SignalCore AI — Your Pipeline, With a Brain
v3.2 · Model Online · Latency 9ms

Your pipeline, with a brain.

SignalCore watches every public signal your buyers leave behind — job moves, funding rounds, content engagement — and scores who's ready to talk, before they ever raise their hand.

Deploy the Model → Read the Architecture
inference · live streaming
SIGNALS ATTENTION SCORE
accuracy 98.6% loss 0.0042 tokens/sec 2.4k
01 Training Data

Learned from 1.2M real signals

No synthetic noise. The model was trained on actual buyer behavior — the posts they read, the replies they wrote, the moments they switched vendors.

corpus
1.2M
Signals ingested monthly across LinkedIn, X, and public funding data
labels
42k
Buyer conversations human-annotated to teach intent, not just engagement
accuracy
98.6%
Intent classification accuracy on held-out data, validated weekly
latency
9ms
Median alert latency from signal event to your notification
02 Model Architecture

Four layers deep, zero guesswork

A transformer-style pipeline that reads behavior the way your best rep reads a room — attention-weighted, context-aware, and fast.

architecture · layerssignal-core-3
INPUT HIDDEN x2 OUTPUT
in 4 features · hidden 5 × 2 attention layers · out 3 intent classes
A

Attention Layer

Weights each signal by context — a job change during a funding round matters more than either alone.

mechanism: multi-head attention
B

Behavioral Embeddings

Every account becomes a vector that drifts as intent builds. Drift direction is the tell.

dim: 768 · refreshed hourly
C

Intent Scoring

A 0–100 readiness score per account, calibrated against 42k annotated conversations.

threshold: > 72 = alert
D

Anomaly Detection

Flags behavioral spikes that look like a buying committee forming — three departments, one week.

precision: 94.1%
03 Inference Pipeline

From raw signal to actionable alert

Four stages, nine milliseconds, zero human babysitting. The flow below animates continuously — that's your data moving.

1

Capture

Millions of public events stream in — posts, follows, job changes, funding filings.

2.4k events/sec
2

Enrich

Raw events attach to accounts, personas, and buying-stage context in real time.

+38 fields
3

Score

The model re-scores affected accounts with full attention context, not keyword rules.

9ms median
4

Alert

Readiness > 72 fires a Slack alert with the exact hook that earned the conversation.

p95: 22ms
04 Benchmarks

Trained against the old way

Evaluated head-to-head against legacy keyword-rule scoring on the same 12-month corpus. The bars fill on scroll.

Intent accuracy
signal-core vs rules
94%vs 61% legacy
Alert latency
signal-to-notification
9svs 3.1h legacy
Reply lift
touch timing
3.1×vs 1.2× legacy
False positives
noise per 1k alerts
-72%noise reduction
Deal influence
attributed pipeline
+189%attributed pipeline
05 Deploy

One API call from a smarter pipeline

Spin up your model instance, stream account scores, and wire alerts to the tools your team already lives in.

signalcore — deploy 
$ curl -X POST https://api.signalcore.ai/v1/score \
  -H "Authorization: Bearer sk_live_..." \
  -H "Content-Type: application/json" \
  -d '{
    "account_id": "acct_8812",
    "window_days": 30
  }'

> 200 OK // 9ms
{
  "readiness": 87,
  "signal": "hiring_spike + funding_round",
  "hook": "Congrats on the round — the team you're adding is exactly who we help."
}
Get Your API Key → Read the Docs