Enriched-field glossary
What each key in the enriched-lead payload means, how it's derived, the shape it takes, and when it's empty or null. Written for a buyer deciding whether this data is good enough — not just a syntactic schema.
Firmographics summary of the account.
company_overview.company_sizestringBucketed headcount estimate. Derived from firmographic signals (job-board activity, LinkedIn, press). One of: '1-10', '11-50', '51-200', '201-1000', '1000+', or 'Unknown' if we can't form a confident guess.
"51-200"
No reliable headcount signal was found.
company_overview.industrystringPrimary industry classification in plain English. Not a GICS code — this is written for human readers.
"B2B SaaS"
Industry couldn't be inferred (very rare).
company_overview.summarystringOne-paragraph description of what the company does, written for a salesperson about to send outreach. Reflects positioning and product focus, not just scraped marketing copy.
"Acme Corp builds workflow automation for mid-market finance teams, focused on month-end close acceleration."
Only on complete enrichment failure (score_rationale will explain).
Technologies the account uses, grouped by category. Lists are deduped and ordered by signal strength.
tech_stack.cloudstring[]Cloud providers detected (AWS, GCP, Azure, Cloudflare, etc.).
["AWS", "Cloudflare"]
Returns [] (empty), never null.
tech_stack.crmstring[]CRM in use (HubSpot, Salesforce, Pipedrive, Attio, Close, …).
["HubSpot"]
Returns [] when no CRM signal was found.
tech_stack.securitystring[]Security / IAM tooling (Okta, 1Password, CrowdStrike, …).
["Okta"]
Returns [] when nothing matches.
tech_stack.infrastructurestring[]Infra and devtools (Kubernetes, Datadog, Snowflake, …).
["Datadog", "Snowflake"]
Returns [] when nothing matches.
tech_stack.otherstring[]Notable tools that didn't fit the categories above (Notion, Linear, Figma, Segment, …).
["Notion", "Segment"]
Returns [] when nothing matches.
Products or services the company sells. Order reflects prominence on their site.
productsstring[]Each entry is a one-line product name (and sometimes a short tag). Used to tailor outreach to the right line of business for multi-product accounts.
["Workflow Builder", "Close Accelerator"]
Returns [] for single-product companies where no distinct SKUs could be extracted.
Fresh signals that the account might be in-market right now. We prefer recent signals (<90 days) over anything stale.
intent_signals.hiring_signalsstring[]Recent hires or open roles signalling intent. Each item is a short sentence (role + context). Pulled from ATS APIs and site crawls.
["Hiring 3 Senior AEs in NYC (posted 12 days ago)"]
Returns [] if no relevant roles are open.
intent_signals.recent_newsstring[]Press hits from the last ~90 days. Deduped. Most-recent-first.
["Series B $45M led by Accel (Feb 2026)"]
Returns [] when the news search returned nothing.
intent_signals.content_publishedstring[]Articles, launches, or notable blog posts from the account.
["Published 'The CFO guide to automated close' on their blog"]
Returns [] when no content was surfaced.
intent_signals.events_attendedstring[]Industry events, sponsorships, or speaking slots.
["Sponsoring SaaStr Annual 2026"]
Returns [] when no event signal was found.
Who to talk to and in what order. Not a dump of the whole org chart — a curated short list.
buying_committeeobject[]Each item has `name`, `title`, `role` (e.g. 'Economic buyer', 'Champion', 'Technical evaluator'), and `linkedin_url` when available. Max ~5 people.
[{"name":"Priya Rao","title":"VP Finance","role":"Economic buyer","linkedin_url":"https://linkedin.com/in/priyarao"}]Returns [] for very small companies where we couldn't identify distinct roles.
The problems this account most likely cares about right now. Based on tech stack gaps, industry pattern, and recent signals.
pain_pointsstring[]Each item is one sentence describing a problem the account is likely feeling. Used to pick the right value prop angle.
["Month-end close drags into week 2 because reconciliation is still manual."]
Returns [] on sparse accounts where pain couldn't be inferred.
AI-written outreach starter. Opinionated — pick the pieces that fit your voice.
engagement_playbook.timingstringWhen to reach out. E.g. 'Within 2 weeks of their Series B announcement'.
"Within 2 weeks — hiring signals suggest active evaluation."
Rarely null; usually a hedged recommendation.
engagement_playbook.opening_linestringA personalized first-line for the outreach email. Not a template — references specifics from the enrichment.
"Noticed Acme just closed a $45M Series B — scaling finance ops is going to look different in 60 days."
Null on complete enrichment failure.
engagement_playbook.value_propstringThe positioning statement that's most relevant for this specific account.
"We cut month-end close from 8 days to 2 for Series B+ fintechs."
Null on complete enrichment failure.
engagement_playbook.case_study_anglestringWhich proof story to lean on — references a persona and outcome, not a brand name.
"Mid-market SaaS CFO who went from manual to automated close in a quarter."
Null when no plausible analog pattern was identified.
The single number sales teams filter by. 0 = ignore, 100 = highest priority.
opportunity_scoreinteger (0–100)Composite score combining fit (firmographic match to your ICP) and intent (freshness and strength of in-market signals). Weighting is model-dependent; see score_rationale for the specific reasoning on each lead.
72
Never null — defaults to 0 on enrichment failure (with an explanation in score_rationale).
score_rationalestringOne-sentence plain-English explanation of why the score landed where it did. The thing you read when the score looks wrong.
"Strong hiring + recent funding, but mid-market SaaS doesn't fit your enterprise-only ICP."
Null only on complete enrichment failure.