To every engineer, AI researcher, and product person on this team - every spec is yours. Now we scale it.

Plug-and-play across BSPs, CRMs, and native apps. 100+ enterprises already running on it. Bajaj Finance, HDFC Bank, Cipla, Hero, Zomato. 117% net revenue retention. The product works.

Our proprietary foundational AI video model: Trained on 1,000+ hours of proprietary data Real-time delivery, up to 5 lakh videos per minute 175+ languages 20x lower cost than global peers Quality indistinguishable from reality We didn't buy this. We built it from scratch.

Proud to be part of this. When @nimishgl1 and I started TrueFan 5 years ago, the tech to do this at scale didn't exist. So we built it. A thread on what that actually means 👇

Thank you Sandeep!! We're building Enterprise AI video from India for the world.

Thank you Sandeep!! Global ambition has always been the plan enterprise AI video is a category we're building from India for the world. 😀

We just launched our first realtime avatar experience at TrueFan AI. See the product in the comments. 🎙️ The goal is simple: make interacting with AI feel more natural, human, and engaging. Building this was a coordination challenge as much as an intelligence one. Realtime avatars require multiple systems working in sync, all at once: 🗣️ Understanding spoken language in real time 💡 Generating context-aware responses 🔊 Converting responses into natural-sounding speech 🎭 Syncing voice with a visually coherent avatar The tricky part? Keeping all of this in harmony while handling: -> Continuous input -> Evolving context -> Real-time responses This is still v1 and we're improving fast. If you're exploring use cases in customer experience, kiosks, sales, or interactive interfaces, I'd love for you to try it and share your feedback. 🙏 #AI #ConversationalAI #AvatarTech #TrueFanAI #ProductLaunch

We thought generating 1,000 AI videos per day was already a big achievement. Then the Zomato team asked us a question that broke our entire infrastructure. "Can you generate 500,000 personalized videos in one day?" At that moment we realized something important. Our AI models were not the bottleneck. Our infrastructure was. As we tried to scale, multiple problems started appearing: ⚠️ GPU machines were heavily underutilized ⚠️ Video rendering pipelines were running sequentially ⚠️ Storage I/O started becoming a major bottleneck ⚠️ Autoscaling systems could not handle sudden traffic spikes It became clear that the system was not designed for this level of scale. So we redesigned the architecture from scratch. ⚙️ Key changes we made: • Converted video generation into fully asynchronous pipelines • Built a GPU task queue system for distributed processing • Introduced batch inference for rendering workloads • Optimized storage pipelines for high-throughput I/O We also tuned the inference stack to run efficiently across T4, L4, A100 and H100 GPUs, allowing us to dynamically choose instance types based on availability and cost efficiency. 🚀 The result: 1,000 videos/day → 500,000 videos/day capacity At peak throughput, the system can generate 2,500 videos per minute. 💡 The biggest lesson for me as a technical founder: Many AI companies think their moat is model accuracy alone. But in real production systems, the real moat is: Model accuracy + strong engineering infrastructure. That architecture became the foundation of our platform, and we’ve continued evolving it as we scale further. 💬 Question for other engineering leaders: What has been the hardest scaling problem you have faced in production systems?

Education should speak every learner’s language

Nicely explained!!

Thank you

We feel incredibly proud to have helped make more than lakhs of Mothers extra happier on this Mother's Day. This opportunity meant a lot to us, and We are genuinely thankful 🙏 @deepigoyal @TrueFan_In @zomato