It's been an inspiring quarter at Vtrio, and I want to take a moment to reflect on the amazing progress our team has made.
I'm thrilled to share a milestone moment — the launch of our new content team's very first Ezine.
Congratulations and thank you for breathing new energy into our storytelling. Onward and upward!
Our AI R&D team has been delivering impressive outcomes. We're not just playing around with prompts —
we're diving deep into Large Language Models (LLMs) and building true AI capabilities. This is a leap forward,
and we're excited to pitch our innovations. Huge thanks to Amazon, who supports in a big way.
A big shoutout to our team working with the Australian client on the Carbon Farming Project.
We secured this project thanks to our relentless focus on tech excellence.
It's a textbook case of how we learn fast and execute even faster. Kudos to the entire team!
We now also have a Japanese language researcher on board, expanding our horizons and pushing the boundaries of what we can offer.
Alongside this, our interns have been doing stellar work — bringing in fresh energy and ideas.
We've received great feedback for the well-deserved annual hike. Let's keep this momentum going!
Thank you once again for all your efforts.
On a broader note — look around. The world has truly embraced AI. Every industry is finding new ways to innovate.
My advice: stay curious, stay updated, and keep learning. AI isn't the future anymore — it's the now.
Last, but not the least, my heartfelt gratitude to everyone who came to my place for the get-together.
It was lovely to see everyone enjoying the day with singing, joking, pulling each other's leg,
and watching the movie together. Moments like these build stronger teams and lasting memories.
Q: Can you describe your current role and main responsibilities as a TV & Film Research Coordinator?
A: Certainly. In my role as a TV & Film Research Coordinator, I focus on researching and maintaining accurate metadata for Indian television programs and feature films. This includes tracking what's currently airing or has recently been released, as well as identifying upcoming projects in development. I gather information such as cast, crew, production companies, networks, and executive contacts. In addition to collecting this data, I ensure its accuracy and consistency across the platform. I also work closely with other team members to align on updates and maintain a high standard of data quality across our entire system.
Q: What type of content do you typically track in your day-to-day work?
A: On a daily basis, I track a wide range of content including top-performing television shows, high-profile feature films, and newly announced or developing projects. I monitor various platforms, media outlets, and press releases to capture details the moment a project is announced or updated. My focus is not just on mainstream Hindi content, but also regional programming in languages like Tamil, Telugu, Malayalam, and Bengali, ensuring a holistic representation of Indian entertainment.
Q: How do you ensure the accuracy and integrity of the metadata you handle?
A: Accuracy is a critical part of my job. I rely on trusted sources, official press releases, production announcements, and established media channels to gather and verify information. Whenever possible, I directly communicate with industry professionals to confirm details such as casting, production schedules, and executive involvement. I also perform regular audits of existing data to eliminate inconsistencies or outdated information, and cross-check entries with team members to ensure a high degree of reliability.
Q: What kind of metadata do you collect for TV shows and films?
A: The metadata I collect is quite comprehensive. For both television and film projects, this includes the title (in native language and English), language, genre, cast and crew information, production companies, networks or distributors, release dates, and development stage.
I also collect and update executive contact information, format types (e.g., series, mini-series, feature film),
episode counts (for TV), and any changes related to scheduling or team composition.
Q: How do you manage in-development projects and keep the database current?
A: In-development projects are dynamic and require constant monitoring.
I follow entertainment news portals, industry blogs, social media, and official production announcements to catch updates in real time.
Once a project is spotted, I enter its initial details into our system and mark it as "in development."
From there, I monitor it regularly for updates on casting, shooting schedules, or release changes.
This way, the database stays up-to-date and accurately reflects the project's progress over time.
Q: How tough was it to find a Japanese resource?
A: Finding the right person for a niche role like Japanese Film Research Coordinator was definitely challenging.
We weren't just looking for someone fluent in Japanese—it had to be someone deeply immersed in Japanese media, pop culture,
and historical context, with strong research capabilities.
It took time to find someone who wasn't just linguistically skilled, but also genuinely passionate about the storytelling
traditions of Japanese cinema and animation.
When we came across Aishwarya, it was clear her profile was a rare match.
Q: What is the scope of Aishwarya's role?
A: Aishwarya's role as our Japanese Film Research Coordinator is quite specialized and integral to our archival process.
She's responsible for researching and verifying credits from Japanese films, television shows, and animated content.
That includes identifying cast, crew, production details, and ensuring that all data is both culturally accurate
and consistent with our global formatting standards.
What sets her apart is not just her language fluency, but her deep understanding of Japanese society, history, and pop culture—
which allows her to interpret and contextualize information that might otherwise be lost in translation.
Her work adds a rich cultural layer to our structured datasets and ensures we're honoring the authenticity of Japanese media
while making it accessible in a global context.
Apple in Talks to Acquire Perplexity AI.
A potential $14B deal could supercharge Siri and reduce Apple's reliance on Google Search.
Developers can now run Gemini 2.5 Pro directly from their terminal — enabling code generation, debugging, and automation with natural language.
Meta Acquires PlayAI
Strengthens Meta's voice tech for smart glasses and VR headsets.
DeepMind's AlphaGenome Launched
A new AI model that simulates genetic mutations, revolutionizing genomics and cancer research.
Elon Musk's Grok AI Under Fire
Grok 4 Heavy identified with the surname "Hitler," sparking backlash and raising concerns about model safety and moderation.
ChatGPT Becomes #1 Free App Globally
Surpassing TikTok and Facebook in downloads — AI is officially mainstream.
In an era where speed, accuracy, and personalization are vital, the integration of Artificial Intelligence (AI) into digital platforms is not just an upgrade, it's a transformation. One of the key innovations being implemented in the media and entertainment data space is
AI search using RAG (Retrieval-Augmented Generation), a cutting-edge technology that's redefining how users find and interact with content.
What is RAG and How Does It Work?
Retrieval-Augmented Generation (RAG) is a hybrid AI approach that combines:
Information retrieval (searching a large database of documents or metadata),
With natural language generation (like how conversational AI responds).
In simple terms, RAG fetches the most relevant data from a trusted source and then uses a language model to present that data in a clear, human-like format. This ensures that responses are both accurate and contextually rich.
The Power of AI Search with RAG
Smarter Discovery: Unlike traditional keyword searches, RAG understands the context and intent behind a query, delivering precise and relevant results.
Speed & Efficiency: It reduces the time spent navigating complex datasets, ideal for professionals who rely on timely insights.
Scalability: Whether working with thousands or millions of entries, RAG scales seamlessly without compromising performance.
Continuous Learning: The system improves over time by adapting to user interactions and evolving data.
Advantages for Users
Enhanced Experience: Users can ask natural questions like "Show all Indian dramas released in 2023," and receive curated, accurate responses.
Informed Decisions: Researchers, content creators, and analysts gain quick access to relevant information, aiding in faster decision-making.
Reduced Manual Effort: No need to sift through pages of results—RAG handles the analysis and presentation.
How It Makes the Job Easier?
This implementation makes research and content navigation easier by:
Enabling natural language queries for complex searches.
Minimizing manual filtering and sorting of data.
Providing instant, actionable summaries, saving time and effort.
How to Use It?
Using RAG-enhanced AI search is simple:
Go to the AI-enabled search interface.
Type your query naturally like speaking to a colleague.
Instantly receive a concise, context-aware answer, with options to explore further.
