The AI platform Construction uses to get recommended on
Homeowners and developers ask a model who to hire before they ask anyone else, and lead marketplaces are answering. LBOX measures what the engines say about your firm, shows who they name instead, and builds the evidence that changes it.
Measured across
Perplexity
Gemini
Google AI
Claude
Copilot
Meta
DeepSeekGrok... and moreEvery one of them is competing for the same answer. One name comes back when a buyer asks.
What makes Construction different
Not general marketing advice. These are the constraints your category actually operates under.
Lead marketplaces answer, then sell the same lead to your competitors.
Ask who to hire and the engines surface Angi, Houzz and Thumbtack. Those platforms charge you for the introduction and sell it to three other firms at the same time, so the source answering for you is also the reason you are bidding against everyone.
Licensed, bonded and insured is the question that decides the job.
It is also the one the engines answer from stale third-party profiles. A model that cannot confirm your licence will name a firm it can confirm, and being right about your own credentials does not help if nothing readable says so.
Low volume means one bad review becomes the whole summary.
A firm completing a few dozen projects a year has a thin review record, so a single dispute carries enormous weight in anything a model summarises. The same complaint stays in the answer long after it was settled.
Cost questions get answered with national averages.
What should this cost gets a figure scraped from national data with no bearing on your market, your code requirements or your labour rates. Your accurate quote then reads as expensive against a number nobody stands behind.
A handful of answers a month decides the year
Construction has the opposite shape to most of the categories on this site. Project values are large and volume is low, so a firm might win its whole year from a few dozen conversations. That makes the arithmetic of being named completely different: in a high-volume category a percentage point of visibility is a rounding error, and here it can be a project. It also means the usual marketing metrics mislead badly. Impressions and traffic describe a funnel you do not have. What matters is whether your name appears in the small number of conversations where someone with a real budget is deciding who to call.
What you get back
The console, measuring a company in Construction against the questions its buyers ask. Every brand named, every domain cited, every engine separately.
A worked example on an illustrative company, shown to display the console. Your own numbers come from measuring your questions through the engines’ own APIs.
See which firms the engines name
Visibility and position on the unbranded who-should-I-hire questions your clients ask, measured per engine.
- Ranked against the firms the engines actually named
- See where a model reads your pages without naming you
You against the field, engine by engine
unbranded questionsTurn what the answers get wrong into work that ships
Findings arrive as a worklist sorted by who can act: what LBOX writes, what needs your decision, what needs your estimator.
- Scored against your trades and service area, not a generic SEO checklist
- Nothing publishes without your sign off
The worklist
Where you disappear as the client gets closer to hiring
Presence falls away as intent rises. The who-should-I-hire questions are the ones that convert, and the ones the lead marketplaces win.
- Visibility split by where the question sits in the journey
- Names the stage that costs you the project
Visibility by buying stage
where you show up in the journeyThe sources the engines trust about construction firms
Lead marketplaces decide which firms get named, and they charge you for the introduction they are already making.
- Owned versus earned, so you know what to build and what to earn
- Every citation traced to the answer it supported
Which domains the engines cite
571 citationsThe program for Construction
Everything below is included. Measured through the engines’ own APIs, so the same questions can be asked again and the result checked.
- Measure what every major AI engine says about your firm today, through the engines own APIs
- The questions your clients actually ask, mapped by trade, project type and service area
- Where a lead marketplace or directory is answering in your place
- Whether the engines state your licences, insurance, trades and service area correctly
- Which sources the engines cite for your trade, and which of those you control
- Trade, project and service-area pages built so the engines can read and quote them accurately
- Re-measurement on the same questions, so the change is provable rather than asserted
What this does not show: what any individual user saw. LBOX measures what the engines answer through their own APIs, which is repeatable and checkable. It is not a record of any one person’s session, and it does not predict revenue.