Gnani.ai Showcases Real-Time AI Voice Automation at MLDS 2025 - Related to key, agent, copilot, expands, showcases
GitHub Copilot adds Agent Mode, expands AI capabilities
Developer platform GitHub has introduced Agent Mode for GitHub Copilot, giving its AI-powered coding assistant the ability to iterate on its own code, recognise errors, and fix them automatically.
“Today, we are infusing the power of agentic AI into the GitHub Copilot experience, elevating Copilot from pair to peer programme,” wrote GitHub CEO Thomas Dohmke on X while announcing the launch.
Alongside this, Copilot Edits is now generally available in Visual Studio Code, and GitHub has provided a first look at Project Padawan, an autonomous AI-driven software engineering agent.
“The name [Copilot] reflects our belief that artificial intelligence (AI) isn’t replacing the developer. Instead, it’s always on their side. And like any good first officer, a Copilot can also fly by itself,” read their statement.
Agent Mode enhances Copilot’s ability to handle complex coding tasks autonomously. It can refine its own output, detect and fix errors, suggest terminal commands, and analyse runtime issues with self-healing capabilities. To access it, developers need VS Code Insiders and must enable the Copilot Agent setting.
Now available for all, Copilot Edits lets developers edit multiple files using natural language prompts. The AI makes inline changes, streamlining workflow across projects. It supports multiple AI models, including OpenAI’s GPT-4o, Anthropic’s Claude [website] Sonnet, and Google’s Gemini [website] Flash. A Visual Studio 2022 preview is also available.
GitHub is also developing Project Padawan, an AI Software Engineering (SWE) agent capable of handling coding tasks, reviewing code, and automating workflows. Developers will be able to assign GitHub Issues to Copilot, which will generate fully tested pull requests and respond to reviewer feedback. The system will operate in a secure cloud sandbox.
As introduced, GitHub plans to expand Agent Mode to more IDEs and improve Project Padawan. Developers can try these elements in VS Code Insiders and share feedback through GitHub’s official channels.
Last month, at the Microsoft AI Tour in Bengaluru, GitHub’s director of international developer relations, Karan MV, showcased how Copilot Workspace can comprehend and generate code in Indian languages, including Hindi and Kannada.
Dohmke also highlighted how Copilot Workspace is transforming software development in local languages. “As always, Karan MV demonstrated the power of Copilot Workspace—building with agents in Kannada. This is the way. Every developer will conduct a symphony of AI agents in natural language,” he expressed.
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Gnani.ai Showcases Real-Time AI Voice Automation at MLDS 2025
The AI-driven solutions developed for enterprise-scale use are enhancing customer experiences, reducing call handling times, and automating millions of daily interactions across industries such as banking, insurance, and retail.
In a presentation at MLDS 2025, AI innovators at Gnani ai showcased how AI-powered voice agents they developed are transforming industries.
Ganesh Gopalan, CEO and co-founder of [website], shared an example where an AI voice agent helped a user who had forgotten about their PAN card application within just 24 hours.
Voice Agents That Talk, Think, and Assist in Real Time.
AI voice agents are more than just chatbots. “They are the next level of automation—handling real-time conversations at scale,” Gopalan explained.
Their AI-powered voice customer service agents have already processed over 30,000 concurrent calls and millions of daily interactions.
The technology works across platforms, including telephone, WhatsApp, and iMessage, automating customer service and reducing operational costs.
“Our AI can lower call handling time by 15% and improve outcomes for customer support agents by 40%,” Gopalan shared.
The Challenges of Building AI Voice Agents.
Creating an AI voice assistant is not as simple as using a chatbot. Bharat Shankar, co-founder and chief product officer at [website], highlighted the need for high-quality speech recognition that works across various accents and languages.
Another major challenge is handling interruptions naturally, as consumers frequently cut in during conversations. “consumers interrupt a lot. AI must process interruptions smoothly,” he mentioned.
