AI agents that live inside your website
Assistants that answer from your actual content, qualify leads and hand off to humans — wired into the site you already have. We built one for ourselves first, so you can judge the craft before you buy it.
Judge it by ours
The agent on our /ai page is a production build, not a slide: intent matching, structured responses rendered as real UI blocks — cards, tags, forms, CTAs — and a brief-collecting flow that hands off to a human.
What agents do well
- Answer from your content — services, cases, FAQs — with real UI, not walls of text
- Qualify leads: collect the brief, budget range and timeline before a human ever joins
- Hand off gracefully — to a form, an email thread or a booked call
- Stay on brand: tone, restrictions and fallbacks are designed, not left to chance
What we will not promise
- Promise "AI will replace your team" — it will not, and we will not pretend otherwise
- Hallucinate offers or prices — the agent answers from structured content you approve
- Ship as a black box — you get the intent map, the content model and the levers
Building agents people don’t hate — the evidence
The uncomfortable research we design around. A bad bot is worse than no bot, and the data below is why ours behave the way they do.
Most people do not want your bot
Gartner: 64% of customers would prefer companies not use AI in customer service, and 53% would consider switching if AI blocks them from a human. Design consequence: a visible “talk to a human” escape in every conversation, never gated behind failed bot turns.
Disclosure is law now
EU AI Act Article 50: users must be told they are talking to AI at first contact — enforceable from August 2, 2026, with fines up to €15M or 3% of global turnover. A bot pretending to be “Sarah from support” is now a legal liability, not a growth hack.
Grounding or nothing
Our agents answer only from retrieved site content, cite what they used, and fail closed — below a confidence threshold they say “I don’t know” and offer a human instead of guessing. A regression suite of real queries re-runs after every content change.
Prompt injection is the #1 LLM risk
OWASP has ranked it first two editions running. Untrusted content stays segregated from instructions, outputs are filtered, and the agent’s tools run least-privilege — it physically cannot leak what it cannot reach.
The hype correction is underway
Gartner expects over 40% of agentic AI projects to be canceled by 2027 — costs, unclear value, weak risk controls — and warns about “agent washing.” Our answer: scoped pilots with KPIs agreed before the build, an unanswered-questions log, and a kill switch.
The warm handoff is the product
Escalation triggers on low confidence, frustration, a direct request, or sensitive topics — and the human receives a summary of what the visitor already tried, so nobody restarts discovery. Healthy early deployments escalate 10–15% of conversations. That is a feature.
Sources: Gartner on AI in service · Gartner on agent cancellations · EU AI Act, Article 50 · OWASP LLM Top 10
Honest answers
What does an agent project look like end to end?
Content and intent mapping first, then response design (which answers deserve cards, forms or a human), then the build and a tuning period on real conversations. The /ai page on this site is the reference implementation.
Does it need our content in a specific system?
No — structured content helps (Craft CMS is ideal), but agents can draw from whatever your source of truth is. That is an integration problem, and integrations are our home turf.
What about data privacy?
You decide what the agent can see and what leaves your infrastructure. We design the data boundary explicitly and document it — no silent third-party sharing.
What happens when the agent does not know — will it make something up?
No system is hallucination-free, and anyone claiming otherwise is lying. Ours are engineered to fail closed: answers come only from your approved content, low retrieval confidence triggers an honest “I don’t know” plus a human handoff, and a suite of real queries re-runs after every content change to catch drift.
Will our customers’ conversations train someone else’s model?
Not without you knowing: major providers do not train on API traffic by default, and we verify that per contract rather than assume it. Transcripts are encrypted, retention is limited and agreed, and the data boundary is documented explicitly — you decide what the agent can see.
Won’t a chatbot annoy our visitors?
The skepticism is data-backed — 64% of customers would prefer no AI in service (Gartner). Badly built bots earned that. Ours are designed against the failure modes: triggered by intent instead of popping up instantly, clearly disclosed as AI, easy to dismiss, and never standing between a visitor and a human.
Want an agent like ours?
Try the live demo first, then tell us what yours should do differently.
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