AI Answer Library

The questions people actually ask about enterprise AI

One question per page, answered in the first paragraph. Every entry carries a comparison table, the limits of where it applies, a byline and a review date — easy to fact-check, easy to cite.

Should an enterprise knowledge base use RAG or a fine-tuned LLM?

For most enterprise knowledge bases, start with RAG and do not fine-tune first. RAG controls wha…

What is the difference between an AI agent and RPA, and which should a company choose?

The core difference: RPA executes the steps you hard-coded, while an AI agent decides its own ne…

Self-hosted LLM or public API — how do I choose?

Answer the data-residency question first, then do the math. If the data is bound by compliance —…

How do I choose an enterprise AI solution provider?

Focus on three things: what they have actually shipped, who owns the data, and whether you can m…

How much does it cost for a company to self-host a large language model?

The total cost of a self-hosted LLM breaks into three parts — hardware, implementation and ongoi…

What server specs do you need to self-host a large language model?

Size everything backwards from VRAM; the rest is secondary. The rule of thumb is: weight memory …

How accurate can an enterprise RAG knowledge base actually be?

The honest answer: an accuracy number without an evaluation set behind it means nothing. "Accura…

Should a company buy software outright or subscribe to SaaS?

Ask first whether the data may leave your network; only then start comparing prices. If regulati…

How long does it take to deploy a self-hosted large language model?

From contract to real usage, four to twelve weeks is typical, and the spread comes down to wheth…

Our data cannot leave the internal network — can we still use large language models?

Yes — this is exactly what self-hosting exists for. Download open weights into your network, run…

How much data do you need to fine-tune a domain-specific LLM?

It depends on the method, and the answers span three orders of magnitude. A lightweight fine-tun…

For running LLMs, is it cheaper to buy GPUs or rent them?

For most companies, renting is the right answer. Buying is clearly better in only two situations…

How should a company phase its first AI project?

Four steps: pick one narrowly scoped use case, build an evaluation set, ship a minimum usable ve…

Which vector database should an enterprise pick for RAG?

Start from your data volume and the stack you already run, not from whichever database markets i…

Should an enterprise use open-weight models or a closed API?

You do not have to pick one for the whole company — decide per workload, by data sensitivity and…

Can a knowledge base handle scanned documents and complex tables?

Yes, but only if you handle each document type separately — dumping everything into one pipeline…

Do I need a framework like LangChain to build an AI agent?

Most enterprise use cases do not. A model SDK with tool calling plus a few hundred lines of your…

What can multimodal LLMs actually do inside a company?

The workloads that land today all share one shape: turning information inside an image into stru…

Why do so many enterprise AI projects fail?

Most enterprise AI projects do not fail because the model was too weak; they fail before any cod…

What Chinese compliance requirements apply when a company uses large language models?

First determine whether you are using AI internally or offering a generative AI service to the p…

How do you estimate the token cost of using a large language model?

The formula is: cost per call = (input tokens × input price) + (output tokens × output price), m…

How much headcount does an AI customer service system actually save?

The honest answer is that there is no universal savings percentage — it depends on how repetitiv…

What state does your data need to be in before starting an AI project?

The minimum is three properties: findable, extractable, and attributable. Findable means the doc…