Account Fit Rank

Score and tier a list of accounts on four axes (fit, intent, trigger, workforce) using firmographics, technographics, intent topics, events, and workforce trends, then apply a transparent weighted composite the calling model computes from the returned data.

Input

• Accounts (required): list of business IDs, company names, domains, or a mixed CSV.
• Use case (default prospecting): one of prospecting, abm, territory_planning, pipeline_acceleration. Shifts tier thresholds and recommended actions.
• ICP definition (required): industries, employee buckets, revenue buckets, country or region, optional tech-stack vendors, optional intent topics. Capture inline; no persisted ICP exists.
• Weight overrides (optional): {fit, intent, trigger, workforce} summing to 100. Default 45 / 25 / 25 / 5.
• Tier thresholds (optional): {A, B}. C is the remainder. Default A>=75, B 50-74.

Workflow

1. Lock the ICP and intent topics. Restate the ICP from the user. Discover canonical values for every free-text dimension (industry, technology, intent topic, city). Resolve intent topics one term at a time: fuzzy multi-term queries fail silently. If a tag does not resolve, drop it and flag that axis as configuration-gap, not signal-absent.

2. Resolve identifiers. Route inputs by shape: existing business IDs pass through; domains and names resolve via business match, with an optional country tiebreaker for names. Never silently pick a winner: surface top candidates for ambiguous rows and ask for confirmation. For high-collision names, require domain confirmation before scoring. Sanity-check the resolved firmographics: if a major-brand input returns 1-50 employees and Corporate-Managing-Offices category, the match likely routed to a shell entity. Retry with the alternate domain or the name string. Every input ends as auto-resolved, verified, ambiguous, or failed.

3. Pre-flight relationship context. Tag each resolved account against any user-supplied competitor / customer / partner lists before scoring so a "pursue this competitor" line is never produced silently.

4. Fetch firmographic, technographic, and signal data in small chunks end-to-end (resolve, enrich, score, write row, discard raw payloads). Per chunk: enrich with firmographics, technographics, recent LinkedIn posts, funding and acquisitions, workforce trends, strategic insights, and website changes; then fetch business events scoped to the last 90 days for funding rounds, leadership changes, product launches, and expansions. If the ICP includes intent, size intent-topic exposure separately; if no topics resolved in step 1, set intent weight to zero and redistribute. Drop raw payloads after extracting the per-axis inputs and the single winning signal for "why now".