Also, reducing response time is also critical since long pauses negatively impact user experience. “Long pauses kill the experience. Our AI responds in under two seconds,” Shankar noted.
While data security remains a top priority, especially for enterprises that require strict compliance, scaling AI-powered conversations requires advanced cloud infrastructure to handle millions of conversations daily.
To solve these challenges, [website] has developed a “speech-to-speech LLM” (Large Language Model) that processes both audio and text simultaneously for faster, more efficient interactions.
“This is the future—AI that thinks and speaks instantly,” Shankar stated, revealing that the technology will be showcased at NVIDIA’s GTC conference in California in March.
In the insurance sector, a live demonstration was showcased where an AI-powered assistant seamlessly handled customer queries by greeting callers politely, switching languages from English to Hindi mid-conversation, recognising customer preferences, and offering customised insurance plans.
The entire interaction was completed in under two minutes, including a WhatsApp link for document submission. In banking and government services, AI voice agents are helping streamline processes, such as retrieving PAN card details in record time, demonstrating how AI can improve government services. “end-individuals no longer need to wait days for a simple query—AI speeds up the process,” noted Gopalan.
To make AI adoption easier, [website] launched an “Agent Builder” platform, an end-to-end tool for businesses to create, test, and deploy AI-powered voice agents. The platform offers support for 40+ languages, 400+ pre-built AI templates, real-time voice orchestration, and seamless CRM and enterprise integrations.
Looking ahead, AI voice agents will understand emotions and adjust their tone, seamlessly switch between multiple languages, and handle complex multi-step tasks automatically.
As businesses increasingly rely on AI to reduce wait times, improve accuracy, and enhance human-like interactions, early adopters will gain a significant competitive edge. “The future is here. AI voice agents are not just changing businesses—they are transforming customer experiences forever,” Gopalan concluded.
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MLDS 2025: Key Highlights from Day 3
The last day of MLDS 2025, India’s largest developer conference, kicked off with Paras Chopra, founder of Turing’s Dream (now LossFunk), discussing how India can build advanced AI models for its own needs.
Chopra stressed the urgency of AI investment, with the [website] pouring $500 billion and China $150 billion into the field.“Does it make sense for the world’s most populous country to be left behind permanently?” he asked.
To stay competitive, India must develop foundation models, drawing on DeepMind’s breakthroughs and the country’s strong AI talent. He stressed the need for India to have home-grown superintelligent AI models.
His vision for India competing in the AI SOTA landscape was clear—an “ISRO of AI” by 2025, making India a global AI powerhouse.
Building on the theme of AI’s impact on the developer ecosystem, Invact Metaversity CEO Tanay Pratap explored AI’s impact on programming, presenting both challenges and opportunities for developers.
Drawing on his experience at Microsoft and Cisco, where he trained over 1,000 students in coding, he noted that while AI can predict tokens, it lacks a true understanding of code functionality, highlighting the need for human oversight and improved training models.
Taking this discussion further into real-world applications, Abhishek A, the chief technology officer at SmartQ, and Keshav Meda, co-founder and chief growth officer, presented their insights into how SmartQ is acting as a bridge between humans and machines, enabling structured communication.
They highlighted the importance of purpose-built hardware, software optimisation, and high-quality data in accelerating this shift.
During the talk, Abhishek showcased Leva, Smart Cube’s in-house AI tool that automates software development, improving overall productivity. Looking ahead, he predicts a future where AI writes code at lightning speed, customises user interfaces, and manages real-time updates, fundamentally reshaping the way software is built and maintained.
Shifting the focus to AI’s role in financial services, the event also saw Mathangi Sri Ramachandran, chief data officer at Yubi, lead a workshop on how Large Language Models (LLMs) are transforming finance. She spoke about their role in risk management, fraud detection, automated reporting, and personalised customer experiences, providing real-world applications and fine-tuning strategies.
As AI continues to disrupt industries, its convergence with Web3 is unlocking new possibilities. Vivek Gupta, CTO at CoinDCX, shared insights on how Web3 and AI are shaping the next wave of innovation in his session on building crypto in an AI world.
