to real-time customer data.
• Core SX BundleA product bundle powered by Sentiment, Escalation, Prioritization, Routing, and Language Agents to drive efficiency by understanding customer sentiment, reducing escalations, closing cases faster, and improving retention.
• Elevate SXA product powered by Coaching and Voice Agents that improves support quality through automated and manual QA, voice analytics, and predictive scoring for effective agent coaching.
• Resolve SXA product powered by Knowledge Agent and Prioritization Agent that empowers customers and agents by instantly retrieving accurate information, integrating with knowledge bases and existing cases.
• Sentiment AgentContinuously scores every customer interaction across various channels, generating sentiment scores per signal, case, and account.
• Escalation AgentPredicts escalations before they are filed, surfacing at-risk cases in advance for proactive intervention.
• Prioritization AgentRanks the entire backlog by urgency, business impact, and customer health to guide agents on which cases to work next.
• Routing AgentMatches every case to the most suitable engineer based on skills, bandwidth, shift coverage, and past performance.
• Coaching AgentPerforms QA on 100% of interactions, generates CES scores, identifies coachable moments, and supports managers in calibration.
• Voice AgentTranscribes, redacts, and analyzes voice calls, detecting tone, hold time, dead air, profanity, and professionalism, converting calls into structured signal streams.
• Knowledge AgentProvides precision RAG for accurate, cited answers from internal and external knowledge bases, and drafts new articles from resolved cases.
• Resolution AgentCloses Tier-1 issues end-to-end using grounded answers from the knowledge base, citing sources, and handing off to humans when necessary.
• Account Health AgentGenerates real-time account health scores from all signals across channels, pinpointing churn risk and expansion opportunities.
• Summarization AgentGenerates case summaries, account summaries, escalation digests, and weekly briefings on demand or pushed to communication platforms.
• Language AgentProvides auto-translation across replies and grammar/tone assist for agents, enabling support in multiple locales.
• SLA AgentMonitors SLAs for every case, alerting before breaches and surfacing patterns that indicate root-cause SLA problems.
• Text Analytics AgentSurfaces trends, themes, product feedback, and feature requests across thousands of cases, turning support into a continuous voice-of-customer feed.
• Context AgentMaintains memory across time, channel, and system for persistent context.
• Signal Extraction AgentExtracts over 40 signals from noise in customer interactions.
• Data Extraction AgentProvides zero-copy, differential sync for data.

Key capabilities

• Predictive and generative AI
• Autonomous AI agents
• Unified Data Cloud
• MCP server for AI models
• Real-time customer sentiment analysis
• Proactive alerts and recommendations
• Integration with existing CRM and support systems (Salesforce, Zendesk, ServiceNow, Jira, Dynamics, Freshdesk)
• Integration with voice & chat platforms (NICE, Genesys, 8x8, Zoom, Webex, RingCentral)
• Integration with data lakes (Snowflake, BigQuery, S3, Oracle)
• Integration with knowledge bases (Confluence, SharePoint, Salesforce Experience Cloud, MindTouch)
• Snowflake-native data access
• REST API for programmatic access
• Embeddable CRM widgets
• Auto QA and manual QA
• Voice analytics and transcription
• Predictive scoring
• Escalation prediction and management
• Backlog management
• Text analytics
• Proactive alerts
• SLA/SLO management
• CRM writeback
• Agent scheduling
• Grammar assist
• Next best action
• Case summarization
• Account summary and health score
• Auto case assignment
• Virtual teams, accounts, and queues
• Slack and MS Teams integration
• Case prioritization
• Language translation
• Precision RAG answer engine
• Knowledge extraction from existing cases
• Related cases suggestion
• Draft KB article creation
• Knowledge summary with source citation
• Zero-copy, single-tenant architecture
• ISO 27001, SOC II Type 2, GDPR, HIPAA compliance
• OAuth/TLS 1.2+, SHA-256/RSA Encryption, FIPS 104-2 Compliance, 2-Factor Authentication, Access via Bastion Host, Data Classification Matrix

Use cases

• Preventing customer escalations
• Reducing customer churn
• Elevating customer support experience
• Proactive customer support
• Automating support workflows
• Analyzing customer sentiment from structured and unstructured data
• Coaching agents effectively
• Improving support quality
• Reducing resolution time
• Eliminating survey tools
• Eliminating analytics and routing software
• Reducing operational expenses
• Improving net dollar retention (NDR)
• Uncovering growth opportunities and critical risks
• Eliminating knowledge gaps
• Eliminating manual coaching and QA
• Eliminating churn risk
• Eliminating backlog and operational inefficiencies
• Eliminating language barriers
• Eliminating manual routing
• Grounding AI assistants in real customer context
• Building custom workflows and applications
• Identifying and understanding support trends at scale
• Building more accurate customer health scores
• Analyzing sentiment at individual or group levels
• Discovering product usage trends and creating benchmarks
• Customer health scoring, churn risk prediction and customer journey mapping
• Post-sales customer marketing and digital customer success automation
• Building business intelligence dashboards and in-house AI applications
• Migrating between CRMs with reverse ETL
• Comparing customers across tailored dashboards
• Drilling down into specific product issues
• Tracking subsets of customers using virtual groups
• Executive briefings and reporting
• Slack copilots for support channels

AI approach

SupportLogic provides an AI infrastructure for customer experience, leveraging predictive and generative AI to analyze customer sentiment from structured and unstructured data. It uses a Cognitive AI Cloud with ambient AI agents (e.g., Sentiment, Escalation, Routing, Coaching, Knowledge, Voice, Account Health, Summarization, Language, SLA, Text Analytics, Resolution) to extract signals, build context, and provide recommendations and workflows. The platform integrates with existing CRMs and post-sales systems, offering a Model Context Protocol (MCP) server, Data Cloud, and API to ground AI assistants like Claude, ChatGPT, Gemini, and Microsoft Copilot with real customer context. It uses LLMs and small, domain-specific language models (SLMs) within an enterprise-level environment.

Tech named: predictive AI, generative AI, AI agents, Model Context Protocol (MCP), Cognitive AI Cloud, Ambient AI Agents, LLMs, small, domain-specific language models (SLMs), Natural Language Processing (NLP), Precision RAG

Industries served

• High Tech & Data
• Infrastructure
• Sales / Martech
• AI / Data
• Telecom

What it says sets it apart

• World’s first support experience (SX) management platform
• Purpose-built AI infrastructure for support, replacing traditional CRM and survey tools
• Cognitive AI Cloud extracts data and signals from all post-sales systems, building context across years of customer history
• Open interfaces (MCP server, Data Cloud, API) allow integration with various AI models and applications (Claude, ChatGPT, Gemini, Copilot)
• Snowflake-native integration for queryable signals, scores, and predictions directly from data warehouse
• Ambient AI agents run continuously across every case, call, and chat, replacing manual workflows
• Focus on proactive support and preventing escalations rather than reactive measures
• Guaranteed security and compliance (ISO 27001, SOC II Type 2, GDPR, HIPAA) with a zero-copy, single-tenant VPC architecture
• Ability to go live and extract signals within 45 days
• Hybrid pricing model that scales with business usage and seat-based licensing
• Leverages both LLMs and small, domain-specific language models (SLMs) for support use cases
• Provides grounded answers with source citations, persistent context, and permissioned access for AI assistants