Know Our Founders.

Gopal is building the reliability layer for LLM infrastructure. Nearly a decade in AI/ML, including teaching machine learning to master's students at 22.

He started Metriqual on a simple observation: existing gateways route requests, but they lose the conversation the moment a provider fails. Metriqual solves stateful failover, carrying context across providers so applications stay coherent when things break.

The work is backed by published research: the ContinuityBench and FailureAtlas benchmarks, and papers on attention efficiency and inference latency.

Vishal is a research engineer working at the intersection of reinforcement learning, world models, and efficient deep learning, building learning systems that are efficient, scalable, and interpretable across spiking neural architectures, linear attention mechanisms, and representation geometry in transformers.

His published research spans Variational Linear Attention, a Spiking Decision Transformer, and AgroSense, an agricultural sensing system, alongside work on interpretable healthcare AI. He has also contributed to Meta AI's open-source optimizer research, implementing Muon Grafting for the Distributed Shampoo optimizer.

He has collaborated with NIT Agartala, NIT Mizoram, and C-DAC on projects spanning precision agriculture, healthcare AI, and computer vision for government signature-verification systems, and spent over a year in enterprise engineering maintaining production systems under live deployment constraints. He holds a bachelor's degree from Stanford, and is headed to Yeungnam University in South Korea as a Global Korea Scholarship recipient, pursuing an M.S. in Robotics & AI Convergence.