Detect early signs of crisis, child grooming, fraud, and targeted violence in milliseconds—without mass surveillance, profiling, or storing raw user data.
Enterprise platforms cannot afford the liability of mass user surveillance. PegBit Anima operates on mathematical evidence without storing plain-text identities or running intrusive profiling.
Every user ID, phone number, and wallet address is salted with a tenant-private secret and replaced with a non-invertible HMAC-SHA256 digest before reaching our storage. Pegbit never holds raw PII.
Sub-30ms ONNX neural embeddings map text into a 384-dimensional latent space. The engine calculates the velocity of negative emotional drift over a rolling 7-day window, distinguishing theological lament from imminent suicide intent.
Participating institutions share cryptographic fingerprints of confirmed scam wallets, fraudulent phone numbers, and predator accounts via PSI blind queries without pooling sensitive user records.
Live WebSocket audio streaming calculates RMS dBFS to detect screams and distress spikes, while 64-bit difference perceptual hashing (dHash) flags near-duplicate illicit imagery at scale.
Regulatory 15-minute response deadlines are monitored automatically. Overdue incidents escalate roles to SECURITY_ADMIN and dispatch signed webhooks directly to Slack Block Kit, PagerDuty, or Splunk.
Every decision, triage step, and role handover is appended to an immutable SHA-256 chained ledger. Export verifiable compliance dossiers ready for insurer audits, court discovery, or law enforcement.
Switch domain rules instantly with dynamic tenant policies without redeploying code or taking server downtime.
Targeted violence triad detection (weapon + location + time), minor boundary auditing (secrecy, isolation, off-platform steering).
Contextual violence classifier suppresses false-positives for game terminology ("destroy squad in next match") while isolating genuine hate speech.
De-obfuscates spaced text and emojis to isolate fraudulent crypto addresses, cashtags, and elder financial coercion before wire release.
Measures accelerated distress curves over 7-day rolling states and triggers instant 988 Crisis Lifeline intervention modals.
Engineered for sub-30ms p95 latency, infinite multi-tenant horizontal scaling, and dual PostgreSQL/SQLite multi-dialect portability.
Submissions, chat dialogue turns, PCM audio chunks, image bytes
Zero-width stripping, leetspeak, homoglyphs, keyed HMAC pseudonymization
384d FastEmbed centroids, despair velocity, boundary grooming audit, 64-bit dHash
Blind query matching across cross-platform scam indicators with zero PII leakage
15m SLA countdown, Slack/PagerDuty/Splunk webhook, SHA-256 chained audit
Built to satisfy corporate legal counsel, institutional insurers, and international safeguarding legislation.
Pegbit strictly complies with the EU AI Act ban on predictive pre-crime profiling. The engine assesses concrete behavioral content only, with mandatory human-in-the-loop closure for all critical cases.
Statutory data minimization: automated background purge redacts plain-text submissions past the retention horizon (e.g. 90 days), while preserving immutable cryptographic hashes for audit proof.
Built-in playbooks and standardized court-admissible dossiers (/compliance-dossier) mapped to UK Online Safety Act duty-of-care and US 18 U.S.C. 2258A reporting requirements.
Drop-in SDKs with automatic retry mechanisms, strict typing, and HMAC signature verification.
from pegbit_sdk import PegbitClient # 1. Initialize client with tenant credentials client = PegbitClient(api_key="pbk_prod_...", tenant_id="tenant_enterprise_01") # 2. Evaluate submission in real-time (<30ms) verdict = await client.evaluate( text="Urgent user message...", user_id="user_anon_99" ) if verdict.decision_band == "D3_URGENT_SAFEGUARDING": await trigger_immediate_safeguarding_escalation(verdict)
Test our pre-configured threat scenarios across EdTech, Gaming, Fintech, and Healthcare in our friction-free interactive sandbox.