Continual

Indexes and embeds proprietary internal knowledge sources (documents, databases, APIs) into a vector store, then retrieves and synthesizes answers in real-time using retrieval-augmented generation (RAG). The system maintains semantic search over indexed content without requiring external API calls for every query, enabling privacy-preserving instant answers grounded in company-specific data rather than generic LLM knowledge.

Enables definition of multi-step workflows with conditional branching, state persistence, and integration with external systems via API calls or webhooks. Workflows are likely defined declaratively (YAML, JSON, or visual builder) and executed by an orchestration engine that manages state transitions, retries, and error handling across distributed steps without requiring custom backend code.

Classifies incoming text (customer queries, support tickets, emails) into predefined categories or extracts structured data (entities, intent, sentiment) using fine-tuned or prompt-based LLM inference. The system likely supports both zero-shot classification (via prompting) and few-shot learning (via examples), with results cached or indexed for analytics and workflow routing.

Provides a pre-built, embeddable chat widget or API that injects conversational AI directly into web or mobile applications without requiring custom UI development. The interface connects to Continual's backend for LLM inference, knowledge retrieval, and workflow execution, with support for conversation history, context management, and multi-turn interactions.

Abstracts underlying LLM provider selection (OpenAI, Anthropic, open-source models) behind a unified API, allowing developers to switch providers or route requests based on cost, latency, or capability requirements without changing application code. The system likely implements provider-agnostic prompt formatting and response parsing, with fallback logic to retry failed requests on alternative providers.

Enforces LLM outputs to conform to predefined JSON schemas or structured formats, with built-in validation and error handling for malformed responses. The system likely uses prompt engineering, function calling, or output parsing libraries to ensure LLM responses match expected structure, with fallback retry logic if validation fails.

Maintains conversation history and context across multi-turn interactions, with automatic summarization or compression of long conversations to stay within LLM context windows. The system likely stores conversation state in a managed backend, with support for context retrieval, relevance filtering, and optional memory persistence across sessions.

Tracks and analyzes AI interaction metrics (response latency, user satisfaction, classification accuracy, cost per interaction) with dashboards and reporting capabilities. The system likely collects telemetry from chat interactions, workflow executions, and LLM calls, with aggregation and visualization for performance optimization and cost analysis.

Exposes REST or GraphQL APIs for programmatic access to core AI capabilities (instant answers, classification, extraction, workflow execution) without using the embedded chat widget. Developers can call APIs directly from backend services, integrating AI features into custom applications or workflows with full control over request/response handling.