A solicitors’ office in Manchester spent £12,000 on a site rebuild with an AI assistant baked into the homepage. Six months later, that assistant had handled four conversations. Two of them were staff testing it.
That’s the usual story, and the technology isn’t the problem. “Build me an artificial intelligence website” is a vague brief, and vague briefs turn into expensive toys. What works is narrower: pick one task, build the smallest thing that does it properly, and judge the result on numbers rather than vibes.
Here’s the six-step version small teams keep landing on, with real costs and the places it usually goes wrong.
Step 1: Find the one job worth automating
Write the job down as a single sentence phrased around a visitor outcome. Not “improve customer service” but “read three photos of a garden and return a ballpark quote in under two minutes.”
Three examples that actually shipped:
- A landscaping firm in Leeds replaced a 14-field quote form with a photo upload and three short questions. Quote requests went from 1.9% of visitors to 4.6%, and the office saved about nine hours a week on triage.
- A two-partner law firm lets visitors describe their problem in plain English. The model sorts it into one of six matter types and books the right 30-minute slot. No legal advice, no document drafting.
- A bike parts shop uses a model to read returns emails and pre-fill the form: order number, item, reason code. Tickets that took four minutes now take 40 seconds.
None of those is a chatbot. Each one removes a single piece of friction, and in each case the AI is close to invisible.
To pick your own, score candidates on three things: how often the task happens, how formulaic it is, and what a wrong answer costs you. A misrouted booking costs nothing. A wrong quote loses a customer, so automate the routing and let a human set the price. If you want a structured way to hunt those tasks down, the same moves in this seven-step guide to using AI at work without wasting hours work just as well on a website. Map the task, find the data, define the output, test it, then automate.
Step 2: Decide where the intelligence lives
Three options, and they differ more in control than in price. Whichever route you take, spend ten minutes learning how to tell a real AI platform from a resold wrapper, because a lot of vendors in this space are thin skins over someone else’s API with a big marketing budget.
No-code widgets
$20 to $60 a month, a script tag, and a settings panel. Fine for answering FAQs pulled from your own pages. Useless for anything that needs your order data, because the widget can’t see it. They also tend to sound identical to every other site using the same vendor, which customers notice faster than you’d expect.
Model APIs you call yourself
This is where most small teams land. You send a request, get text back, and render it. Pricing is lower than people assume: a support exchange with 800 tokens in and 300 out costs about $0.004 on a small model such as GPT-4o mini or Claude Haiku. At 10,000 conversations a month, that’s roughly $40.
Model choice matters less than retrieval and prompt quality, but it isn’t irrelevant. Google’s Gemini line is worth testing if you already run on Workspace, and this breakdown of what AI Google does well and where it struggles is a fair look at the trade-offs.
Self-hosted open models
Only sensible above roughly 200,000 requests a month, or when data legally can’t leave your servers. A single rented GPU runs about $1 an hour, plus the engineering time to keep it breathing.
One more placement question: where do your customers actually ask? If half of them message you on WhatsApp or Instagram, they’ll expect answers there too. Meta AI now sits across those apps, in search, in chats, in DMs, which quietly raises what people expect from a small business. The background is in this piece on how Meta AI spread into everyday messaging.
Step 3: Feed the model your own content
Off-the-shelf models don’t know your returns window, your service area, or that you don’t work weekends. Two ways to fix that, and one of them is usually wrong.
- Fine-tuning. Slow, costly, and unnecessary unless you have thousands of labelled examples. It teaches style and format more than facts.
- Retrieval. Split your existing pages into chunks, store them, and paste the closest matches into the prompt. This is what 90% of useful AI websites do under the hood.
Concrete version: take your 30 most-asked-about pages, cut them into 300 to 500 word chunks, and store those with their embeddings in pgvector or Pinecone. When someone asks a question, pull the three nearest chunks and drop them into the prompt. The answer comes from your text rather than the model’s training data, which also stops it inventing your prices.
Watch for contradictions. If your FAQ says delivery takes three days and your shipping page says five, the model will quote both within a minute of each other, and a customer will screenshot it.
Step 4: Design for the wrong answer
Every AI feature fails sometimes. What separates a useful one from an embarrassing one is what happens in the two seconds after it fails.
- Set a confidence floor. If retrieval scores are low, don’t generate at all. Show a short form instead.
- Keep a human exit visible on every screen, not buried in a footer.
- Log every query and the chunks behind the answer. The first 200 conversations will teach you more than a year of analytics.
- Cap the blast radius. The model can suggest, draft, and route. It shouldn’t issue refunds, confirm bookings, or email customers without a human pressing send.
Air Canada found out why in 2024. Its chatbot invented a bereavement fare policy, a customer relied on it, and a tribunal ordered the airline to honour the invented price, which was around $1,600 in real money. The lesson isn’t “avoid AI.” It’s that a confident wrong answer becomes your policy the moment somebody acts on it.
Step 5: Test with 50 real questions before launch
Write 50 questions real customers would ask. Include 10 that should fail: things you genuinely can’t help with, out-of-area postcodes, orders that don’t exist. Then run all 50 and count.
Three numbers matter. Correct answer rate should clear 80% on the first pass; if it doesn’t, the fix is better retrieval nine times out of ten, not a bigger model. Refusal accuracy means it says “I don’t know” when it doesn’t know instead of inventing something plausible. Response time should stay under three seconds before you add anything clever.
Test on the slowest connection and the oldest phone you support. Plenty of your visitors are on a mid-range Android over patchy 4G, and a streaming answer that feels instant on your laptop will feel broken on theirs.
Step 6: Launch narrow, then measure
Put the feature behind one page, one audience segment, or one time of day. Then track four things for a month:
- Task completion: did the person actually get a quote, a booking, or an answer.
- Deflection rate: conversations that ended without a human getting involved.
- Cost per conversation, including API calls, hosting, and your own time.
- Handoff quality: when it escalates, does the human start with context or from scratch.
If completion and deflection are both high, widen it. If completion is low, you picked the wrong job and no amount of prompt tuning will rescue it. That last point is the whole difference between bolting a chatbot onto a page and building an artificial intelligence website that earns its keep.
What the first 30 days look like
Week one: pick the job, gather the source content, and write the 50 test questions before you write any code. Week two: build the retrieval pipeline and a plain, unstyled interface, since design decisions get cheaper once the answers are good. Week three: run the tests and fix the ten worst responses by adding better source chunks rather than cleverer prompts. Week four: launch to 10% of traffic and read every conversation log yourself.
Total cost for the landscaping example came to roughly £38 a month in API fees and 12 hours of a developer’s time. Compare that with the £12,000 rebuild in Manchester that handled four conversations in six months.
Keep the retrieval layer separate from the model so you can swap one without touching the other, because models improve fast and the plumbing you build now should outlast whichever one you plug in today. If you want a structured way to think a year or two ahead, this five-step exercise on preparing a team for more capable AI takes an afternoon and forces the right questions.
The teams getting real value here aren’t the ones with the biggest models or the flashiest demos. They picked a boring, specific task, kept it narrow for a month, and only widened it once the numbers proved it worked.