Edge Intelligence · Advanced AI Research · Born 2026
Aarvihu AI Research Labs
Tiny Models. Immense Intelligence. Zero Cloud.
Tiny Models. Immense Intelligence. Zero Cloud.
Aarvihu AI Research Labs is a mathematics-first AI research organisation. We design edge-native models — compact, provably efficient, and sovereign. Our belief is simple: the most powerful AI is the one that works entirely on your device, even when the internet doesn't.
Every model we build starts with a theorem, not a trend. We derive efficiency from information-theoretic bounds, Riemannian geometry, and spectral analysis — then engineer from first principles.
We don't shrink large models and call them edge-ready. We architect sub-1B parameter models from scratch in fp32, bf16, and fp16 — designed for embedded chips and edge hardware with zero precision sacrifice.
Your data never leaves. No telemetry, no API calls home, no subscription to think. On-device AI means you own your intelligence — completely, permanently, unconditionally.
We pursue deep, fundamental research across AI's hardest open problems — with a relentless focus on making results deployable at the edge, not just publishable in a journal.
Designing sub-1B transformer architectures in fp32, bf16, and fp16 — built small from the ground up, not shrunk from above. Full-precision reasoning that fits where no model has fit before.
We design models that are small by construction — not by squeezing a large one. Custom attention mechanisms, parameter-efficient layer design, and architectural sparsity from the ground up.
Transforming cameras into intelligent sensors through compact, high-performance vision AI. Designed to detect, interpret, and understand the physical world in real time—running entirely on edge hardware with privacy and efficiency built in.
Researching end-to-end speech architectures for low-resource and morphologically rich languages — where frontier labs stop and real research begins.
Optimisation on curved manifolds: geodesic gradient descent, hyperbolic embeddings, and Lie group symmetries that unlock model efficiency inaccessible to flat Euclidean methods.
Formalising model alignment through mutual information bounds, rate-distortion theory, and PAC-Bayes generalisation guarantees — so safety is proven, not assumed.
Most AI labs tune hyperparameters and call it research. We write proofs. Every architectural decision at Aarvihu is grounded in information theory, differential geometry, convex optimisation, or measure-theoretic probability — because intuition ships bugs and mathematics ships guarantees.
Four engineering pillars that convert mathematical research into deployable, sovereign intelligence.
Spectral pruning, structured sparsity, and knowledge distillation — shrinking without losing any of the intelligence that makes a model useful.
Ultra-low latency AI on microcontrollers and embedded CPUs. No GPU, no cloud, no wait — just instant intelligence exactly where you need it.
Targeted corpus curation and domain-specific training on mathematically compressed base models — purpose-built for a single vertical, not generalised for the world.
Air-gapped deployment, zero telemetry, and on-device inference that mathematically guarantees data sovereignty. Your intelligence stays yours.
We build razor-sharp micro-LLMs for individual verticals — trained on domain-curated corpora, mathematically compressed, and small enough to run entirely on the device they serve.
The future belongs to models that know exactly what they're built for. Deep domain understanding, instant reasoning, and expertise refined for real-world decisions. Not bigger. Smarter. That's Aarvihu.
OBD-II fault reasoning, predictive maintenance, and repair guidance — running entirely on-board. No connectivity needed, no data sent home.
Clinical note summarisation, drug interaction checks, and differential diagnosis — HIPAA-compliant, on-device, with zero PHI leaving the hospital premises.
Contract clause extraction, regulatory scoring, and legal citation retrieval — fully air-gapped, provably precise, running on a standard laptop without internet.
Fraud pattern detection, KYC document extraction, and loan reasoning — offline-capable models that operate inside a bank's firewall with no cloud dependency.
Predictive maintenance, quality anomaly reasoning, and safety protocol Q&A — running directly on PLCs and edge controllers on the factory floor.
Crop advisory in local languages, soil health analysis, and adaptive tutoring — working fully offline in zero-connectivity rural regions.
We are building the mathematical foundation for a future where AI runs at the edge, not inside distant server farms controlled by a handful of organisations. Where privacy is guaranteed by design, not promised by policy. Where efficiency is not a compromise, but a breakthrough. Where "small" doesn't mean less capable — it means fundamentally optimal. In this world, powerful AI is not concentrated in a few data centres or privileged ecosystems. It is personal, sovereign, and universally accessible — carried in every pocket, embedded in every tool, and available to anyone, anywhere.
We are mathematicians, systems engineers, and domain experts who believe the next decade of AI will be won at the edge — not in the cloud.
Pioneers novel mathematical frameworks and machine learning architectures designed to surpass conventional models with significantly lower computational cost, memory footprint, and energy consumption while maintaining state-of-the-art performance.
Oversees cloud-native AI deployment pipelines — ensuring models are securely deployed, scaled, monitored, and optimized across production environments with high reliability and performance.
Designs domain-specific training pipelines and benchmark protocols that ensure our models are measurably optimal — not just small.
Most transformative labs are invisible before they're inevitable. If you're a researcher, investor, or builder who thinks AI belongs at the edge — not in a hyperscaler's cloud — this is your signal to reach out.