Distributed AI Architecture, Adversarial Simulation, & Reliability for ML Teams.

We bridge the gap between Security (keeping bad things out) and Reliability (keeping the system working). Specializing in zero-hallucination policies and adversarial testing for high-stakes environments.

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End-to-End Observability Pipelines

Debug decisions, not just code. We implement custom OpenTelemetry tracing for distributed Python applications, enabling real-time triage of decision failures and logic drift.
[View GitHub: Custom Tracing Schemas]

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Adversarial Simulation & Red Teaming

Your agents are vulnerable. We generate thousands of synthetic 'Market Panic' and 'Prompt Injection' scenarios to stress-test your AI against logic traps before deployment.
99% Defense Rate achieved against indirect injection attacks in multi-cloud environments.

Life Saver isolated

Agentic Orchestration

Architect multi-step automation workflows with built-in 'Circuit Breakers. If agent confidence drops below 85%, our systems force a Human-in-the-Loop handoff to ensure 99.99% logical correctness.

Our pricing and plans

The Audit

Identify where your current agent falls short.

Assessment/project

Feature List

  • Vulnerability Surface Detection
  • Training Data Audit (Missing Segments)
  • Logic Flow Mapping
  • Security Pass Rate Baseline
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The Prototype

Deploy domain-informed simulations to generate a Challenge Set.

Development/project

Feature List

  • Synthetic Data Generation (5,000+ Scenarios)
  • Adversarial Red Teaming
  • Circuit Breaker Implementation
  • Cloud Infrastructure Design (AWS/GCP)
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The Handover

Enterprise-grade reliability with full failover protection

Production/project

Feature List

  • Full Observability Stack (Arize/Datadog)
  • Automated Repair Loops
  • Multi-Cloud Failover Protection
  • HIPAA/Compliance Guardrails
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Frequently asked questions

What technical stack do you support?

We specialize in Python, Go, and C for development. For AI/ML, we use PyTorch, TensorFlow, LangChain, and CrewAI. We deploy on AWS, GCP, and Oracle Cloud.

How do you handle AI hallucinations?

We implement 'Circuit Breaker' agents that monitor confidence scores. If confidence drops (e.g., below 85%), the system halts execution and requests human intervention.

How do you secure agents against Prompt Injection?

We use generative models to produce thousands of attack variations (red teaming) to train the agent to sanitize inputs before execution.