ApexAgent Systems
Technical Architecture & Commercial Due Diligence Memo
1. Executive Problem Statement & Architectural Thesis
First-generation autonomous CX platforms (e.g. Sierra.ai, Decagon, Intercom Fin) have introduced severe operational friction for enterprise software companies and high-volume e-commerce brands:
Vendors charge $2.50 to $4.50 per automated resolution—a 3,500% markup over underlying token compute costs, punishing brands for scaling containment.
Proprietary SaaS clouds store customer transcripts and PII in vendor databases, creating data processing liabilities and security review friction.
Relying on system prompts to obey financial rules results in jailbreaks and unauthorized refunds. Business policies must be mathematically compiled.
2. Architectural Due Diligence Matrix
| Dimension | ApexAgent (Open Core) | Sierra.ai | Decagon | Legacy Chatbots (Zendesk) |
|---|---|---|---|---|
| Resolution Fee | $0.00 / resolution | $2.50 – $4.50 / res | $1.80 – $3.20 / res | $1.50 per add-on |
| Deployment Model | Self-Hosted VPC or Dedicated Cloud | Proprietary Closed Cloud | Proprietary Closed Cloud | Multi-Tenant SaaS |
| Policy Enforcement | Two-Stage Python Code Guardrails | Supervised Prompt Chains | Fine-tuned LLM Probabilistic | Hardcoded Static Trees |
| External Pip Dependencies | 0 (Python 3.9+ Stdlib Only) | Unknown / Proprietary | Complex Node/Python Stack | Heavy Enterprise Monolith |
| Policy Financial Simulator | Built-In Pre-Flight Backtester | None (Test in Production) | None | None |
| Data Sovereignty & ZDR | Guaranteed Zero-Data Retention | Vendor Managed Storage | Vendor Managed Storage | Vendor Cloud Storage |
3. Technical Security & Data Governance Blueprint
All customer intents undergo two-tier validation. Pre-guardrails evaluate tokens for prompt injection or jailbreak patterns before any LLM inference. Post-guardrails mathematically evaluate tool parameters (e.g. strict enforcement of max autonomous refund ceilings $\le \$50.00$) in pure code.
Enterprises can deploy ApexAgent as an isolated single-tenant service within their existing AWS or GCP VPC. Database records reside in local WAL-mode SQLite or customer-managed PostgreSQL. Customer PII is never pooled into a shared multi-tenant database.
Model inference is routed through enterprise endpoints configured with Zero-Data Retention (ZDR). Customer transcripts are never used to train public foundation models. Automatic regex filters sanitize payment card numbers and phone numbers prior to LLM submission.
Every tool invocation, policy pass/fail status, and latency metric is written to an immutable audit log exportable to CSV on demand. We sign standard mutual enterprise NDAs prior to any customer sandbox configuration or data integration.
4. Financial Containment & Unit Economic Model
For an organization handling 35,000 monthly tickets with a fully burdened human agent wage of $28/hour (10 minutes average handling time):
5. 14-Day Zero-Risk Technical Pilot Framework
Mutual NDA execution, domain whitelisting, and policy guardrail rule configuration.
Automated evaluation against 100 historical enterprise support tickets to achieve $\ge 99.0\%$ adherence.
A/B test alongside existing human workflows with live Voice of Customer (VoC) sentiment tracking.