Enterprise Security

AI proxies are infrastructure layers focused on request routing and cost optimization. They have no memory, no workflows, and no execution capability. Call Stream AI is a secure AI execution platform that controls workflows, decisions, and outcomes with context-aware security, function-level validation, conversation memory, and full business logic execution.

Call Stream AI uses true multi-provider orchestration across OpenAI, Anthropic, and Google Gemini. Unlike AI proxies that route based on rules or cost, our platform uses function calling and workflow context to select the optimal model for each task. Model selection is part of execution, not a separate routing decision, providing both performance and redundancy.

Enterprise APIs from OpenAI, Anthropic, and Google Gemini provide production-ready reliability with high uptime SLAs, built-in compliance (SOC 2 aligned), state-of-the-art model quality, native function calling support, and continuous automatic updates. This eliminates the significant security, operational, and maintenance burden of self-hosting open source models while delivering higher baseline performance.

Call Stream AI is designed with SOC 2 alignment across all five Trust Service Criteria: Security, Availability, Processing Integrity, Confidentiality, and Privacy. Every component in our stack, from Twilio to AWS and Google Cloud, is SOC 2 compliant or aligned, and our platform enforces controls including RBAC, workflow validation, function-level permissioning, and full audit logging.

Unlike AI proxies that have limited or no awareness of PII, Call Stream AI provides context-aware handling of PII within workflows, data segmentation by client through multi-tenant isolation via Supabase Row-Level Security, controlled exposure of sensitive fields, and the ability to restrict AI responses based on data classification. All interactions involving PII maintain full audit trails.

Call Stream AI maintains alignment with SOC 2 Trust Service Criteria, TCPA for consent-based communications, GDPR for data privacy, PCI DSS for payment data, and CASL. AI proxies have no native understanding of consent or data usage rules and cannot enforce privacy policies at the workflow level. Call Stream AI enforces these at every step of execution.

Centralized AI proxy layers become high-value attack targets due to aggregation of API keys and traffic. Recent incidents involving tools like LiteLLM highlighted these vulnerabilities. Call Stream AI has no external AI proxy dependency. Our native cascading architecture integrates directly with LLM providers, eliminating this entire attack vector.

Call Stream AI operates on a distributed, multi-cloud architecture: Twilio for communications, Render for frontend delivery, Supabase (PostgreSQL) for data with Row-Level Security, AWS and Google Cloud for infrastructure redundancy, and GitHub for secure CI/CD pipelines. This distributed model eliminates single points of failure and reduces the blast radius of any potential compromise.

Call Stream AI follows the NIST AI Risk Management Framework (U.S. standard for trustworthy AI), the OECD AI Principles (global guidelines for responsible AI), and the Partnership on AI (PAI) Guidelines (industry best practices for fairness and accountability). These frameworks guide our approach to transparent, human-centered AI deployment.

Yes. Call Stream AI enforces strong multi-tenant data isolation through Supabase Row-Level Security (RLS), ensuring each client's data is segmented at the database level. Combined with RBAC at the application layer and controlled data access within workflows, sensitive information is protected with restricted, auditable access across the entire platform.

Call Stream Verify is a cryptographic integrity and audit layer applied to every finalized call record. Each finalized call is canonicalized into a deterministic payload and converted into a SHA-256 hash, which acts as a verification reference stored in an immutable version record. Every event is also written to an append-only, hash-linked audit chain, so any modification — to a record or to history — is detected the next time integrity is recomputed. This delivers tamper-evident records, deterministic recomputation, and full audit traceability without the overhead of a blockchain.