How Do Builders Get Found in AI Search Results?
Schema markup for builders starts at £49 and is delivered in 48 hours. We convert your Federation of Master Builders credentials, insurance levels, planning support, and project specialties into machine-readable data so AI assistants surface your firm when homeowners ask about extensions, loft conversions, or structural repairs.
Why Builders Are Missing Out on AI Visibility
Six-Figure Projects Demand Trust Signals
AI assistants want proof that you are safe to recommend for a £120k extension. Without Organization schema declaring your FMB or TrustMark membership, SMSTS-trained site managers, and £5m liability cover, assistants downgrade you to “uncertain builder” status. National contractors that publish those credentials as structured data win the slot, even if they outsource everything.
Planning Permission Expertise Is Invisible
When someone in Sale asks, “Find a builder who handles drawings and planning permission,” ChatGPT scans for Service schema describing feasibility studies, structural engineering coordination, and planning appeals. If your site buries that expertise in a PDF brochure, AI has nothing to cite. You end up losing work to architects who barely touch construction.
Project Types Look Generic
“We do all building work” means nothing to AI. Service schema lets us split your offer into loft conversions, single-storey extensions, basement digs, period refurbishments, and commercial fit-outs. Each can include square footage, materials (timber frame vs steel), and lead times. Without it, assistants lump you with jobbing handymen.
Portfolios Are Dark Data
Your photo galleries prove craftsmanship, but AI can’t interpret them unless you add ImageObject schema. That markup stores captions like “Victorian terrace loft conversion, Chorlton, 32m², completed Oct 2025.” Those data points help AI match you to “loft conversion Manchester Victorian roofline” queries instead of citing Pinterest boards.
How AI Visible Helps Builders Be Seen
We audit your entire digital footprint—website, Google Business Profile, planning case studies—and map it to schema types AI engines trust. LocalBusiness schema configured as GeneralContractor declares your service radius, minimum project values, insurances, and emergency support. Service schema describes every project type with budgets, lead times, and whether design-and-build is included. Organization schema houses memberships (FMB, TrustMark, CHAS), DBS checks, and named project managers. AggregateRating pulls in Google, Houzz, or Checkatrade reviews with verifiable counts.
📊 What You Get:
GeneralContractor LocalBusiness schema with all SK/M/WA postcodes you cover • Service schema for extensions, lofts, renovations, insurance reinstatement • Organization schema storing FMB/TrustMark IDs and £5m liability cover • ImageObject schema for up to five hero projects • AggregateRating anchored to your Google reviews.
48-Hour Implementation Process for Builders
We follow the same disciplined workflow outlined in our New Rules of SEO 2026 briefing. Day 1 begins with a structured crawl of your site, case studies, and Companies House filings so we can extract memberships, VAT info, typical budgets, and service areas. By midday we map each project type—rear extensions, shell builds, insurance reinstatement—to the relevant schema classes and identify missing trust signals.
During the afternoon we draft JSON-LD for LocalBusiness, Service, Organization, AggregateRating, and key ImageObjects, then run everything through schema.org validators and our own ChatGPT/Perplexity prompts (“Who builds loft conversions in SK4 with FMB membership?”) to confirm assistants can now cite you. Day 2 covers deployment support and screenshot-based validation. Weeks 2-4 are spent retesting the same AI prompts, logging before/after citations, and flagging extra opportunities such as targeting "near me" micro-intents with additional service pages.
Essential Schema Types for Builders
LocalBusiness Schema: Proving Your Construction Footprint
LocalBusiness schema configured as GeneralContractor stores your registered office, satellite depots, service radius, emergency repair availability, project minimums, helpline numbers, and office hours. We include every postcode district you cover plus typical contract values so AI understands you tackle £50k shell builds, not £500 handyman jobs.
Service Schema: Detailing Project Scopes
Service schema lets us split offerings into loft conversions, multi-storey extensions, structural openings, listed-building restoration, basement digs, and insurance reinstatement. Each service lists square footage added, planning stage support, build timeline, whether design-and-build is offered, and any guarantees. AI assistants then answer queries like “builder for double-storey extension with planning support in Trafford” with your firm instead of a general directory.
Organization Schema: TrustMark, Insurance, and Team
Organization schema captures FMB, TrustMark, CHAS, Considerate Constructors, SMSTS, and IOSH credentials. We include £5m public liability, £10m employers’ liability, and PI coverage if you offer design. Named contacts (project director, QS, site manager) can be referenced using Person schema for extra trust. When Perplexity looks for “builder with TrustMark and DBS-checked supervisors,” this data proves you qualify.
ImageObject Schema: Turning Portfolios into Structured Proof
By wrapping your best projects in ImageObject schema we publish property type, neighbourhood, square footage added, materials, and completion dates in machine-readable form. Assistants can then cite “Loft conversion, Didsbury, 28m², timber frame, completed August 2025” when recommending you for similar jobs.
Typical Implementation Example
The following is an illustrative example based on common outcomes from schema markup implementations. Individual results may vary.
The Scenario: A family-run building firm in Bristol was losing extension work to larger competitors. Homeowners checked their reviews on Checkatrade but missed them in AI search.
The Diagnosis: Their trusted reputation was siloed on third-party sites. AI assistants couldn't verify their Federation of Master Builders (FMB) status.
The Solution: We implemented GeneralContractor schema, linking their FMB membership and pulling their 4.9-star aggregate rating directly into search results.
The Outcome: The builder reported **3x more extension leads** in the following quarter, with clients specifically citing their verified reviews visible in search. (Source: FMB & Checkatrade Digital Trends)