With AI playing a more central role in decision-making, ensuring strong governance is crucial. Mantra Labs CTO Kumar Sambhav Singh talked about the importance of strong data and AI governance, highlighting the need for accuracy, reliability, accessibility, and compliance with industry standards.
In addition to this, Saurabh Pramanick, data governance officer at Bank Muscat, highlighted executives driving AI governance in organisations with larger budgets and the urgency of their investments. The session covered the forecast for new AI models, the tools and frameworks in use, and confidence in AI capabilities. It also examined key AI risks, regulations, and governance ownership.
AI is also transforming the education industry. Navin Reddy, founder of Telusko Edutech, talked about how AI is changing the way students learn and solve problems. While they once turned to books, peers, and online trainers, many now rely on AI for debugging and troubleshooting. He stressed that educators now have an even bigger role in helping students use AI effectively while building a strong foundation in core concepts.
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Market Impact Analysis
Market Growth Trend
| 2018 | 2019 | 2020 | 2021 | 2022 | 2023 | 2024 |
|---|---|---|---|---|---|---|
| 23.1% | 27.8% | 29.2% | 32.4% | 34.2% | 35.2% | 35.6% |
Quarterly Growth Rate
| Q1 2024 | Q2 2024 | Q3 2024 | Q4 2024 |
|---|---|---|---|
| 32.5% | 34.8% | 36.2% | 35.6% |
Market Segments and Growth Drivers
| Segment | Market Share | Growth Rate |
|---|---|---|
| Machine Learning | 29% | 38.4% |
| Computer Vision | 18% | 35.7% |
| Natural Language Processing | 24% | 41.5% |
| Robotics | 15% | 22.3% |
| Other AI Technologies | 14% | 31.8% |
Technology Maturity Curve
Different technologies within the ecosystem are at varying stages of maturity:
Competitive Landscape Analysis
| Company | Market Share |
|---|---|
| Google AI | 18.3% |
| Microsoft AI | 15.7% |
| IBM Watson | 11.2% |
| Amazon AI | 9.8% |
| OpenAI | 8.4% |
Future Outlook and Predictions
The Mlds 2025 Github landscape is evolving rapidly, driven by technological advancements, changing threat vectors, and shifting business requirements. Based on current trends and expert analyses, we can anticipate several significant developments across different time horizons:
Year-by-Year Technology Evolution
Based on current trajectory and expert analyses, we can project the following development timeline:
Technology Maturity Curve
Different technologies within the ecosystem are at varying stages of maturity, influencing adoption timelines and investment priorities:
Innovation Trigger
- Generative AI for specialized domains
- Blockchain for supply chain verification
Peak of Inflated Expectations
- Digital twins for business processes
- Quantum-resistant cryptography
Trough of Disillusionment
- Consumer AR/VR applications
- General-purpose blockchain
Slope of Enlightenment
- AI-driven analytics
- Edge computing
Plateau of Productivity
- Cloud infrastructure
- Mobile applications
Technology Evolution Timeline
- Improved generative models
- specialized AI applications
- AI-human collaboration systems
- multimodal AI platforms
- General AI capabilities
- AI-driven scientific breakthroughs
Expert Perspectives
Leading experts in the ai tech sector provide diverse perspectives on how the landscape will evolve over the coming years:
"The next frontier is AI systems that can reason across modalities and domains with minimal human guidance."
— AI Researcher
"Organizations that develop effective AI governance frameworks will gain competitive advantage."
— Industry Analyst
"The AI talent gap remains a critical barrier to implementation for most enterprises."
