Sarah OS is not just another chatbot. It is a high-assurance autonomous terminal designed to bridge the gap between Large Language Models and the strict regulatory requirements of the financial industry. Here is the lifecycle of a secure, compliant interaction.
Calls are routed through a Cloudflare mTLS (mutual TLS) tunnel, ensuring that the connection between the PSTN and the local inference engine is authenticated and invisible to the public internet.
The system verifies the "Mission Authorization" and Campaign Signature before the first audio frame is processed. No call proceeds without cryptographic validation.
Using real-time Fast Fourier Transform (FFT) analysis, Sarah calculates the fundamental frequency (F0) of the caller. If a minor's voice is detected (>260Hz), the system executes an autonomous terminal exit to prevent unauthorized third-party disclosure.
The system monitors RMS energy and pitch spikes to identify customer hardship or escalation, allowing the AI to adjust its strategy in real-time. Progressive agitation triggers automatic supervisor transfer.
MINOR (terminal exit) |
230-260Hz = UNCERTAIN (age verification prompt) |
< 230Hz = ADULT (proceed)
Sarah utilizes a dual-model "Maker-Checker" architecture to eliminate hallucinations. No financial figure, no legal disclosure, and no customer data reaches the caller without deterministic verification.
A LoRA fine-tuned Llama-3 model generates a natural language response based on the conversation history and collection script. The model has been trained on 4,219 examples including corrective failure cases from adversarial testing.
Before a single word is spoken, a secondary verification model intercepts the generated text. It extracts critical tokens — balance amounts, dates, SSN fragments, card numbers — and validates them against a secure, local SQLCipher database.
If the Checker finds even a one-cent discrepancy between the AI's "thought" and the database's "truth," the audio bridge is interdicted by a hardware-bound circuit breaker. The incorrect response is replaced with a safe fallback before TTS synthesis.
_check_currency() → _check_identity() →
_check_dates() → _check_account() →
PASS: Synthesize | FAIL: Circuit BreakBased on the legal status of the call, Sarah autonomously shifts her vocal "texture." In professional disclosure phases, she utilizes a stable, 1.1x speed. In hardship discovery phases, she shifts to "Human Mode," adjusting pitch and breathiness to foster trust.
Native phonetic maps ensure that complex legal disclosures and financial terms are pronounced with 100% accuracy across multiple supported languages: English, Spanish, French, Hindi, Malayalam, and Mandarin.
Professional — Speed: 1.0x | Noise: 0.667 | Stable pitch (Disclosure, Verification, PTP)Human — Speed: 1.1x | Noise: 0.75 | Warm, breathy (Hardship, De-escalation)Supervisor — Speed: 1.15x | Noise: 0.5 | Authoritative (Post-transfer, Michael Torres)
Every conversational turn, including the raw model tokens and the synthesized audio output, is hashed (SHA-256) and cryptographically chained to the previous turn. Any modification to any turn invalidates the entire chain downstream.
The ledger is watermarked with the physical serial number of the Apple M3 Pro hardware. This creates a legally defensible "Examiner Pack" that proves exactly what was said, when it was said, and which deterministic logic authorized it.
SHA-256(metadata + prev_hash) → chained →
Turn N+1: SHA-256(metadata + turn_n_hash)