Lead Scoring, a queue your reps actually trust.
A score is only worth trusting if it was validated against your real wins. This build assembles a two-model score, tunes the thresholds against your closed-won and closed-lost data, wires BDR routing, and proves it with an A/B test. It is a scoped engagement with acceptance criteria, not a zip file.
Artemis GTM is a consulting service for AI GTM engineering, delivered as headless agents that install into your own Claude Code, claude.ai, or Codex.
Callsign: Vector.
What it builds
The Lead Scoring agent is a paid module that closes unqualified lead volume inside your own stack. It builds a two-model score, a Pain-First fit model from your ICP and a behavioral intent model from enrichment and visitor signals, then tunes the thresholds against your real conversion data and wires BDR queue routing and decay. It costs $349, owned forever, and ships with monthly threshold-tuning and quarterly fit-refresh routines so the score does not drift.
Written up in full in the knowledge layer: How to Implement Lead Scoring.
Who it is for
- Teams drowning in raw lead volume where BDRs cannot tell fit from noise.
- HubSpot or Salesforce shops with enrichment (Amplemarket or Apollo) and Warmly intent signals.
- RevOps leads who want a score validated against real closed-won and closed-lost data.
Built to finish, not just advise
Every paid agent runs the build like a chief of staff. Tracked tasks, scheduled milestones, blockers flagged early. The part a course or a PDF structurally cannot do.
Progress you can see.
A live progress line every session: what is done, what is next, and how much is left before the system is live.
It schedules the work.
Connect Google Calendar, Slack, or Gmail and the agent puts milestones on your calendar, posts the day's task to Slack, and sends weekly recaps, so the build keeps moving between sessions.
Blockers surfaced early.
It maps dependencies up front, for example flagging that you will need HubSpot admin access before the CRM wiring step, not after it stalls you.
A finish line, not a fade-out.
Milestone unlocks and a verified end state keep momentum. Most self-serve builds stall halfway; this one is engineered to get done.
How the scoring build runs
Discovery
Pull your closed-won and closed-lost data. Map current ICP, scoring if any, and conversion baseline.
Fit model
Build the firmographic and technographic fit score using the Pain-First framework. Tested against your real wins.
Intent model
Layer in behavioral signals: visitor identification, content engagement, sequence opens, and ICP-match recency.
Threshold and routing
Combine scores, set thresholds against your conversion data, and wire BDR queue rules and handoff SLAs.
Decay and refresh
Apply decay schedules so stale leads do not poison the queue. Includes the lead-score decay framework.
Install routines
A monthly threshold-tuning routine and a quarterly fit-model refresh so the score stays honest as your funnel evolves.
A license key drops into the engineer you already installed, and Lead Scoring lights up. From key to first discovery question in seconds.
Full transcript of a simulated unlock. Example license key; yours arrives at checkout.
Scoring, qualification, or ICP: which layer do you need
Three different layers, and the order matters. Here is what each builds and when it is the right entry point, from the FAQ.
| Layer | What it builds | Right entry point when |
|---|---|---|
| ICP Definition | Who you are aiming at: win patterns plus the acute pains behind your best deals. Feeds the fit half of every score. | Your ICP is still firmographic-only, so the fit model has no real target yet. |
| Lead Scoring (this agent) | The two-model score on top of that ICP: firmographic fit plus behavioral intent, with decay, BDR queue rules, and an A/B test to prove it. | Your ICP is solid but leads sit unscored. |
| Qualification Automation | The wider machine the score plugs into: routing rules, MQL-to-SQL handoff SLA, BANT or MEDDIC capture, and the dashboards that catch a broken queue. | Leads are scored but still leak between marketing and sales. It builds a starter score itself. |
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Common questions about the Lead Scoring Agent
What signals should a B2B lead scoring model include?
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The Artemis model uses four signal categories. Firmographic fit (size, revenue, industry) capped at 20 percent, because fit alone does not indicate intent. Demographic (title, seniority), useful for routing but limited as a buying signal. Behavioral (page visits, demo requests, return-to-pricing), the strongest imminent-purchase predictor. And pain (recent funding, job changes in target roles, technology installed, metric downturns) weighted 60 to 80 percent, because pain-driven leads close 3 to 5 times faster than firmographic-only fits. Directional, from our audits and industry benchmarks, not a controlled study.
How does the Pain-First framework differ from standard lead scoring?
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Standard lead scoring weights firmographic fit equally with behavior, which means a Fortune 500 visiting your blog scores higher than a Series A founder researching pricing for the third time this week, backward. Pain-First weights pain signals 60 to 80 percent and firmographic at 20 percent, with hard disqualifiers validated against closed-won and closed-lost outcomes. This agent ships the Pain-First rubric as a template plus playbook layer. Defining the full pain-point ICP underneath it is the deeper ICP Definition engagement, and the advisor will tell you during discovery if your ICP needs that build first.
Does it work with HubSpot, Salesforce, or both?
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Both. The agent ships CRM property exports for HubSpot (Marketing Hub Professional or Enterprise) and Salesforce (Sales Cloud, with or without Einstein). The Claude orchestration branches during discovery to use the platform you have. If you run both CRMs, the agent builds parallel models and recommends a single source of truth.
What does the Lead Scoring agent cost?
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Lead Scoring is $349 one-time. You own the agent forever: the scoped build engagement, the Pain-First rubric and playbook, the CRM property exports, the validation and A/B plan, and the tuning routines it installs. There is no subscription required to keep using it. Aegis membership, $1,200 per year across every agent you own, is optional and only keeps it current.
Does this agent run on OpenAI Codex, or only Claude?
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Both. Every Artemis agent ships a verified OpenAI Codex edition in the same download, alongside the Claude edition for Claude Chat and Claude Code. Buy once and run the engagement on whichever assistant your team already uses. The Codex edition is a purpose-built port for the Codex CLI, not a copy-paste, with scheduling and engagement state handled natively per platform.
Read How to Implement Lead Scoring See pricing, bundles, and Aegis membership
What you get when you pay, in seconds, not setup calls.
No new dashboard, no download to babysit. A license key drops into the engineer you already installed, and Lead Scoring Agent lights up. Its node turns gold in your constellation the moment the key resolves.
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