Knock2 vs Lucida

Knock2 identifies website visitors, builds profiles, scores visits, and routes leads into sales workflows. It identifies companies globally, while names and contact details for individuals are available only for US traffic. Profiles can include a visitor’s name, title, verified work email, phone, company, and pages viewed. Its AI lead-scoring agent evaluates visits against a team’s description of its ideal customer profile. Workflows can alert sales representatives in Slack, create CRM records, and enroll visitors in sales sequences. Listed connections include HubSpot, Salesforce, Attio, Slack, Microsoft Teams, Apollo, Outreach, Instantly, Smartlead, Zapier, and webhooks. MCP and REST API access also connects with AI tools such as Claude, Claude Code, Cursor, and ChatGPT. Knock2 is available on web and through an API. Paid plans start at $199 a month, billed monthly, for up to 600 contacts per month and 3,600 credits. A 7-day trial is available; there is no free plan. Monthly credits reset each billing month, and exceeding an allocation requires upgrading or waiting for the reset.

Lucida is a web-based lead-scoring tool that ranks prospects by predicted likelihood of conversion, helping sales teams prioritize follow-up. It accepts CSVs from CRMs, forms, ads, webinars, and spreadsheets, including messy files, and learns from a team’s own won and lost deals. Its pipeline prepares features, balances classes, and trains a model around the customer’s conversion pattern. Lucida says it can detect useful signals without manual schema mapping, then return ranked lead lists that can be exported to a CRM. The first ranking is free before a paid plan is selected. The Free plan includes one full ranking, training one custom model, and exporting the top 100 ranked leads, with no credit card required. Starter costs $299 a month and covers up to 10,000 leads monthly; Pro is $799 a month, while Scale is $1,999 a month. Lucida is positioned for growing sales teams, scaling departments, and enterprise operations seeking machine-learning lead scoring without a data science department.