We deploy AI on your infrastructure. Zero data routes to public clouds. Zero data trains external models. Here's exactly how we protect your business.
We deploy open-weight models — Mistral, LLaMA, Qwen, and others — entirely within isolated, local server environments on your premises or on dedicated edge hardware that you control.
Your PDFs, Excel data, transcribed audio, and inventory images never leave your internal network perimeter. No data is routed to external cloud providers, API endpoints, or third-party model vendors.
This architecture eliminates the risks associated with public LLM services: unconsented data usage for model training, data leaks, and regulatory violations.
100–300ms local vs. 500–1000ms cloud round-trips. Critical for real-time conversation monitoring.
Fixed infrastructure cost vs. unpredictable pay-per-token cloud pricing that scales with volume.
Automations continue running during internet outages, API downtimes, or vendor disruptions.
Models trained on your specific jargon, product catalogs, and operational parameters using RAG.
Our security posture follows SOC 2 principles even as we work toward formal certification.
AES-256 encryption for stored data. TLS 1.3 for all internal communications between system components.
Granular permissions. Owners, managers, and staff see only what their role requires. All access is logged.
Every data access, model query, and configuration change is logged with timestamps and user identity.
You define how long data is stored. Automatic purge schedules ensure nothing persists beyond your window.
Your data is used exclusively to produce your results. It is never contributed to external AI model training.
Documented procedures for breach detection, containment, notification, and recovery with defined timelines.
We align our practices with established security and privacy standards.
Our AI deployment practices follow the NIST AI Risk Management Framework for trustworthy, accountable AI systems.
Data minimization and disassociated processing — process locally where possible, limit observability and linkability.
Collection, use, and retention are limited to what is reasonably necessary and proportionate to the purpose.
Because our conversation intelligence product processes audio from business environments, we take consent, notice, and compliance extremely seriously.
Texas is a one-party consent state for audio recording. Our deployment guidance addresses both state and federal requirements, including reasonable expectation of privacy considerations. We recommend consulting with legal counsel for your specific jurisdiction and use case.
We only retain what's needed to produce your alerts and reports. You choose retention windows. You can run models locally when policy requires it. We collect only what we need, keep it safe, and dispose of it securely.
We're happy to walk through our architecture, controls, and compliance posture in detail.