AI Website Experiences
Most AI features added to websites are a chat bubble that answers questions the FAQ already covered. The useful version is narrower and more specific: it does a job the business currently does by hand.
- Assistants
- Personalization
- Automation
- Content Tooling
- Guardrails
THE PROBLEMS THIS WORK EXISTS TO FIX.
- 01
The assistant answers confidently and wrongly
A model with no grounding in your actual services will invent them. That is a liability, not a feature.
- 02
It creates work instead of removing it
A chatbot that produces unqualified conversations for a human to sort through is a cost.
- 03
Costs are unpredictable
Without rate limiting and token budgets, one scripted abuse run can produce a memorable invoice.
- 04
Nobody thought about the failure state
Models time out and refuse. The interface has to behave sensibly when that happens.
AI built into the product where it removes work, rather than a chat bubble in the corner.
Grounding beats cleverness
An assistant should answer from your content: your services, your process, your published policies. When it does not know, it should say so and hand over to a person.
That means retrieval against a controlled source rather than a general model answering from training data. It is the difference between a feature and a liability.
Where AI actually pays for itself
Qualifying enquiries before they reach a person. Turning a long form into a short conversation. Summarising a request into the shape the team already works with. Generating a first draft of a routine reply for a human to approve.
The common thread is that each one removes a repetitive task. Features that only add novelty tend to get switched off within a quarter.
Guardrails are part of the build
Server-side keys, never client-side. Rate limiting per address and per session. Token ceilings. Input validation. Bot protection on anything that costs money to run. Logging that captures enough to debug without storing more personal data than necessary.
Where a feature fetches a URL the visitor supplied, it needs protection against requests to private networks. That is a specific, well-understood class of vulnerability and it has to be handled deliberately.
Disclosure
AI-generated output gets labelled as AI-generated. Conceptual previews get labelled as conceptual. We do not present model output as a verified audit, and we do not present generated imagery as real company work.
THE PROCESS.
- 01
Scope
Identify the specific task the feature removes, and what happens when it fails.
- 02
Ground
Assemble the controlled content source the model answers from.
- 03
Build
Server-side integration with validation, rate limiting, and budgets.
- 04
Test
Adversarial testing for hallucination, abuse, and cost.
- 05
Monitor
Usage, cost, and quality tracked after launch.
FAQ
Which models do you use?
It depends on the task. Selection is made against cost, latency, and quality for the specific job rather than defaulting to one provider.
Who pays for the API usage?
You do, on your own account. We build with your keys stored server-side and set budgets and rate limits so usage stays predictable.
Can it answer questions about our services?
Yes, when it is grounded in your actual content. That is the part that makes it safe to publish.
Is any of this going to make things up?
Ungrounded models will. Grounded ones with an explicit fallback to a human are the reason we build them that way.
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Website Development
High-performance responsive development.
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Custom Website Design
Custom UI/UX design built specifically around the business and customer journey.
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SEO
Technical SEO, on-page SEO, structured data, performance optimization, and search foundations.
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