Lead-Generation Websites for Service Businesses
Conversion-focused websites for service businesses that need to explain complex offers clearly, earn trust, and turn qualified visits into enquiries.
Explore the solution →AUZ Case StudyLive
AUZ Case Study · AI Automation
An always-on assistant that understands, guides, and qualifies.
Vivi is the intelligent assistant Auztec built for Vision Touch Ltd’s website. It understands natural-language, typo-tolerant questions, recognises 140+ Greater London areas and postcodes, answers from the site’s own content, and qualifies enquiries through a guided flow — all without a mandatory paid AI service, with a one-switch upgrade to Cloudflare Workers AI.
- Client
- Vision Touch Ltd
- Sector
- Conversational AI · Lead Qualification
- Category
- AI Automation
AuztecVivi
AI Automation
The challenge
Qualify enquiries 24/7 — without a fragile backend
A construction firm’s website visitors ask wide-ranging questions — services, areas, timelines, quotes — often with typos and local place names. The brief called for an assistant that could answer instantly and offline-capably, qualify real leads, and run at near-zero cost on serverless hosting, with a clean upgrade path to a hosted LLM when the business wanted it.
Our solution
A three-layer engine: local intent, optional AI, safe fallback
We engineered a small conversational engine that scores keywords over a knowledge base built from the site’s own content, with bounded fuzzy matching and light stemming for typo tolerance, plus conversation context that remembers the current service and area. An optional server-side AI layer (Cloudflare Workers AI or any OpenAI-compatible provider) is consulted only for low-confidence questions, and a rule-based fallback handles human handover, lead capture, and email drafting.
What we built
Capability highlights
The engineering and design work behind the product.
Typo-tolerant understanding
Bounded Levenshtein fuzzy matching and light stemming resolve “rennovation”, “kitchin”, “illford”, and more.
140+ areas & postcodes
Recognises all Greater London districts plus postcode-prefix detection (IG1, HA1, SE15 …).
Conversation context
Remembers the current service and area, so follow-up questions and a plain “yes” continue the right thread.
Guided lead qualification
A nine-step flow captures the enquiry with project type and location pre-filled, then drafts an email.
Knowledge-grounded answers
A retrieval fallback fuzzy-searches the whole knowledge base before ever saying “I’m not sure”.
Privacy & safety guardrails
No server-side conversation storage, input caps, rate limiting, and grounded prompts — no prices, no promises, no invented facts.
Delivered
What the build ships with
Understands natural-language, typo-tolerant questions
Recognises 140+ Greater London areas plus postcode prefixes
Nine-step lead-qualification flow with pre-filled project type and location
Answers grounded in the site’s own knowledge base — no mandatory paid AI
37 automated intent checks in the test suite
One-switch upgrade path to Cloudflare Workers AI
Services applied
Multidisciplinary delivery, one team
Conversational AI EngineeringKnowledge-Base DesignIntent & Flow EngineeringServerless IntegrationLead-Capture UXPrivacy & Safety GuardrailsAutomated Intent Testing
Technology & delivery
The stack
- Vanilla JavaScript widget (lazy-loaded)
- Shared local intent engine
- Cloudflare Pages Functions (/api/chat, /api/chat/lead)
- Optional Cloudflare Workers AI / OpenAI-compatible layer
- Web3Forms lead pipeline
- Automated intent test suite
Keep exploring
Related work
Learn from the build
Related solutions and engineering insights
See how the principles behind this project apply to other business problems.
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