This organisation aims to work on creating NLP resources. Some ideas are:

1. Tokenization: Create a tool that can split Telugu text into meaningful tokens like words or characters. This is fundamental for many NLP tasks.

2. Part-of-Speech (POS) Tagger: Develop a POS tagger that assigns grammatical categories (like noun, verb, adjective) to each word in a Telugu sentence.

3. Named Entity Recognition (NER): Build a tool that can identify and classify entities in Telugu text, such as person names, organization names, and location names.

4. Text Classification: Create models for classifying Telugu text into predefined categories, such as sentiment analysis, topic classification, or spam detection.

5. Machine Translation: Develop a machine translation system that can translate between Telugu and other languages, or even build a Telugu-to-Telugu translation system for different dialects or styles.

6. Text Summarization: Build a tool that can generate summaries of Telugu text, helping users quickly grasp the main points of a document or article.

7. Language Generation: Create a tool that can generate coherent Telugu text based on input prompts, which can be useful for tasks like text completion or dialogue generation.

8. Spell Checker and Correction: Develop a spell checker and correction tool specifically tailored for Telugu to help users correct spelling errors in their text.

9. Dependency Parsing: Build a tool that analyzes the grammatical structure of Telugu sentences by identifying the relationships between words.

10. Language Model Fine-tuning: Fine-tune pre-trained language models like GPT-3 for Telugu language tasks to improve performance on specific NLP tasks.

11. Text-to-Speech (TTS) Synthesis: Create a high-quality text-to-speech synthesis system that converts Telugu text into natural-sounding speech. This can be particularly useful for visually impaired individuals or for applications requiring spoken content.

12. Speech Recognition: Develop a speech recognition system that accurately transcribes Telugu speech into text. This tool can enable hands-free interaction with devices and applications in Telugu.

13. Dialogue Systems: Build conversational agents or chatbots that can understand and respond to user queries and commands in Telugu. These systems can be deployed in various domains such as customer service, education, and entertainment.

14. Question Answering (QA) System: Create a QA system that can understand and answer questions posed in Telugu based on a given context or knowledge base. This tool can assist users in finding information quickly and accurately.

15. Language Understanding: Develop advanced models for understanding the semantics and context of Telugu text, including tasks like sentiment analysis, emotion recognition, and intent detection.

16. Language Generation with Control: Enhance language generation capabilities by allowing users to control specific attributes of the generated text, such as style, tone, or sentiment. This can enable more nuanced and personalized text generation in Telugu.

17. Document Summarization with Multi-document Input: Extend document summarization capabilities to handle multiple documents in Telugu, producing concise summaries that capture the essential information from a set of related documents.

18. Cross-lingual NLP: Build tools that facilitate cross-lingual understanding and interaction between Telugu and other languages. This could include tasks like cross-lingual information retrieval, translation, and sentiment analysis.

19. Knowledge Graph Construction: Develop algorithms to automatically construct knowledge graphs from Telugu text, capturing entities, relationships, and semantic information to represent knowledge in a structured form.

20. Advanced Text Analytics: Create tools for advanced text analytics tasks in Telugu, such as opinion mining, trend detection, and event extraction, to gain insights from large volumes of Telugu text data.