Match All processes every unmatched BOQ item in the scenario in one operation, scoring each against the rate table using the saved Matcher Scoring Profile. It is the recommended first step after configuring the scoring profile.
AI Refine is the pre-processing step that improves matching accuracy by cleaning descriptions and attaching invisible matching metadata to every item in a table. Running Refine before matching is strongly recommended — it is the single biggest lever for improving match quality.
Coverage Analysis and Template Seeding are the recommended workflow for generating URAs at scale. Instead of calling the AI once per BOQ item (hundreds of calls), you seed a small set of templates (typically 8–15) and then apply them across the entire BOQ in one batch pass. The result is faster generation, lower API cost, and consistent outputs across all items of the same work type.
Conio uses a freemium model. The Free tier is permanent and fully functional for all non-AI workflows. The Pro tier adds AI features by unlocking them with a license token.
The Matcher Scoring Profile is a project-wide configuration that controls exactly how Conio scores the similarity between a BOQ item and a rate table item. Every change you make here applies to both Match All and the live candidate previews — what you tune is what runs.
AI-assisted URA generation produces a complete resource breakdown for a matched BOQ item in seconds. The AI draws on the active Resource Price Book, the Template Library, and the Generation Contexts you have configured — it never makes up prices.