— Chief AI Officer
Areas of Expert Consensus
- Acceleration of Innovation: The pace of technological evolution will continue to increase
- Practical Integration: Focus will shift from proof-of-concept to operational deployment
- Human-Technology Partnership: Most effective implementations will optimize human-machine collaboration
- Regulatory Influence: Regulatory frameworks will increasingly shape technology development
Short-Term Outlook (1-2 Years)
In the immediate future, organizations will focus on implementing and optimizing currently available technologies to address pressing ai tech challenges:
- Improved generative models
- specialized AI applications
- enhanced AI ethics frameworks
These developments will be characterized by incremental improvements to existing frameworks rather than revolutionary changes, with emphasis on practical deployment and measurable outcomes.
Mid-Term Outlook (3-5 Years)
As technologies mature and organizations adapt, more substantial transformations will emerge in how security is approached and implemented:
- AI-human collaboration systems
- multimodal AI platforms
- democratized AI development
This period will see significant changes in security architecture and operational models, with increasing automation and integration between previously siloed security functions. Organizations will shift from reactive to proactive security postures.
Long-Term Outlook (5+ Years)
Looking further ahead, more fundamental shifts will reshape how cybersecurity is conceptualized and implemented across digital ecosystems:
- General AI capabilities
- AI-driven scientific breakthroughs
- new computing paradigms
These long-term developments will likely require significant technical breakthroughs, new regulatory frameworks, and evolution in how organizations approach security as a fundamental business function rather than a technical discipline.
Key Risk Factors and Uncertainties
Several critical factors could significantly impact the trajectory of ai tech evolution:
Organizations should monitor these factors closely and develop contingency strategies to mitigate potential negative impacts on technology implementation timelines.
Alternative Future Scenarios
The evolution of technology can follow different paths depending on various factors including regulatory developments, investment trends, technological breakthroughs, and market adoption. We analyze three potential scenarios:
Optimistic Scenario
Responsible AI driving innovation while minimizing societal disruption
Key Drivers: Supportive regulatory environment, significant research breakthroughs, strong market incentives, and rapid user adoption.
Probability: 25-30%
Base Case Scenario
Incremental adoption with mixed societal impacts and ongoing ethical challenges
Key Drivers: Balanced regulatory approach, steady technological progress, and selective implementation based on clear ROI.
Probability: 50-60%
Conservative Scenario
Technical and ethical barriers creating significant implementation challenges
Key Drivers: Restrictive regulations, technical limitations, implementation challenges, and risk-averse organizational cultures.
Probability: 15-20%
Scenario Comparison Matrix
| Factor | Optimistic | Base Case | Conservative |
|---|---|---|---|
| Implementation Timeline | Accelerated | Steady | Delayed |
| Market Adoption | Widespread | Selective | Limited |
| Technology Evolution | Rapid | Progressive | Incremental |
| Regulatory Environment | Supportive | Balanced | Restrictive |
| Business Impact | Transformative | Significant | Modest |
Transformational Impact
Redefinition of knowledge work, automation of creative processes. This evolution will necessitate significant changes in organizational structures, talent development, and strategic planning processes.
The convergence of multiple technological trends—including artificial intelligence, quantum computing, and ubiquitous connectivity—will create both unprecedented security challenges and innovative defensive capabilities.
Implementation Challenges
Ethical concerns, computing resource limitations, talent shortages. Organizations will need to develop comprehensive change management strategies to successfully navigate these transitions.
Regulatory uncertainty, particularly around emerging technologies like AI in security applications, will require flexible security architectures that can adapt to evolving compliance requirements.
Key Innovations to Watch
Multimodal learning, resource-efficient AI, transparent decision systems. Organizations should monitor these developments closely to maintain competitive advantages and effective security postures.
Strategic investments in research partnerships, technology pilots, and talent development will position forward-thinking organizations to leverage these innovations early in their development cycle.
Technical Glossary
Key technical terms and definitions to help understand the technologies discussed in this article.
Understanding the following technical concepts is essential for grasping the full implications of the security threats and defensive measures discussed in this article. These definitions provide context for both technical and non-technical readers.
platform intermediate
large language model intermediate
API beginner
How APIs enable communication between different software systemsinterface intermediate
machine learning intermediate
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