Armored Helix Services

What we build

Production AI systems and the engineering platforms around them—designed, built, deployed, and maintained for regulated organizations.

Enterprise AI Agents

AI agents that operate inside production business workflows—triaging requests, executing routine work, and escalating to humans when judgment is required. Organizations adopt agents to handle operational volume without sacrificing control: every action is permissioned, logged, and reviewable. Built on LLM orchestration frameworks, enterprise APIs, and identity systems.

  • Agents integrated with ticketing, documentation, and workflow platforms
  • Human oversight: approval gates, escalation paths, and full audit trails
  • Permissioned tool use scoped to least-privilege service accounts

Enterprise Knowledge Platforms

Retrieval systems that make years of operational history—documentation, tickets, procedures, decisions—answerable in seconds while respecting who is allowed to see what. Organizations need this because institutional knowledge is fragmented across systems and lost as teams change. Built on vector search, permission-aware retrieval, and citation-grounded generation.

  • Permission-aware retrieval across collaboration and documentation systems
  • Citation-backed answers grounded in approved sources
  • Query audit logging for governance and access review

Workflow Automation

Structured automation of request, approval, triage, and handoff processes that currently run on email and spreadsheets. The business problem is cycle time and reconstruction cost—slow handoffs and no reliable record of what happened. Built with event-driven architectures, stable APIs, and integrations into existing systems of record.

  • Request/approval flows with automatic timestamps and status visibility
  • Event-driven integration with existing enterprise systems
  • Audit-ready logs and role-based access throughout

AI Evaluation

Automated frameworks that measure how AI systems actually behave—before and after every change. Organizations deploying AI without evaluation are operating on assumption; regressions surface in front of users instead of in test runs. Built with scenario suites, LLM-assisted grading, telemetry, and CI integration.

  • Evaluation suites built from hundreds of realistic enterprise scenarios
  • Regression detection wired into release processes
  • Telemetry and quality metrics that make AI behavior measurable over time

Security Automation

Platforms that automate evidence collection, vulnerability tracking, and security posture reporting. Security teams spend disproportionate effort assembling review materials by hand; automation turns that into a continuous, queryable record. Built with API integrations into scanning, identity, and infrastructure tooling.

  • Automated evidence collection and posture reporting
  • Vulnerability and exception tracking across the environment
  • Dashboards designed for security reviews and leadership visibility

Enterprise AI Architecture

The secure foundation production AI runs on: cloud-native infrastructure, data boundaries, model governance, and deployment patterns—including private and air-gapped environments. Organizations need this because AI systems inherit every weakness of the platform beneath them. Built on AWS and Azure with RBAC, audit logging, monitoring, and controlled egress.

  • Cloud-native AI infrastructure on AWS and Azure
  • Private deployments: on-prem inference with no egress, offline update bundles
  • Model/version governance with rollback strategies

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Tell us about your workflows, the systems they run on, and the constraints they operate under.

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