We build foundational AI technology — open standards for evidence integrity, grammar-first tokenizers for complex languages, and absence-detection architectures for safety-critical systems. Published on arXiv. Built from first principles.
AI generates convincing fake photos in seconds. EIF is an open standard that evaluates whether a photograph constitutes reliable evidence for a specific claim — not just "is this real?" but "is this trustworthy?"
C2PA tracks provenance — who created it, what device, what edits. Deepfake detectors classify real or fake. Neither evaluates whether a photo is reliable evidence for a specific claim.
EIF analyses the photograph itself: compression artifacts, noise patterns, lighting physics, and statistical signatures that separate real sensors from neural networks.
A standard for truth cannot itself be opaque. EIF is open.
Patent pending — EIF multi-dimensional metric framework for domain-specific evidence integrity evaluation.
BPE shatters agglutinative words into meaningless bytes. VerChol's grammar-first approach decomposes words at morpheme boundaries — preserving grammatical meaning for 500M+ speakers globally.
Existing tokenizers — SentencePiece, BPE, WordPiece — were designed for isolating languages like English. They systematically fail on languages where a single word carries clause-level meaning through grammatical suffixing.
VerChol's grammar-first approach achieves a 3.1% fertility improvement over BPE on agglutinative language benchmarks — by decomposing words into grammatically meaningful morphemes instead of statistically frequent byte-pairs. Published on arXiv.
BharatMini — Low-Cost Domain Training
Alongside the tokenizer, we demonstrated narrow-domain model training at ₹2,700 — proving domain-specific AI for manufacturing and robotics doesn't require massive compute.
Every AI safety framework detects what IS present. SenseAi inverts this: it detects what SHOULD be present but ISN'T. A fundamentally different computational problem.
The architecture uses a four-state processing model that classifies signal streams by the absence of expected patterns. In autism: the absence of expected physiological variability predicts crisis. In manufacturing: a missing sensor reading means failure.
Applications
Any program, in any language, that can read and write files can now use AI. No SDKs. No API keys. No cloud dependency.
Speech-to-text, audio classification, sound event detection. Write audio data to a file — get intelligence back.
/dev/ai/hearImage classification, object detection, document understanding. Any camera, any image, any format.
/dev/ai/seeSummarization, Q&A, translation, analysis. Local models by default. Cloud when you choose.
/dev/ai/thinkSchool dropout. 20+ years in technology. 5+ years in textiles and manufacturing. Worked across finance, trading, insurance, aviation, and ecommerce domains. Solo technical founder — all architecture designed and built personally. Published researcher on arXiv. 1 patent filed, 3 in preparation. Based in Bengaluru, India.
We build from first principles and publish our research. Open standards, open code, open papers.