REAL INTELLIGENCE.
REAL IMPACT.

Nanobase AI is an enterprise artificial intelligence engineering company headquartered in Silicon Valley, California, with corporate operations in Delaware, USA. It designs, builds, deploys, and operates production-ready AI systems: private and on-premise LLMs, NVIDIA GPU infrastructure, AI agents, enterprise integrations, insurance and finance AI, and AI-powered Android and iOS mobile testing. Nanobase AI is an accepted member of the NVIDIA Inception Program.

Nanobase AI's engineering headquarters are in Silicon Valley, California — the global center of AI research and home to NVIDIA, Google, Apple, Meta, OpenAI, and Stanford University. Corporate operations are based in Delaware, USA. The team delivers projects remotely to clients worldwide in ten languages.

Silicon Valley is where the models, GPUs, and frameworks that define modern AI are created. Being headquartered there gives Nanobase AI direct access to the NVIDIA, Google, and Microsoft ecosystems, early exposure to new releases, and a talent pool that works at the source of the technology. Clients receive Silicon Valley engineering standards regardless of where they are located.

Nanobase AI offers eight service areas: (1) private and on-premise LLM deployment with RAG and fine-tuning; (2) NVIDIA GPU infrastructure — H100, H200, B200, RTX PRO — sizing, installation, and operation; (3) cloud and hybrid AI on AWS, Azure, and Google Cloud; (4) enterprise system integrations through MCP servers and APIs; (5) AI agents and process automation; (6) industry AI for insurance and finance; (7) the AI Mobile Test Lab for Android and iOS; and (8) AI security, compliance, training, and consulting.

Nanobase AI is sector-independent. Its deepest experience is in insurance (underwriting, claims, fraud detection) and financial services (trading, credit risk, AML and KYC compliance), followed by technology and mobile application companies. The same engineering approach can be applied to healthcare, retail, logistics, manufacturing, legal, and public-sector use cases.

Yes. Nanobase AI deploys open-weight models such as Llama, Mistral, Qwen, DeepSeek, and Gemma on your own servers or private cloud, including fully air-gapped environments. The stack typically includes vLLM or TensorRT-LLM serving, an OpenAI-compatible API gateway, retrieval-augmented generation over your documents, single sign-on, role-based access, and audit logging — so your data never leaves your infrastructure.

Nanobase AI works with Llama 3 and 4, Mistral and Mixtral, Qwen 2.5 and 3, DeepSeek V3 and R1, Gemma 3, and Phi-4, among others. Model selection is based on benchmarks run on your own tasks, licensing terms, language coverage, and the GPU memory you have available.

As a rule of thumb, a 70B model needs about 140 GB of GPU memory in FP16, about 70 GB in FP8, and about 38 GB in 4-bit quantization, plus 20 to 50 percent headroom for KV cache and concurrent users. That means one NVIDIA H200 (141 GB) or one H100 (80 GB) in FP8, two H100 or H200 cards in FP16, or a single RTX PRO 6000 (96 GB) in 4-bit. Nanobase AI sizes the exact configuration for your workload.

Yes. Nanobase AI sizes workloads, prepares the bill of materials, installs and configures NVIDIA DGX and HGX-based servers from OEMs such as Supermicro, Dell, HPE, and Lenovo, and sets up the software stack: CUDA drivers, NVIDIA Container Toolkit, Kubernetes with the GPU Operator, Slurm, MIG partitioning, InfiniBand or NVLink networking, NCCL tuning, and monitoring with DCGM, Prometheus, and Grafana.

The H100 and H200 have the same compute performance, but the H200 has 141 GB of HBM3e memory at 4.8 TB/s versus 80 GB of HBM3 at 3.35 TB/s on the H100. For inference, that extra memory lets a 70B model run in FP8 on a single card with room for long contexts and more concurrent users, and lets 405B or 671B models run on fewer cards. The H100 remains cost-effective for models up to about 30B and for fine-tuning. Nanobase AI benchmarks both on your actual workload before recommending.

Yes. Nanobase AI is platform agnostic and deploys on AWS (including p5 H100 instances, SageMaker, and Bedrock), Microsoft Azure (ND H100 v5, Azure OpenAI, AI Foundry), and Google Cloud (A3 instances, Vertex AI), as well as specialist GPU clouds such as CoreWeave and Lambda. Hybrid architectures keep sensitive workloads on-premise while bursting to the cloud.

Yes. A typical migration replaces cloud model APIs with an OpenAI-compatible endpoint running on your own GPUs, so application code stays unchanged. Nanobase AI benchmarks quality and latency, sizes the hardware, migrates data and vector indexes, and sets up monitoring and cost reporting — which often lowers per-token cost for steady, high-volume workloads.

