Conva.ai

Native natural language understanding engine with dedicated support for Indian languages (Hindi, Tamil, Telugu, Kannada, Marathi, Bengali) alongside English, using language-specific tokenization, morphological analysis, and intent classification models trained on regional linguistic patterns. Unlike generic multilingual models that treat all languages equally, Conva.ai implements language-specific NLU pipelines that handle script variations, grammatical structures, and colloquialisms native to each language.

End-to-end speech recognition and NLU pipeline that converts audio input directly to structured intents and entities, combining automatic speech recognition (ASR) with intent classification in a single flow. The architecture streams audio frames to the ASR engine, buffers recognized text, and pipes it through the NLU layer to extract actionable intents without requiring intermediate manual transcription steps.

Automatic fallback mechanism that detects when the bot cannot confidently handle a user request (low intent confidence, unrecognized intent, or repeated failures) and seamlessly escalates to human agents. The system can transfer conversation context, conversation history, and extracted information to the human agent, enabling warm handoffs without requiring users to repeat information.

Stateful conversation engine that maintains context across multiple user-assistant exchanges, tracking conversation history, user intents, extracted entities, and dialogue state within a session. The system implements a context window that persists user information and previous turns, enabling the assistant to resolve pronouns, handle follow-up questions, and maintain coherent multi-step conversations without requiring the client to manage state externally.

Library of pre-trained intent and entity models for vertical-specific domains (e-commerce, banking, customer service, travel, food delivery) that can be deployed immediately without custom training. These models include domain-specific intents (e.g., 'book_flight', 'check_account_balance', 'track_order'), entities (e.g., 'destination', 'account_type', 'order_id'), and dialogue flows optimized for each vertical, reducing time-to-deployment from weeks to days.

NLU module that parses user input to identify the user's intent (what they want to do) and extracts relevant entities (parameters needed to fulfill the intent), returning structured JSON with confidence scores for each extraction. The system uses neural sequence labeling for entity extraction and intent classification, providing confidence thresholds that allow applications to handle low-confidence predictions by requesting clarification or escalating to human agents.

Low-code interface for designing multi-turn conversation flows using a visual node-and-edge graph editor, where nodes represent dialogue states (user input, bot response, decision branches) and edges represent transitions. Developers can define branching logic, slot-filling sequences, and fallback paths without writing code, with the builder generating executable dialogue specifications that the runtime engine interprets.

RESTful and SDK-based integration layer that allows developers to embed Conva.ai NLU and dialogue capabilities into native iOS/Android apps and web applications. The platform provides language-specific SDKs (iOS, Android, JavaScript) that handle audio capture, API communication, and response rendering, with built-in error handling, retry logic, and offline fallbacks.

NLU module that analyzes user messages to detect emotional tone and sentiment (positive, negative, neutral, frustrated, satisfied), enabling applications to respond contextually or escalate to human agents when users are dissatisfied. The system uses language-specific sentiment models trained on conversational data, with support for sarcasm and context-dependent sentiment in Indian languages.

Framework for defining custom entity types and implementing slot-filling dialogues that guide users through providing required information. Developers define entity schemas (e.g., 'flight_destination', 'departure_date', 'passenger_count') and the system automatically extracts these from user input, prompts for missing slots, and validates extracted values against business rules.

Dashboard and analytics engine that tracks conversation metrics (total conversations, average turn count, intent distribution, user satisfaction, escalation rate) and provides insights into bot performance. The system logs all conversations (with privacy controls), identifies common failure patterns, and surfaces recommendations for dialogue improvement based on conversation data.