Splutter AI

Splutter AI provides a curated library of pre-configured dialogue templates for common business scenarios (lead qualification, FAQ handling, appointment scheduling, ticket triage). These templates use intent-matching and slot-filling patterns to guide conversations without requiring custom training data or prompt engineering. Templates are parameterized to accept business-specific values (product names, pricing tiers, support categories) and can be deployed immediately without modification.

Splutter AI maintains conversation context across multiple turns by integrating with CRM systems to retrieve and reference customer history, previous interactions, and account metadata. The system uses this context to inform response generation, enabling the chatbot to reference past conversations, customer preferences, and account status without explicit re-prompting. Context is stored in a session state that persists across conversation turns and is synchronized with the underlying CRM database.

Splutter AI provides compliance features including data encryption, audit logging, and privacy controls to meet regulatory requirements (GDPR, CCPA, HIPAA). The platform logs all conversation data and system actions, enables data retention policies, and provides tools for data deletion and export. Conversations can be configured to exclude sensitive data (PII, payment info) from logging or to apply data masking.

Splutter AI provides pre-built connectors for major CRM (Salesforce, HubSpot, Pipedrive) and helpdesk platforms (Zendesk, Intercom, Freshdesk) that enable bi-directional data synchronization. The integration automatically creates leads, updates contact records, routes conversations to agents, and logs interactions back to the CRM without manual data entry. Connectors use OAuth 2.0 for secure authentication and support real-time event webhooks to trigger bot actions when CRM records change.

Splutter AI uses intent classification models to categorize incoming customer messages and route conversations to appropriate bot flows or human agents. The system analyzes message content to identify customer intent (e.g., 'billing question', 'product inquiry', 'complaint') and either handles the conversation with a bot flow or escalates to a human agent based on confidence thresholds and routing rules. Handoff includes full conversation history and customer context to ensure continuity.

Splutter AI uses large language models (LLM) to generate natural, contextually-appropriate responses to customer queries. The system combines template-based responses with LLM generation to handle both standard scenarios (using templates for speed and consistency) and novel queries (using LLM for flexibility). Responses are constrained by safety guardrails and business rules to prevent off-topic or inappropriate outputs.

Splutter AI provides built-in analytics dashboards that track conversation metrics (volume, duration, resolution rate, customer satisfaction) and identify patterns in bot performance. The system generates reports on which conversation types the bot handles well vs. poorly, which intents are most common, and where customers are escalating to agents. Insights are presented as actionable recommendations (e.g., 'improve FAQ coverage for billing questions', 'add new intent category for refund requests').

Splutter AI enables deployment of the same conversation logic across multiple channels (web chat widget, SMS, WhatsApp, Facebook Messenger, voice) without requiring separate bot configurations. The system abstracts channel-specific formatting and protocols, allowing a single conversation flow to work across text and voice interfaces. Channel-specific features (e.g., rich cards for web, quick replies for SMS) are automatically adapted based on the target channel.

Splutter AI provides a visual conversation builder that enables non-technical users to create complex, conditional conversation flows without coding. The builder uses a node-and-edge graph interface where users define conversation branches, conditions (if-then logic), variable assignments, and integrations. Flows can reference CRM data, perform calculations, and trigger external actions (API calls, CRM updates) based on conversation state.

Splutter AI includes a QA workflow that enables human reviewers to audit bot conversations, flag quality issues, and provide feedback to improve future responses. Reviewers can mark conversations as 'good', 'needs improvement', or 'escalation required' and add comments explaining issues. Feedback is aggregated to identify patterns (e.g., 'bot frequently misunderstands billing questions') and used to retrain or adjust bot behavior.

Splutter AI uses a conversation-volume-based pricing model where costs scale with the number of conversations handled. The platform provides transparent cost tracking and usage dashboards showing conversation volume, cost per conversation, and projected monthly costs. Pricing tiers offer different feature sets (basic templates, advanced analytics, multi-channel deployment) at different price points.