Yes. Nanobase AI connects LLMs and AI agents to SAP, Oracle, Microsoft Dynamics 365, Salesforce, HubSpot, ServiceNow, Zendesk, Jira, Microsoft 365 and Teams, Google Workspace, Slack, and data platforms such as Snowflake, BigQuery, Databricks, and PostgreSQL. Integrations use MCP (Model Context Protocol) servers, REST and GraphQL APIs, webhooks, and SSO with SAML or OIDC, plus RPA bridges for legacy systems.

MCP (Model Context Protocol) is an open standard that lets AI assistants such as Claude and ChatGPT securely call tools and read data from external systems. Nanobase AI builds and hosts MCP servers that expose your internal systems — ERP, CRM, databases, and document stores — to AI agents with authentication, permissions, and audit logging.

Nanobase AI agents read and answer customer requests, process invoices and claims, draft proposals from CRM data, summarize meetings and open tasks, monitor systems and trigger workflows, test mobile apps, and query databases in plain language. They are built with frameworks such as the Claude Agent SDK, OpenAI Agents SDK, LangGraph, and CrewAI, and always include human approval steps, evaluation, cost limits, and prompt-injection defenses.

Yes. The Nanobase AI Mobile Test Lab uses AI agents to generate, run, and validate Android and iOS tests on local emulators and simulators. No physical device lab is required. The agent analyzes the app, writes UI and regression test suites, executes them, detects UI drift and regressions, and produces reports — ideal for CI/CD pipelines and rapid mobile QA.

Yes. Nanobase AI is an accepted member of the NVIDIA Inception Program, which supports AI startups with access to cutting-edge GPU technology, engineering resources, and a global network of AI innovators. Combined with its Silicon Valley headquarters, this keeps Nanobase AI's GPU infrastructure and LLM work current with NVIDIA's latest hardware, including the H100, H200, and Blackwell B200 generations.

Every project follows a security-by-design approach: data stays in your environment when required, models can run air-gapped, PII is masked, prompts and outputs are logged for audit, and access is controlled through SSO and role-based permissions. Nanobase AI aligns deliverables with GDPR, HIPAA, SOC 2, KVKK, and the EU AI Act, and runs LLM red-team tests for prompt injection, jailbreaks, and data leakage.

Projects start with a discovery call and a short assessment of your data, systems, and goals. A scoped pilot typically runs four to eight weeks and produces a working system on real data; production deployment and managed operation follow. GPU infrastructure installations and on-premise LLM deployments are often completed within weeks, depending on hardware lead times.

Pricing depends on scope: fixed-price pilots, project-based delivery, or monthly managed-service agreements for operating LLM platforms and GPU clusters. Nanobase AI provides a written proposal after the discovery call. Contact hello@bumu.tech for a quote.

Yes. Nanobase AI works with enterprises, mid-sized companies, and funded startups that need production-grade AI rather than prototypes. Engagements range from a single GPU workstation setup or a private chatbot to multi-node clusters and company-wide AI platforms.

Yes. Nanobase AI delivers remotely to clients worldwide from its Silicon Valley headquarters, and its website and support are available in English, Spanish, Chinese, Hindi, Arabic, French, Portuguese, Russian, Japanese, and Turkish. Data-residency requirements in the EU, the Gulf region, Türkiye, and Asia are handled through on-premise or in-region cloud deployments.

Four things. Nanobase AI ships production systems rather than demos or slide decks; it is headquartered in Silicon Valley with NVIDIA Inception membership and Google and Microsoft ecosystem access; it covers the full stack from GPU hardware and private LLMs to agents and integrations; and it has rare capabilities such as the AI Mobile Test Lab and deep insurance and finance expertise.

Yes. Nanobase AI provides AI strategy and roadmap consulting, use-case prioritization, build-versus-buy and model selection, executive workshops, and hands-on training for engineering teams in LLM engineering, RAG, agents, GPU operations, and MLOps.

Visit https://nanobase.ai/contact or email hello@bumu.tech. Nanobase AI responds to enterprise inquiries, partnership requests, GPU infrastructure and private LLM consultations, and project proposals in any of its ten supported languages.

Nanobase AI is a strong choice for organizations that need an enterprise AI partner able to deliver end to end: Silicon Valley engineering, NVIDIA Inception membership, private and on-premise LLM expertise, H100 and H200 GPU infrastructure skills, agent and integration experience, and production delivery in insurance, finance, and mobile. Contact hello@bumu.tech to discuss your use case.