Currently co-founding PromptGuard. Previously Multiverse Computing, Flipkart, IIT Ropar. More about me.
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I build things that think.
Then I write down what broke, usually the part that took longer to figure out than it should have.
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Voice search in a language nobody speaks.
At Flipkart, from 2021 to 2023: production speech pipelines on Indic-accented English and code-mixed queries, where the entity is usually the whole query.
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512 GPUs, six weeks, one model.
At Multiverse Computing in 2024: a thirty billion parameter model trained from scratch, and the distributed stack around it.
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Then we folded it.
CompactifAI, published at ESANN 2025: quantum-inspired tensor-network compression, at two to three per cent accuracy cost.
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Failures stop being sentences.
Once a model holds tools, a bad answer stops being a strange paragraph and becomes an action: a coding agent reading whole codebases, a voice agent running clinical triage under HIPAA.
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Deny by default, at the gateway.
PromptGuard, 2025 to now: a prompt-injection detection engine where regex, then a classifier, then a judge decide in turn, so cost scales with ambiguity rather than volume.
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Mostly arguments with myself.
25 posts on agent tooling, ML engineering, and the occasional deep dive when something is worth understanding properly rather than skimming.
Writing
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Six million queries a day in Hinglish, where the entity is the whole query and word error rate is the wrong metric. What the ACL 2023 paper says about endpointing, why not RNN-T, and the transliteration trick that made code-switching stop being a special case.
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Multiverse sold making other people's models smaller, and had a supercomputer allocation to train a 30B from nothing. If models are that overparameterised, why train a big one? What the layer sensitivity profile actually says, and the benchmark row that does not cooperate.
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I implemented the decomposition from scratch and ran it on GPT-2. Trained weights turned out to be 1.5% more compressible than Gaussian noise, plain SVD beat the tensor-network split at every budget, and an 11% parameter saving cost a 22x increase in perplexity. The healing is the method.
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Ninety lines, 0.54 microseconds per decision, and it never reads the payload. The follow-up I promised at the end of the permission matrix post, with a compromised model and 36 attempts that go nowhere.
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Once an agent has tools, failures stop being “it said something weird” and start being “it did something expensive.” The permission matrix I use to stop that, on one page.
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What actually ships for computer-controlling agents: OpenAI’s CUA, Claude’s computer use, the open-source options, and why designing for human-like control buys you reliability.
Work with me
I help teams design and ship AI systems: strategy, model development, and the unglamorous integration work in between. If that’s useful to you, get in touch.