AI lead qualification: the pipeline pattern that 5x’d SDR productivity

Lead qualification is the AI use case with the clearest ROI in B2B. Here is the pipeline pattern we ship as part of AI integration work, with the LLM call points specified.

The three LLM call points#

1. At form submit: enrich and score. Pull data from Clearbit/Apollo, ask the model to score fit against your ICP definition (5-line prompt, returns 0-100). Lead lands in CRM with a Quality Score field.
2. At first reply: classify intent. Buyer replies to nurture email. Model classifies: meeting-ready, more-info-needed, not-fit, unsubscribe. Route accordingly.
3. Pre-meeting: brief generation. The morning of the meeting, a brief lands in Slack: 3-bullet company summary, recent news, predicted objections. SDR walks in prepared.

Where humans stay in the loop#

The model never sends a reply on behalf of the SDR. It drafts, the SDR reviews, the SDR sends. The 80% time saving is in the draft, the 20% human time is the trust layer.

Results from a recent build#

A B2B SaaS client doing 1,200 inbound leads/mo, 4 SDRs. Pre-AI: SDRs spent ~30 min per qualified lead (research, brief, follow-up). Post-AI: 6 minutes. The SDR team now handles 5x the volume; the second SDR hire was cancelled.

What it cost#

4-week build, $18k all-in (CRM workflow + n8n orchestration + OpenAI/Claude routing). Monthly ops cost: $140 in model calls + $39/mo for n8n cloud.

Our AI integration engagements often start with the inbound lead funnel since payback is fastest. Send us your funnel volume for a quick ROI estimate.