CORVX
Vietnam (Southeast Asia Hub):
Vincom Landmark 81 (72/F), 720A Điện Biên Phủ, Phường 22, Bình Thạnh, Ho Chi Minh City.
United States (North America Hub):
17875 Von Karman Avenue, Suites 150 & 250, Irvine, CA 92614.
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Enterprise AI Governance
The Central Nervous System for Your Non-Human Workforce.
We provide structured, enterprise-grade AI development services that treat AI as governed infrastructure—not a collection of disconnected pilots. Our teams design and implement the orchestration, memory, data, and governance layers that let you scale AI value across the organization while maintaining control, compliance, and economic discipline.
Model-Agnostic Memory-Native Audit-Ready
6%Operating Margin Uplift
90%+Reduction in Re‑briefing
90–120 DaysTypical Time to First ROI
100%Use Cases with Audit Trails
The Architecture of Autonomy
Strategic Infrastructure Overview
ACTIVE
In most enterprises, AI has already proven its value in pilots. The challenge now is scaling that value without scaling cost and risk. Our AI development services focus on building the reference architecture and operating model—across orchestration, knowledge, data, and integration—so that AI behaves like a governed platform, not an uncontrolled set of tools.
We work with technology, data, and business leaders to define clear ownership, decision rights, and success metrics, then deliver concrete use cases on top of a platform that is auditable, resilient, and aligned with your existing security and compliance frameworks.
01
Enterprise Orchestration & Control
Execution GovernanceWe replace ad-hoc prompts and isolated agents with a unified orchestration layer that runs AI as governed workflows. This is the control point for how AI systems initiate, sequence, and complete work across your enterprise.
- Deterministic WorkflowsWe implement explicit, auditable workflows (for example, state machines and graph-based orchestration) so that critical processes are predictable, testable, and change-controlled.
- Governed Tool AccessWe align agent permissions with enterprise identity and access controls, ensuring that AI systems interact only with approved tools, data, and environments.
- Economic ControlWe centralize visibility into model and infrastructure usage so that spend is attributed by product, use case, and business unit—with guardrails to prevent uncontrolled consumption.
02
Institutional Memory & Knowledge Systems
Enterprise RAG & ContextWe design and implement retrieval and knowledge architectures that make AI systems fluent in your policies, products, history, and terminology—grounded in authoritative sources, not generic internet content.
- Authoritative Knowledge BasesWe connect AI to clearly defined sources of truth across documents, transactional systems, and knowledge repositories, with governance on who can contribute and approve content.
- Retrieval‑Augmented Generation (RAG)We implement RAG patterns as a first-class knowledge system—tuning retrieval, ranking, and context strategies for accuracy, latency, and compliance.
- Consistent, Citable AnswersWe design systems so that responses are grounded in verifiable data, with traceability back to underlying documents and records where required.
03
AI‑Ready Data Foundation
Structured Inputs & ExtractionWe build the data and document processing layer that turns unstructured content into reliable, governed signals for AI and analytics, aligned with your existing data platforms.
- Intelligent Document Processing (IDP)We extract structured data from contracts, invoices, and reports using schema-first patterns with validation, so downstream systems can rely on the outputs.
- Data Quality and LineageWe instrument checks, lineage, and retention rules so that inputs to AI systems are traceable and compliant with regulatory and internal standards.
- Integration with Core PlatformsWe integrate AI-derived data into existing warehouses, lakes, and operational systems so that AI enriches, rather than fragments, your data landscape.
Core Capabilities
Agent Registry & Identity
Financial Orchestration
Contextual Persistence (Mem0)
Relational Intelligence (GraphRAG)
Deterministic Accuracy
Intelligent Document Processing
Also in This Practice
MLOps-as-a-Service
Model versioning, performance monitoring, and automated deployment. We operationalize your ML models so they run reliably in production with continuous retraining and A/B testing.
MLflowKubeflowSageMaker
Digital Twin Simulations
Virtual models of manufacturing and maritime environments for scenario planning, predictive maintenance, and training. Connect IoT and OT data for real-time digital replicas.
IoTSimulationUnity
AI Compliance & Governance
Explainable AI (XAI), bias detection, and EU AI Act alignment. We help you deploy AI that meets regulatory requirements and audit trails for high-risk use cases.
EU AI ActXAIBias Detection
Executive FAQ
Strategic Implementation
user@corvus:~/executive-faq
$ query --strategic-implementation
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Most organizations begin with RAG at the level of individual use cases or tools. Our focus is on institutional memory as shared infrastructure: a governed knowledge layer, aligned to your systems of record, with clear ownership, lifecycle management, and controls. This allows multiple AI solutions to draw on the same, validated context rather than each building its own partial view.
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Our AI development services are designed to extend, not bypass, your existing security and compliance frameworks. We align orchestration, data access, and logging with your identity, access management, and risk controls—so that AI usage is visible, explainable, and auditable. This typically increases confidence from risk and compliance stakeholders rather than creating exceptions.
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No. Our architectures are intentionally model‑agnostic. We design the control, memory, and data layers so that you can use the right model for each task—whether commercial, open‑weight, or proprietary—while maintaining consistent governance and observability across them.
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Timelines vary by organization, but most clients see tangible results within the first 90–120 days when we pair a focused set of high‑value use cases with foundational platform work. Early wins often come from improved reliability, reduced duplication, and lower operating cost, followed by broader impact as more workflows move onto the shared platform.
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Yes. Many organizations begin their AI journey with heterogeneous, siloed data. We start by prioritizing the domains and document types that matter most to your initial use cases, then design extraction, validation, and integration patterns that can be expanded over time. The objective is to improve data readiness as a product of delivering concrete AI outcomes, not as a prerequisite that delays value.