TECHNICAL ARCHITECTURE OVERVIEW
Infrastructure for AI that runs inside the Americas, not beside it.
Glápagos is the deployment platform behind the AI Corridor of the Americas. A modular DevOps stack for orchestrating models, agents, and data across jurisdictions.
01 — PLATFORM ARCHITECTURE
The platform separates control plane and data plane. The control plane manages tenant configuration, policy enforcement, and orchestration state; the data plane handles inference traffic, retrieval, and agent execution, scaled independently from control-plane logic. An AI orchestration layer routes requests through a model gateway that abstracts provider-specific model APIs behind a unified interface — authentication, rate limiting, and fallback routing included. An agent runtime executes multi-step agent processes as durable workflows, coordinated by a workflow engine that persists execution state across retries. Traffic enters through an API gateway; internal components communicate asynchronously through an event-driven messaging layer. A vector store supports retrieval, a knowledge graph supports structured entity reasoning, and an observability layer instruments every request with tracing, logging, and performance metrics.
# Specific technology selections within each layer are withheld from this overview and available for technical review under NDA.
Control plane, data plane, and the layers between them
02 — AI INFRASTRUCTURE
Hybrid, sovereign, and multi-cloud by design
Deployment supports hybrid, sovereign, and multi-cloud environments through a common container orchestration abstraction, spanning managed cloud infrastructure and on-premises deployment where a jurisdiction requires it. Workload scheduling accounts for accelerator availability, and edge inference is supported for latency-sensitive use cases at the regional level. The platform scales on load and provides high availability and disaster recovery across zones and regions. Specific cloud partners, accelerator classes, and failover configurations are confidential and, for sovereign deployments, may be subject to government classification.
03 — DATA FABRIC
Federated by default, not centralized by convenience
The platform supports federated data access — queries execute against source systems without requiring centralization of raw data, in support of jurisdictional residency requirements. A metadata catalog tracks schema, ownership, and lineage across datasets and derived artifacts, including transformations applied during retrieval-augmented generation. Both structured and unstructured data are supported: unstructured content is indexed for semantic retrieval, structured data is queried natively, and streaming ingestion keeps the retrieval index current in near real time. Specific ingestion pipelines and data-partner integrations are confidential.
04 — AI GOVERNANCE
Policy evaluated at request time, not hardcoded in application logic
Access, data-handling, and model-approval rules are defined as policy-as-code and evaluated per request. A model registry tracks versions, evaluation status, and deprecation; prompt templates are versioned alongside the model they were validated against.
RBAC and Zero Trust network principles govern service-level access. Encryption applies at rest and in transit, with PII detection screening data before it reaches a model. High-risk actions route through human approval, and every governance decision is logged to an immutable audit trail.
# Cryptographic standards, policy-engine implementation, and audit infrastructure are confidential and available under NDA.
05 — AI OPERATIONS (AIOPS / LLMOPS)
Continuous evaluation in production, not just at launch
The platform continuously evaluates production model behavior, including checks for factual grounding against retrieved sources. Output guardrails enforce schema and content constraints before responses reach downstream systems. Prompt and model versions are managed under change control, released via canary rollout, and rolled back automatically on latency or error-rate regression. Requests route among approved models by cost, latency, and task type. Specific evaluation methods, detection thresholds, and routing criteria are confidential.
06 — ENTERPRISE INTEGRATION
Standards-based, credential-vaulted, no secrets in application code
The platform exposes REST and GraphQL APIs, webhook subscriptions, and standards-based single sign-on (OAuth, SAML) for identity provider integration. Native integration is supported for common ERP, CRM, ITSM, productivity, and data-platform environments. Credentials are managed through the platform's own vault rather than embedded in client applications. Specific integration partners and connector implementations are disclosed case-by-case under NDA.
07 — TECHNICAL DIFFERENTIATORS
Why this is difficult to replicate by wrapping a single model API
A conventional LLM wrapper calls one provider's endpoint from one region and treats deployment topology as a setup-time decision. The differences below are properties of the orchestration layer itself — evaluated per request, at runtime — not configuration choices made once and left alone.
Cross-border orchestration. Residency policy is evaluated against the requesting tenant's jurisdiction on every call, and the same workflow definition compiles to a different execution path depending on where the request originates. A wrapper that hardcodes a region at integration time cannot reproduce this without rebuilding the integration for each jurisdiction; here, adding a jurisdiction is a policy entry, not a new code path.
Sovereign deployment. Most SaaS AI platforms keep the control plane centralized and let a customer host data locally at best — policy evaluation, model versioning, and audit logging still call back to a vendor-controlled service. Here, the control-plane components themselves are packaged to run standalone inside the customer's infrastructure boundary, so policy enforcement and audit logging do not depend on any external call to function.
Multi-model orchestration. A wrapper typically binds one model provider to one integration point, so changing models means re-integrating downstream logic. Model selection here is a per-step property of the workflow definition, resolved by the orchestration layer at runtime against cost, latency, and task type — different steps of one workflow can call different providers without any change to calling code.
Compliance-aware routing. Because residency and access rules live in the routing layer rather than in application code, regional infrastructure placement and policy-driven deployment are the same mechanism applied twice, not two separate systems. This is also why compliance can be audited as a routing log rather than reconstructed after the fact from application behavior.
# The specific implementation behind each differentiator is confidential and available for technical due diligence under NDA or applicable clearance.
This overview is intended for external technical evaluation and public distribution. It presents Glápagos’ platform architecture at a level appropriate for CIOs, CTOs, enterprise architects, government technology leaders, and infrastructure investors. Certain implementation details, infrastructure partner information, and security control configurations are confidential, proprietary, or subject to applicable government restrictions and have therefore been omitted. A more detailed technical briefing is available to qualified counterparties under appropriate confidentiality agreements or clearance requirements.
