How is this different from HubSpot or Salesforce predictive scoring?

Native predictive scoring reads structured CRM data — firmographics, page views, email opens. The Arahi agent adds the unstructured signal those models miss: actual reply text, product usage, public news, and freshly-changed firmographic events (a lead's company just hired a CMO, or just funded). It also writes a one-line rationale per score so reps don't have to guess why the score moved.

Can I customize the scoring rubric?

Yes — describe it in plain English: "Fit is high if the company has 50–500 employees, in SaaS or ecommerce, US/EU. Intent is high on pricing-page visit, replies asking for a demo, or three product logins in a week." The agent applies it consistently and re-applies as signals change. Update the rubric anytime; the agent re-scores the back catalog.

How does it handle the difference between fit and intent?

Two separate scores, on purpose. A high-fit / low-intent lead is a marketing nurture target. A low-fit / high-intent lead is a fast no — politely. A high-fit / high-intent lead routes to sales same-day. Conflating them into a single number, like most legacy scoring, hides the routing signal.

Free for 1,500 actions/month — enough to score about 500 leads with full enrichment. Paid plans start at $49/mo for unlimited connections; Growth at $149/mo covers most B2B sales teams; Pro at $349/mo for high-volume inbound.

Does it replace my SDR team?

No. Scoring is the triage layer — it tells SDRs which leads to work first and writes the rationale into the CRM so they don't waste a discovery call asking what's already known. Most teams report SDRs do 30–40% more discovery calls in the same week because the queue is ordered correctly.

AI Lead Scoring That Reads More Than the Form Fields

AI lead scoring ranks the leads you already have — by fit, intent, and current sales-readiness — so the team works the inbox top-down instead of first-in-first-out. The Arahi agent reads firmographic enrichment, behavioral signals from your product and site, and the actual reply text on cold-email and inbound replies, then writes a numeric score plus a one-line rationale to Salesforce or HubSpot. Scoring is recalculated continuously as new signal arrives, so a quiet lead that just opened your pricing page three times this morning surfaces before stand-up.

Get Started View the BDR Agent

Real-time — Re-scoring: New signal in, score updated — no nightly batch lag.
Fit + intent — Two-axis score: Both dimensions, separately, so triage routes match the right play.
1-line — Rationale per score: Why the lead landed there, written into the CRM record.
10 min — Setup time: Plain-English rubric, no flow builder.

What an AI lead scoring agent does

Six jobs that turn a flat lead list into a prioritized queue your team actually works in order.

Enriches every new lead: Pulls firmographic data — company size, industry, funding, tech stack, growth signals — from Clearbit, Apollo, or your enrichment provider and writes it back to the lead record. Adds public LinkedIn role and tenure for the contact.
Reads behavioral signals: Pulls product usage (logins, feature touches, trial activity), website visits (pricing, demo, docs), and email engagement. Behavior weight is configurable per signal — not a pre-baked Marketo formula.
Reads reply text, not just opens: When a lead replies to a cold email or marketing send, the agent reads the actual text — "send pricing" scores higher than a polite "thanks, not now." Most scoring models miss this because they only see the open/click event.
Writes a fit + intent score with rationale: Two scores, not a single conflated number — fit (do they look like our ICP?) and intent (are they buying now?). Each gets a one-line rationale written to the CRM record so the AE knows why the lead is hot.
Routes hot leads instantly: When a lead crosses your threshold, the agent assigns the right AE based on territory or round-robin, books a calendar slot if the lead asked, and posts a Slack ping. No lead waits 4 hours for the next round-robin run.
Recycles cold leads as signal changes: Re-scores the back catalog continuously. A lead that went cold six months ago and just raised a Series B this week gets re-flagged — same record, new score, AE notified.

Connects to the data your scoring already runs on

Native connectors for the tools the scoring rubric reads from and writes to. The agent runs on top of Arahi's 1,500+ app library if your stack includes anything else.

CRMSalesforce: Reads lead, contact, opportunity history; writes fit and intent scores plus rationale to fields you specify.
CRMHubSpot: Two-way sync — reads contact and deal state, writes scoring, triggers list-based workflows on threshold cross.
EnrichmentClearbit / Apollo: Pulls firmographics and LinkedIn role/tenure for every new lead within seconds of capture.
NotificationSlack: Posts hot-lead pings to the right AE's channel with rationale and CRM deep link.

Lead scoring vs lead generation — separate jobs, easy to confuse

Lead generation and lead scoring sit on opposite ends of the same funnel and need different tools. Both are real, both matter, but you don't substitute one for the other.

Question	Lead generation	AI lead scoring
Funnel stage	Top of funnel — finding leads	Mid-funnel — ranking the leads you already have
Job to be done	Source new contacts via outbound, content, paid	Decide which contacts your team works first
Inputs	ICP definition, channels, paid budget	Firmographics, behavior, reply text, deal history
Outputs	New rows in the CRM	A score and rationale per existing row
Replaces	BDR / SDR sourcing work	Manual triage in the CRM each morning
Arahi page for this	/use-cases/lead-generation	/ai-agent/lead-scoring (you're here)

Adjacent agents and pages

Cross-linkAI Sales Representative: Once a lead clears the scoring threshold, the full-cycle sales agent picks it up — outbound, qualification, demo booking, follow-up.
Pre-built agentBDR AI Agent: Pre-built outbound BDR — sources prospects, writes the cold email, follows up. The upstream of lead scoring.
Adjacent use caseLead Generation: Top-of-funnel sourcing strategies and the Arahi agents that run them. The other end of the same funnel.

Frequently asked questions

How is this different from HubSpot or Salesforce predictive scoring?: Native predictive scoring reads structured CRM data — firmographics, page views, email opens. The Arahi agent adds the unstructured signal those models miss: actual reply text, product usage, public news, and freshly-changed firmographic events (a lead's company just hired a CMO, or just funded). It also writes a one-line rationale per score so reps don't have to guess why the score moved.
Can I customize the scoring rubric?: Yes — describe it in plain English: "Fit is high if the company has 50–500 employees, in SaaS or ecommerce, US/EU. Intent is high on pricing-page visit, replies asking for a demo, or three product logins in a week." The agent applies it consistently and re-applies as signals change. Update the rubric anytime; the agent re-scores the back catalog.
How does it handle the difference between fit and intent?: Two separate scores, on purpose. A high-fit / low-intent lead is a marketing nurture target. A low-fit / high-intent lead is a fast no — politely. A high-fit / high-intent lead routes to sales same-day. Conflating them into a single number, like most legacy scoring, hides the routing signal.
What does it cost?: Free for 1,500 actions/month — enough to score about 500 leads with full enrichment. Paid plans start at $49/mo for unlimited connections; Growth at $149/mo covers most B2B sales teams; Pro at $349/mo for high-volume inbound.
Does it replace my SDR team?: No. Scoring is the triage layer — it tells SDRs which leads to work first and writes the rationale into the CRM so they don't waste a discovery call asking what's already known. Most teams report SDRs do 30–40% more discovery calls in the same week because the queue is ordered correctly.

Stop working leads in the order they came in.

Connect your CRM, set the rubric in plain English, ship a working AI lead scoring agent in 10 minutes.

Lead scoring is upstream of lead qualification and enrichment

These industry-specific guides cover the related-but-distinct workflows downstream of scoring. Same agent, different cuts of the same data.