An AI research company with a deployment obsession.
BRAHMAI is building the full stack of enterprise AI: proprietary foundation models, memory infrastructure, and on-premise deployment — all designed for organizations where data sovereignty isn't optional.
We are a small, research-first team that ships. Our work spans model architecture, systems engineering, and the hardware integrations that make local AI actually viable at scale.
A complete stack. Nothing outsourced.
bodh & sens Series
A full model family — not just LLMs. Foundation models, embedding models, classification models, TTS, and research-stage architectures. Built for deployment constraints from day one, with quantization-aware training and distillation pipelines that preserve capability under aggressive compression.
MemoryOS
A neuroanatomically-inspired memory layer for AI agents. Persistent, structured, session-aware — enabling continuity without cloud dependency. Model-agnostic and built to run entirely on your own infrastructure.
Qualcomm / Snapdragon
Deep NPU integration for on-device and edge deployments. Enterprise-grade AI at the OEM layer — no server required.
On-Prem Deployment
End-to-end deployment pipelines for organizations running AI on their own servers. Competitive token economics. No vendor lock-in.
Infrastructure Revival
We take commodity and legacy hardware — machines written off as obsolete — and rebuild them into fully operational AI inference rigs. No specialized GPU clusters required. Just sovereignty, at a fraction of the cost.
ONYX
Local inference runtime with model and mode switching — between on-device inference and custom API endpoints. One interface, full control.
The bodh & sens series.
bodh (Sanskrit: enlightenment / awakening) — our family of foundation models built for real-world deployment constraints. Designed with quantization-aware training and distillation pipelines that preserve capability under aggressive compression.
bodh — b family| Model | Focus | Positioning |
|---|---|---|
| bodh-b0 | Content, Code, Assistance, Mathematics | A distilled model built for everyday excellence. Punches well above its size on generic tasks, with particular depth in content generation, coding, user assistance, and mathematical reasoning. |
| bodh-b1 | Agentic & General Purpose | Engineered for agentic workloads — planning, tool use, multi-step task execution. Equally capable as an all-rounder for enterprise deployments that need one model to do it all. |
| bodh-b2 | Agentic, Automation, Code, Physics, Mathematics | The heavy hitter. Designed for deep automation pipelines, complex instruction following, and technically demanding domains including code, physics, and advanced mathematics. Vision-capable. |
| Model | Focus | Positioning |
|---|---|---|
| bodh-x1 | General Purpose Agents | Built for the general public and consumer-facing deployments. Capable and efficient — well-suited for customer support agents, personal assistants, legal Q&A, and domain-specific agent applications. |
| bodh-x2 | General Purpose + Code Generation | Everything x1 does, extended with strong code generation capabilities. The go-to base for AI code agents, developer tools, and technical consumer applications. Vision-capable. |
| Model | Focus | Positioning |
|---|---|---|
| sens-mini-0 | Long-context memory | Built around Engram-style memory compression. Retains meaningful context at exceptional token depth. |
| sens-mini-1 | Personal AI | Single-owner model with real-time context compression. Designed for persistent, personalized intelligence. |
All models are available for on-premise licensing and enterprise deployment. We do not offer shared cloud API access to any model in our families.
Teaching AI systems how to remember.
Human memory is not a single thing. We remember differently depending on what we're remembering: a face, a fact, a feeling, a sequence of events. Different regions of the brain handle different kinds of memory, and they work together in ways that feel seamless.
Current AI systems don't work this way. Most models treat memory as a flat context window — a list of tokens that gets truncated when it gets too long. There is no structure, no persistence, no understanding of what matters. Every session starts from scratch.
MemoryOS is our research initiative to fix this. We are building a memory layer for AI agents that mirrors how biological memory actually works — structured, hierarchical, and selective. The goal is not just to make AI remember more, but to make it remember better: knowing what to hold onto, what to let go of, and how to surface the right context at the right moment.
The architecture draws from neuroscience — the distinct cognitive roles of attention, consolidation, spatial context, and working memory are each treated as first-class concerns, not afterthoughts. The result is a memory system that persists across sessions, scales to real enterprise context volumes, and runs entirely on your own infrastructure.
MemoryOS is model-agnostic and currently under active research development. It is integrated into our enterprise offering and forms the foundation of our sens model family.
Why enterprises are moving AI off the cloud.
- 01.Data sovereigntyYour data never leaves your infrastructure. Compliance requirements, competitive sensitivity, and regulatory obligations are all met by default.
- 02.Predictable costsNo per-token API bills that scale with usage. Fixed infrastructure costs with competitive on-prem token economics.
- 03.No vendor dependencyYou own the stack. Model updates, capability changes, and pricing are not at the discretion of a third-party API provider.
- 04.Air-gapped deploymentsFull functionality in network-restricted environments — government, defence, regulated finance, healthcare.
Active research directions.
BRAHMAI is currently focused on a small number of high-conviction research problems rather than broad surface coverage. Our active areas:
- —Model distillation & quantizationPreserving capability through aggressive compression. Validating that smaller, well-trained MoE models outperform larger poorly-quantized dense models.
- —Memory architecture for AI agentsHow to give AI systems durable, structured, fast-access memory that works across sessions and scales to enterprise context volumes.
- —Coreference & entity resolution at inference timeLightweight neural pipelines for accurate entity tracking in long-context, multi-turn AI interactions.
- —Edge AI on Snapdragon siliconDeploying capable models on Qualcomm NPU hardware. Making on-device AI a real enterprise option, not a demo.
Built for organizations that cannot compromise.
BRAHMAI works with enterprises, government bodies, and technology companies in India and abroad that require AI capabilities without cloud dependency. If your use case involves sensitive data, regulatory constraints, or a need for custom model behavior — we are probably the right conversation.
We are also in active conversation with OEM hardware partners for Snapdragon AI integrations and with investors aligned with the on-prem AI thesis.
