# Bhashik **Bhashik** is Krutrim’s advanced Indic AI model, purpose-built for **vernacular speech and text processing**. It is optimized for **Text-to-Speech (TTS)** and **Speech-to-Text (STT)** use cases across multiple Indian languages, enabling natural, context-aware communication for diverse regional audiences. Bhashik powers a wide range of capabilities for both text and speech workflows, making it ideal for building applications such as voice assistants, multilingual customer support, automated transcription, and real-time translation. ### **Capabilities** #### **Text-based AI Services** * **Text Translation** – Translate written content between multiple languages with high accuracy and cultural nuance. * **Language Detection** – Automatically identify the language present in a given text snippet. * **Entity Extraction** – Identify and extract specific data points or entities from text for structured analysis. * **Sentiment Analysis** – Detect the overall sentiment of content or the sentiment toward a specific entity. * **Summarization** – Condense long text into concise summaries while preserving meaning. #### **Speech-based AI Services** * **Text to Speech (TTS)** – Convert written text into natural-sounding speech across multiple Indian languages. * **Speech to Text (STT)** – Transcribe audio into text accurately, making it easy to record and analyze spoken content. * **Speech to Speech** – Translate spoken language directly into another language, enabling fluid, multilingual conversations. ### **Key Advantages of Bhashik** * **Vernacular-first Approach** – Built to natively support Indian languages and dialects. * **Multi-modal Support** – Seamless integration across both text and speech processing tasks. * **High Accuracy** – Optimized for real-world scenarios such as noisy environments and regional pronunciations. * **Customizable** – Can be fine-tuned or adapted for domain-specific vocabulary and context. --- # Agent Instructions: Querying This Documentation If you need additional information that is not directly available in this page, you can query the documentation dynamically by asking a question. Perform an HTTP GET request on the current page URL with the `ask` query parameter: ``` GET https://docs.cloud.olakrutrim.com/basics/ai-solutions/bhashik.md?ask= ``` The question should be specific, self-contained, and written in natural language. The response will contain a direct answer to the question and relevant excerpts and sources from the documentation. Use this mechanism when the answer is not explicitly present in the current page, you need clarification or additional context, or you want to retrieve related documentation sections.