{"componentChunkName":"component---src-templates-compete-db-md-js","path":"/compare/vector/ai/md/","result":{"data":{"doc":{"id":"607e574f-c481-58a4-944a-f7c4ea7fad61","title":"Vector","createdAt":"2026-10-23T17:24:00.000Z","properties":{"Domain":{"value":"https://vector.co"},"Product":{"value":{"name":"Intent / ABM"}},"Market":{"value":{"name":"Commercial"}},"Status":{"value":{"name":"watch"}},"Summary":{"value":"AI-native ABM platform using contact-level signals to score accounts, target ads to buying committees, and prove marketing impact on pipeline"},"Tag":{"value":"ABM finally works the way you thought it would"},"Funding":{"value":"10.5"},"Founded":{"value":"2022"},"Team":{"value":"20"},"Details":{"value":"Also known as GetVector, Inc. Founded 2022 in Boston by Joshua Perk and Nick Masters, former Drift sales engineering leads. Y Combinator W2023. Raised 10.5M Series A (May 2026) led by SignalFire and HubSpot Ventures. Industry: B2B MarTech (ABM, ad audience sync). Proprietary closed-source SaaS. Sales-led: demo-gated, direct sales team, enterprise pricing with 36,000 annual floor on Target product; ICP is mid-market B2B SaaS with 250,000+ annual paid social budget. Not product-led: no free trial or self-serve signup. Hiring Sales Engineer in Boston. Revenue, web traffic, social: n/a."},"Features_List":{"value":"- Contact-level website visitor identification and de-anonymization\n- 30+ native buying signals including job changes and ad engagement\n- Transparent account scoring and buying stage prediction\n- Dynamic audience syncing to LinkedIn, Google, Meta, and Reddit\n- Pipeline attribution and reporting to tie anonymous signals to revenue\n- CRM integrations with HubSpot and Salesforce\n- Real-time sales notifications for account alignment"},"Critical":{"value":"- Y Combinator-backed; contact-level precision vs. account-level guessing\n- No native revenue attribution layer; needs bolt-on solution\n- Best for US-focused mid-market SaaS with strong inbound traffic and high paid social budgets\n- Dropped outbound SDR use cases in late 2025; marketing audiences only"},"Signals___1st_Party":{"value":"IN: Multi-network ad engagement at individual level (Google, Meta, Reddit, TikTok, X, Snap); HubSpot, Salesforce, Pardot; Fathom call recordings; person-level web de-anonymization\nOUT: No product usage or data warehouse ingestion; no sales engagement, CS, or chat/support tools; no Marketo"},"Signals___2nd_Party":{"value":"IN: LinkedIn organic post reactions and comments; social brand-mention listening\nOUT: No open source, community forums, review sites, events, YouTube/Medium/DEV, or commerce signals"},"Signals___3rd_Party":{"value":"IN: Job changes and champion tracking; AI web research on funding, M&A, earnings, press; off-site category and competitor research intent; custom AI signals\nOUT: No named intent partners; no technographics; no job listings signal"},"Signals___Custom":{"value":"IN: Push/pull webhooks; developer API; Zapier; CSV; Clay (hashed email discovery); HockeyStack webhooks; MCP server for Claude/ChatGPT\nOUT: No native Google Sheets; no warehouse ingestion; no CLI"},"P360___Enrichment":{"value":"IN: Real-time enrichment of live signals (name, work email, LinkedIn URL, title, company, seniority); advertising enrichment (~90% LinkedIn, 45% Meta, 30% X match); 30+ buying signals; Clay and HockeyStack integrations\nOUT: No contact directory or net-new contact discovery; no phone enrichment; no CRM list enrichment or refresh; no multi-provider waterfall"},"P360___AI_powered":{"value":"IN: AI buying-stage classification (Identified, Aware, Interested, Considering, Selecting) with cited evidence; promptable custom AI signals\nOUT: No AI in identity resolution; no image or social profile matching; no AI agents acting on data"},"P360___Identity":{"value":"IN: Deterministic-first waterfall (HubSpot UTK, form fills, logins, email+UTM), then probabilistic device graph and IP; B2B-to-consumer mapping (hashed personal emails, mobile numbers, MAIDs); native resolution without Clearbit/RB2B; ~30% bot/noise filtering; ICP filtering; 25-35% match rate\nOUT: Scoped to web sessions and ad clicks; no cross-source person merge; no merge review queue; weak outside US"},"Auto___Combine":{"value":"IN: Combine web de-anonymization, off-site intent, job changes, funding, and ad engagement (LinkedIn, Meta, Google, Reddit) as filters and triggers\nOUT: No product usage, community, open source, CS, or technographic data to combine"},"Auto___Score":{"value":"IN: AI account scoring into buying stages with cited evidence for every change (e.g., VP Marketing visited pricing 3x in 4 days); no black-box scores\nOUT: Fixed stages at account level; no custom scoring models"},"Auto___AI_Message":{"value":"IN: none\nOUT: No AI outreach; outbound SDR use cases dropped late 2025"},"Auto___AI_Research":{"value":"IN: Vector MCP (May 2026) for natural-language queries from Claude/ChatGPT; AI web research on company events; custom prompted signals\nOUT: No in-product research agent; no CLI"},"Signals":{"value":"1st Party: HubSpot, Salesforce, Pardot CRM deal stage changes, logged calls, booked meetings; marketing activity (email engagement, form submissions); ad clicks across Google, Meta, Reddit, TikTok, X, Snap even without form fill; Fathom call recordings. 2nd Party: LinkedIn organic post reactions and comments; social brand mentions; used as free evidence for scoring. 3rd Party: job changes, new hires, departures, promotions, champion tracking; AI web research on fundraises, acquisitions, earnings, press; off-site category and competitor research intent. Custom: developer API, push/pull webhooks, Zapier, CSV upload, MCP for Claude/ChatGPT, Clay for enrichment and hashed email discovery."},"Identity":{"value":"Lightweight JavaScript pixel with deterministic and probabilistic resolution. Waterfall: HubSpot UTK, form fills, authenticated logins, email+UTM, then device graphs and IP. Maps work emails to hashed personal emails, mobile numbers, MAIDs for cross-device tracking. Native resolution without Clearbit or RB2B. Filters ~30% bot/low-quality traffic; ICP filtering; 25-35% match rate. Enrichment: real-time on live signals; name, work email, LinkedIn URL, title, company, seniority; advertising enrichment lifts ad match rates (90% LinkedIn, 45% Meta, 30% X); 30+ signals; Clay and HockeyStack integrations. AI: stage prediction (Identified, Aware, Interested, Considering, Selecting) with plain-language cited evidence; custom AI signals; no AI in identity matching."},"Automation":{"value":"Workbench: none. Workflow: 30+ contact-level signal triggers with conditions; actions sync dynamic ad audiences (LinkedIn, Meta, Google), update CRM records, fire webhooks. Alerts: customizable Slack alerts with qualifying conditions, custom context, routing to channels, DMs, or CRM account owner. Combine: first-party de-anonymization + third-party intent + ad engagement as filters and triggers. Score: AI buying-stage classification with cited evidence. AI Message: none; outbound SDR dropped late 2025. AI Research: Vector MCP (May 2026) connects data to Claude/ChatGPT for natural-language queries."},"Support___Systems":{"value":"IN: Email support (support@vector.co), M-F 9-5 EST, 24-business-hour response goal; security@vector.co channel; 99.9% uptime guarantee; status page; proactive maintenance emails\nOUT: No chat, ticketing portal, or 24/7 coverage"},"Support___Content":{"value":"IN: Help docs and FAQs (GTM pixel, SSO/SAML, roles, billing, privacy); API, webhook, and MCP docs; playbooks (The Reality of ABM Report, new demand gen manager success kit, signal-to-meeting playbook); podcast; blog; glossary; newsletter\nOUT: No video training, academy, or certification"},"Support___Community":{"value":"IN: none\nOUT: No forum, templates, user profiles, or peer support"}}}},"pageContext":{"id":"607e574f-c481-58a4-944a-f7c4ea7fad61"}},"staticQueryHashes":[]}