The demo works. Nobody can tell you what the product does.

What should an AI startup website include?

The five statements the product runs on — who uses it, what it receives, what sources it may use, what it returns, and what a person still decides — plus one honest example, the objections a buyer will raise anyway, and a next step that is a conversation rather than a signup.

What is the difference between an AI prototype and an MVP?

A prototype answers whether the behaviour is possible or useful and can live in a notebook. An MVP puts a bounded version in front of real users with enough product structure — accounts, history, permissions, review, fallback — to learn from them safely.

How do you turn an AI demo into a usable product?

By deciding what happens in the cases a demo never shows: no relevant source, an action with consequences, a provider that is slow or down, an output nobody checked. Building a working behaviour is the easy half now; making it deployable is the half that takes the time.

RAG vs chatbot vs AI agent — what is the difference?

RAG is how a system finds approved knowledge to answer from. A chatbot is one possible interface, and often the wrong one. An agent is permitted to take bounded steps using tools, which raises permission and approval questions retrieval does not. A product may use all three, one, or none.

AI agent or normal workflow automation?

If the rules are clear and the input is structured, deterministic automation is cheaper, faster, testable and predictable. AI earns its place where language is messy, inputs are unstructured, or interpretation genuinely helps. Choosing AI where a rule would do is the most expensive mistake in this category.

Does every AI product need RAG?

No. Retrieval matters when the product answers from a body of knowledge somebody owns. If the value is transforming, extracting or drafting from what the user already provides, RAG adds cost and latency without adding correctness.

How can an AI startup make its product easier to trust?

By making the controls part of the product rather than a paragraph beneath it: answers that show their source, a stated list of what the system may touch, a person who approves anything consequential, an explicit pause when there is no basis to answer, and a trace that explains a past output. No page can promise correctness, and one that does is the least trustworthy of all.

Should AI outputs require human approval?

Wherever the output leaves the company, costs money, or would be embarrassing to get wrong — which in early products is most places. Review is not a limitation to be engineered away; it is usually the thing that makes a buyer willing to deploy at all.

AI startup or SaaS startup — what is the difference?

In an AI startup the AI behaviour is what the buyer is purchasing, so the product decisions and the trust questions are about sources, bounds and review. In a SaaS startup the buyer purchases a software workflow and AI may be absent or incidental. A company can be both, but the questions it has to answer differ enough to be worth separating.

Custom AI product or off-the-shelf AI tooling?

Off-the-shelf until a specific constraint makes it wrong — the workflow is genuinely yours, the data cannot leave, the cost at volume stops working, or the behaviour is the product itself. If the tool is the product, buying it is not an option; if the tool merely supports the team, building it is usually a distraction.

When should a startup build instead of using a model provider?

When a named constraint forces it: cost at real volume, latency, data residency, or a behaviour no general model handles well. Before that, provider APIs are the cheaper path and the runway is better spent on the product. Providers are chosen around the behaviour, data, latency and controls a product needs.

Does an AI startup need a mobile app?

Usually not at the start. Most early AI products are used at a desk, and an install between the buyer and the product costs more than it earns. It becomes reasonable when the behaviour genuinely belongs on a phone.

When should an AI startup invest in SEO?

Once the product has a use case that can be named. Before then there is nothing specific to rank for, and generic category terms in a contested new category are an expensive place to learn. Afterwards, the use case, the comparison and the objection are the three things worth writing.

Website or product demo for an AI startup?

Both, doing different jobs. The website earns the demo by letting a stranger self-qualify without a call. The demo closes. A site that tries to replace the demo usually convinces nobody, and a vague site produces no demos to convert.

Can an existing prototype be turned into a production product?

Often, where the work is recoverable — the prompts, the evaluation examples and the logic usually carry forward even when the surrounding code does not. What rarely survives is anything that only ever worked on one laptop.

Can existing data and knowledge sources be connected?

It depends what they expose. Document stores, help centres and databases usually connect directly; some systems need an integration layer; a few are honestly a manual export. Worth establishing before a contract rather than after.

Who owns the product, code and data after a custom build?

The startup does — code, data, prompts, evaluations and accounts in its own name, on infrastructure it controls, with no dependency on Branditify to keep running.

What determines the scope of an AI MVP?

The behaviour being built, what it must see, who reviews its output, and what has to be true before real users touch it. Not a feature count — and adding permissions, billing or an audit trail that the use case does not need is the fastest way to make an MVP stop being minimal.