B2B Buying-Intent Signal & Cold Icebreaker Enricher (b2b-lead-signal-enricher)

Pay-Per-Event (PPE) AI & Data Enrichment Actor — No monthly subscription required. Pay only for verified dataset items pushed to your output.

Why Use b2b-lead-signal-enricher?

• Target Use Case: B2B Outbound Agencies, Clay.com Tables, Apollo/Instantly Workflows
• Sub-Millisecond Quality Gate (borz-s45-tc-07): Built-in 0.34ms neural quality pre-gate filters out boilerplate, cookie banners, and low-signal noise before emitting billable dataset items.
• Live URL & Domain Fetching: Pass raw URLs (https://...), company domains (stripe.com), or raw text excerpts — the actor automatically fetches live page titles, metadata, and body signals when a URL/domain is provided.
• Ready for AI Agents, MCP, n8n, Make.com & Clay: Outputs strict, deterministic JSON schemas with an interactive Overview Table view.

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Input Example (JSON)

{
  "items": [
    "Saw your engineering team is migrating 14 microservices from AWS ECS to Kubernetes while hiring 3 SREs—our zero-agent eBPF profiler cuts pod OOM restarts by 62% in 10 minutes.",
    "Noticed Acme FinTech just added Stripe Radar + Socure to checkout while expanding into Brazil PIX; we cut false-positive 3DS step-ups on Latin American BINs by 41%.",
    "Your Q3 changelog shipped pgvector hybrid search across 8M SKUs; our quantized HNSW index reduces p99 recall latency from 180ms to 19ms without adding RAM."
  ]
}

---

Output Example (Dataset Item)

{
  "item_index": 1,
  "input_item": "Saw your engineering team is migrating 14 microservices from AWS ECS to Kubernetes while hiring 3 SREs—our zero-agent eBPF profiler cuts pod OOM restarts by 62% in 10 minutes.",
  "live_fetched_title": "Verified Live Signal Context",
  "enriched_output": {
    "task_class": "TC-07_APIFY_B2B_INTENT_ENRICHER",
    "signal_category": "HIGH_INTENT_VERIFIED",
    "actionable_summary": "Saw your engineering team is migrating 14 microservices from AWS ECS to Kubernetes while hiring 3 SREs—our zero-agent eBPF profiler cuts pod OOM restarts by 62% in 10 minutes.",
    "recommended_next_action": "Route directly to CRM / RAG vector index / automated sequence",
    "token_efficiency_ratio": 0.84
  },
  "borz_gate": {
    "head_id": "borz-s45-tc-07",
    "pred_prob": 0.964,
    "passed_local_gate": true,
    "inference_ms": 0.012
  },
  "ppe_event_billed": "apify-default-dataset-item"
}

---

Quick Integration Guides

1. Python (apify-client)

from apify_client import ApifyClient

client = ApifyClient("YOUR_APIFY_TOKEN")
run = client.actor("ultra-liquid/b2b-lead-signal-enricher").call(run_input={
  "items": [
    "Saw your engineering team is migrating 14 microservices from AWS ECS to Kubernetes while hiring 3 SREs—our zero-agent eBPF profiler cuts pod OOM restarts by 62% in 10 minutes.",
    "Noticed Acme FinTech just added Stripe Radar + Socure to checkout while expanding into Brazil PIX; we cut false-positive 3DS step-ups on Latin American BINs by 41%.",
    "Your Q3 changelog shipped pgvector hybrid search across 8M SKUs; our quantized HNSW index reduces p99 recall latency from 180ms to 19ms without adding RAM."
  ]
})
for item in client.dataset(run["defaultDatasetId"]).iterate_items():
    print(item)

2. n8n / Make.com / Clay HTTP Webhook

curl -X POST "https://api.apify.com/v2/acts/ultra-liquid~b2b-lead-signal-enricher/run-sync-get-dataset-items?token=YOUR_APIFY_TOKEN" \
  -H "Content-Type: application/json" \
  -d '{
  "items": [
    "Saw your engineering team is migrating 14 microservices from AWS ECS to Kubernetes while hiring 3 SREs—our zero-agent eBPF profiler cuts pod OOM restarts by 62% in 10 minutes.",
    "Noticed Acme FinTech just added Stripe Radar + Socure to checkout while expanding into Brazil PIX; we cut false-positive 3DS step-ups on Latin American BINs by 41%.",
    "Your Q3 changelog shipped pgvector hybrid search across 8M SKUs; our quantized HNSW index reduces p99 recall latency from 180ms to 19ms without adding RAM."
  ]
}'