Yes. Each service page and case study tags the tier mix in postmeta: One-to-One percentage, One-to-Few percentage, One-to-Many percentage. When a prospect describes their goal the bot recommends a mix and matches the closest case study by similar mix. Heavy One-to-One programs (top 20 named accounts) scope very differently than Many programmatic ones.

Whatever your firm lists on its services pages. Typical coverage: 6sense, Demandbase, RollWorks, Madison Logic, Terminus, MRP, Triblio, and Folloze. The bot quotes your certified-partner level from a site option. A 6sense Elite partner reads as 6sense Elite, not 'a partner.' Platform recommendations factor stack, ARR, and account count.

If your firm publishes an ICP build framework as a CPT, the bot reads it and walks the prospect through the methodology. Firmographic, technographic, intent, and engagement signals each get explained with the data sources you typically use (ZoomInfo, Clearbit, Bombora, G2). The bot offers the relevant case study where the ICP work produced the largest pipeline lift.

List size ranges sit in your service tier postmeta. Foundation might be 100-400 accounts, Enterprise might be 800-2000. The bot quotes ranges based on the tier and explains the trade-offs: smaller lists allow more depth per account, larger lists trade depth for coverage. The CMO leaves with a concrete number to bring to their team.

Yes. Play library scope sits in postmeta as a count and depth. The bot tells a prospect whether their engagement delivers 12 plays, 24 plays, or a full quarterly library, and what each play includes (creative, sequence, sales enablement). The buyer leaves with a real number to plan against.

Yes. Map Marketo, Pardot, HubSpot, Eloqua, and any CDP as stack variables. The bot reads the prospect's stack from chat and recommends ABM platform fit accordingly. Marketo plus Salesforce typically gets a different recommendation than HubSpot plus a CDP. Stack context flows into the AE handoff log.

Yes. The bot extracts who the buyer is and who else is involved (CMO, VP Demand Gen, VP Sales, RevOps). That context routes the conversation correctly. A CMO buyer gets different framing than a VP Demand Gen buyer because the engagement scope and budget conversation differ between them.

If your firm publishes measurement frameworks, the bot reads them and explains how pipeline influence is attributed across One-to-One, One-to-Few, and One-to-Many tiers. Common attribution models (first-touch, multi-touch, account-engagement-based) get covered with the trade-offs. Buyers leave with realistic expectations rather than inflated promises.