Are AI demos deliberately misleading?

Mostly not. Demos are built to show potential, and teams naturally show what works. The risk is that buyers treat a demo as evidence of production quality. It isn’t — only measurement on your data is.

How many test questions are enough?

For a first proof of concept, 50–100 well-chosen cases usually reveal the main failure types. Mature systems grow the set continuously from real user questions.

What if a vendor refuses to use our data?

Data protection concerns are legitimate — solve them with an NDA, anonymised samples or a sandbox in your own cloud. A vendor that won’t test on realistic data in any form is asking you to buy on faith.

Guide · Buyer’s guide

9 tricks that make an AI demo look better than the product.

AI is the easiest technology in history to demo and one of the hardest to ship. A 20-minute demo can make almost any idea look solved. Most vendors aren’t lying on purpose — demos are optimistic by nature. But you’re the one who pays for the gap, so here is how to see it.

By Nythrex EngineeringUpdated 24 September 2026 4 min read

In the demoVSIn production

Question difficulty

Data messiness

Unexpected user behaviour

Volume & cost pressure

Integration complexity

Illustrative: the conditions a demo is tested under versus what a live system faces.

The 9 tricks

01Cherry-picked questions

The demo questions were tried dozens of times before the meeting. The ones that failed didn’t make the cut.

02Clean sample data

Ten tidy PDFs instead of your 40,000 files with scans, tables, duplicates and outdated versions.

03The document is already in the prompt

“It found the answer!” — because the relevant text was pasted into the context by hand. No real search happened.

04A human in the loop you can’t see

Some “AI” demos are partially operated by people, or show pre-recorded output. Ask to type your own input.

05No permissions

Everyone sees everything. Real systems must hide HR files from sales and customer A’s data from customer B.

06Unlimited budget

The biggest model, the longest context, several calls per question. Nobody mentions that it costs a lot per request at your volume.

07Happy path only

No typos, no ambiguous questions, no attempts to trick it, no questions whose answer isn’t in the data.

08Latency hidden by editing

Recorded demos cut the 25-second wait. Your users won’t.

09Integrations are slides

“And then it updates your CRM” — shown as an arrow on a diagram, not as working code against a real API.

Questions that expose a demo in five minutes

Ask these live, in the meeting

0/9

What a trustworthy demo looks like

Polished but uninformative

Scripted questions, no audience input
Vendor’s sample data
No numbers, only reactions (“impressive!”)
Failures never shown
No mention of cost or latency

Plain but useful

Your questions, typed live
A sample of your real data, messy parts included
A test set with accuracy, failure types and cost per request
Known failure modes shown with a plan to handle them
Latency and running cost stated up front

The better alternative: a paid proof of concept

If an AI project matters, a short, paid proof of concept on your data beats any number of demos. It should end with a written report: the test set, the results, the failure analysis, the cost per request and a clear recommendation. If the vendor can’t commit to measuring, that tells you what you need to know. See how we run one on our AI proof of concept page.

1
Agree on 50–100 real test cases
Written together with the people who will use the system, including awkward ones.
2
Define pass criteria up front
For example: correct answer and correct citation, response within a set time, cost per request below a set limit.
3
Run on a representative data slice
Including the ugly files. Especially the ugly files.
4
Review failures together
The failure analysis is the most valuable part: it shows whether problems are fixable or fundamental.

Frequently asked questions

GuideWhy AI pilots die before productionAI pilots stall on data, unclear value, cost and trust — rarely on the model. Seven failure patterns and the fixes that get AI into daily use.Guide40 questions to ask an AI vendorDue-diligence checklist for hiring an AI development company: approach, data, quality, security, cost, ownership and team — and what good answers sound like.ServiceAI proof of conceptA time-boxed AI proof of concept on your real data: test set, thin end-to-end slice and a go / no-go backed by accuracy, latency and cost numbers.GuideLLM evals for non-ML teamsHow to evaluate an LLM feature without a data-science team: build a test set, choose metrics, use LLM-as-judge carefully, run evals in CI and read the results.Guide13 red flags your vendor is hiding problemsVague reports, shifting dates, no access to code: 13 signs your outsourced project is in trouble — and what to ask or do about each this week.ServiceAI developmentAI systems that reach production: RAG assistants, AI agents, document AI and LLM features — in your cloud, with evals, guardrails and full ownership.

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