Insights
Guides and comparisons on private LLMs, GPU infrastructure, AI agents, integrations, compliance and mobile testing, written by Nanobase AI engineers in Silicon Valley.
AI in insurance: how to automate underwriting and claims safely
AI in insurance pays off first in document intake, claims triage and fraud screening. A practical guide to underwriting and claims automation and governance.
How to choose an AI meeting assistant for an enterprise
An enterprise AI meeting assistant is judged on language coverage, data residency, consent, access control and real-meeting accuracy, not a feature list.
AI mobile app testing without physical devices: how does it work?
AI test agents generate and run Android and iOS UI tests on local emulators and simulators, so most mobile app testing no longer needs physical devices.
Best open-weight LLMs for enterprise in 2026: which to deploy?
Best open-weight LLMs for enterprise in 2026: Llama, Qwen 3, DeepSeek V3/R1, Mistral, Gemma 3 and Phi-4 compared by license, languages, context and GPU needs.
EU AI Act, GDPR and KVKK compliant LLM deployment: the checklist
EU AI Act, GDPR and KVKK compliant LLM deployment checklist: risk tiers and dates, deployer duties, DPIA, VERBİS, in-region hosting, PII masking and audit logs.
H100 vs H200 vs B200: which GPU for LLM inference, and when?
H100 vs H200 vs B200 for LLM inference: H100 up to 70B in FP8, H200 when 405B or DeepSeek R1 must fit one node, B200 for FP4 and rack-scale throughput.
How many GPUs do you need for 70B, 405B and DeepSeek R1?
How many GPUs for LLM inference: 70B needs 2 H100 or 1 H200 in FP8, 405B needs 8 H100, DeepSeek R1 needs 8 H200. Sizing table, KV-cache math, worked example.
Kubernetes GPU Operator vs Slurm: which should run your GPU cluster?
Kubernetes GPU Operator vs Slurm: Slurm for multi-node training and batch HPC, Kubernetes for inference and services, often both. Table and checklist inside.
Turning meeting notes into action items people actually complete
Meeting notes turn into finished work when each item names one owner, one date, and lands in the tracker that owner already checks, not a shared document.
Meeting recordings and privacy law: what enterprises must get right
Meeting recording compliance: consent rules by jurisdiction, GDPR and KVKK obligations, HIPAA scope, data residency, access control, retention and audit trails.
Running one meeting for a team that speaks ten languages
Fix multilingual meetings across time zones: one working language live, a written record in every language after, and async input treated as a real vote.
How to deploy an LLM on-premise in 2026: architecture, stack, cost
On-premise LLM deployment in 2026: open-weight models on your own NVIDIA GPUs, served by vLLM, TensorRT-LLM or NIM behind an OpenAI-compatible gateway and RAG.
Your own GPUs vs cloud APIs: how to calculate cost per token
Self-hosted LLM cost per token = fully loaded cost per GPU-hour / measured tokens per GPU-hour. Full cost model, worked example and break-even vs cloud APIs.
RAG vs fine-tuning: a decision guide for enterprise LLMs
RAG vs fine-tuning: use RAG when answers must cite current documents and respect access rights; fine-tune for stable style, format and domain behavior.
vLLM vs TensorRT-LLM vs Ollama vs SGLang: which should you run?
vLLM is the default enterprise LLM serving engine; TensorRT-LLM gives peak NVIDIA throughput, SGLang wins on prefix caching and JSON output, Ollama is for dev.
What is MCP and how do you build an MCP server for enterprise systems?
MCP (Model Context Protocol) is an open standard that lets Claude, ChatGPT and internal agents call enterprise systems through one MCP server. How to build one.
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