AI questions, answered plainly.

Generative AI is software that produces new text, images, audio or code in response to an instruction. Tools such as ChatGPT, Claude and Gemini are generative AI. They are built on large language models trained on vast amounts of text, which lets them draft, summarize, translate, analyze and answer questions in everyday language.

A large language model is the engine inside tools like ChatGPT. It is a program trained on a huge body of text to predict what comes next, which turns out to be enough to write, summarize, translate and reason about language. GPT, Claude, Gemini, Llama and Qwen are all large language models.

Local AI is an AI model that runs on your own computer or server instead of on a provider’s cloud. Nothing you type is sent outside your network, it keeps working without internet, and there are no per-message fees. The trade-off is that you need suitable hardware and someone to maintain it.

Agentic AI is AI that carries out multi-step tasks on its own instead of only answering questions. An agent can read an email, look up a record, update a spreadsheet, draft a reply and schedule a follow-up, asking a person for approval where it matters.

RAG stands for retrieval-augmented generation. It is a method that lets an AI answer from a specific set of documents: the system first retrieves the relevant passages from your files, then writes an answer from them and cites the source. It is how a company knowledge assistant works.

AI is the broad field of making computers do things that normally need human intelligence. Machine learning is the main technique within it: systems that learn patterns from data. Generative AI is a kind of machine learning that creates new content, such as text or images, instead of only classifying or predicting.

Bit Microsystems provides AI services in Kathmandu from its office in New Baneshwor. Its services include local AI setup on a client’s own hardware, agentic automation, AI chatbots for websites, WhatsApp and Viber, document AI, company knowledge assistants, custom AI development, AI consulting, team training and AI search optimization.

Yes, with limits. Current AI models read and write Nepali in Devanagari and also handle Romanized Nepali. Quality is good for everyday content but below English, and weaker for specialized legal or technical language and for speech. Anything important should be checked by a fluent speaker.

Yes. These tools are used widely by businesses in Nepal. The practical hurdles are paying for international subscriptions, deciding which plan suits each role, and making sure confidential data is not pasted into personal accounts. A company plan and a short usage policy solve most of this.

Yes. An AI chatbot can answer on WhatsApp, Viber, Facebook Messenger, Instagram and a website at the same time. WhatsApp and Viber both require an approved business account and charge their own messaging fees, which should be included when estimating monthly cost.

Businesses with many repetitive inquiries or a lot of paperwork benefit most: education consultancies, travel and trekking agencies, hotels, clinics, retailers, accounting and law firms, banks and cooperatives, and NGOs. The common thread is repeated reading, writing and data entry.

General laws apply to AI use in Nepal, in particular the Individual Privacy Act, 2075 (2018), which covers how personal information is collected and shared, along with rules from sector regulators. Nepal has also been developing national AI policy. Organizations should set their own AI usage policy and take legal advice on their specific obligations.

For a small business the realistic starting point is a few subscriptions to tools such as ChatGPT, Claude or Gemini, plus training. That costs far less than custom development. Custom systems such as chatbots or automations add a setup cost and a monthly running cost that grows with usage. A good supplier will estimate both before you commit.

Four things: how many systems it must connect to, how much data needs cleaning first, how accurate it must be, and whether it runs in the cloud or on your own hardware. Running costs then depend on usage. The model itself is rarely the expensive part; integration and testing are.

Start by training staff on a general AI assistant, because that pays back fastest. Then pick one task that is frequent, time-consuming, already done on a computer and easy to check, and automate only that. Expand once it works.

It depends on the scope. Staff training and a website chatbot answering from existing content are the quickest. Automations that connect several systems, and anything needing a business messaging account approval, take longer. A reliable supplier gives a written timeline after seeing your systems, not before.

Usually not. Most organizations need people who can use AI well, not people who can build it. Training your existing staff and using a managed service for the technical side is more economical than hiring specialists.

Buy when an existing product does the job, which is more often than people expect. Build when your process, data or language is specific enough that no product fits, or when AI is part of your own product. A consultant without a product to sell can tell you which applies.

It depends on the account and the data. Personal accounts may use conversations to improve the provider’s models unless that is switched off, while business plans generally commit not to train on your data by default. In both cases the data leaves your organization, so truly confidential material belongs in a private, local AI.

Cloud AI, such as ChatGPT, runs on the provider’s servers and is the most capable and easiest to start with. Local AI runs on your own hardware, keeps all data in your building and works offline, but needs an upfront hardware purchase and maintenance. Many organizations use both.

Yes. AI models can state incorrect information confidently, which is often called hallucination. The risk is reduced by having the AI answer from your own documents with sources shown, by testing it against known answers, and by keeping a person in the loop for anything that matters.

Yes, even a small one. A short policy that names the approved tools, lists what must never be entered into them, and requires output to be checked prevents most problems. It is easier to set before an incident than after.

In most businesses AI changes jobs more than it removes them. It takes over repetitive reading, writing and data entry, and people move to judgment, relationships and exceptions. The organizations that gain most are those that train their staff to work with it.

Generative engine optimization is the practice of making a business easy for AI assistants such as ChatGPT, Gemini, Perplexity and Google’s AI Overviews to find, understand and recommend. It relies on clear factual content, structured data, and consistent information about the business across the web.

Answer engine optimization means structuring content so that it is quoted as the direct answer to a question, in a search engine’s answer box, a voice assistant or an AI chatbot. In practice it means opening with a clear answer, using question-based headings and adding FAQ structured data.

No one can guarantee it, but you can improve your chances. Publish specific, factual pages about what you do and where, keep your name, address and services consistent everywhere online, add structured data, earn genuine reviews and mentions on other sites, and make sure AI crawlers are not blocked from your website.

Yes. AI assistants that search the web rely on the same signals as search engines, so a site that is fast, crawlable and well-structured is more likely to be cited. GEO and AEO extend SEO; they do not replace it.