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CORVXCorvx builds AI, software, and operating systems for teams that need the work to hold up in practice.
A Corvus Industries company
Vietnam:
Vincom Landmark 81 (72/F), 720A Điện Biên Phủ, Phường 22, Bình Thạnh, Ho Chi Minh City.
United States:
17875 Von Karman Avenue, Suites 150 & 250, Irvine, CA 92614.
AI systems with a clear owner
The shared control layer for the AI work your team needs to run.
We build AI systems as shared, governed infrastructure rather than isolated pilots. The work covers orchestration, memory, data, and review so teams can use AI with clear ownership, controls, and cost visibility.
Model-Agnostic Memory-Native Audit-Ready
SharedControl layer
NamedSources
90-120 DaysTypical first release
VisibleAudit trail
The architecture around the model
System overview
ACTIVE
Many organizations have working AI pilots but no shared way to run them. Our AI development work covers the architecture and operating model around orchestration, knowledge, data, and integration so each new use case does not become its own island.
We work with technology, data, and business leaders to set ownership, decision rights, and measures of success. Then we deliver a real use case on a platform your teams can inspect, operate, and improve.
01
Enterprise Orchestration & Control
Execution GovernanceWe replace scattered prompts and isolated agents with workflows that show how AI starts, hands off, and completes work. The control layer makes those steps testable and reviewable.
- Explicit workflowsWe implement auditable workflows, such as state machines and graph-based orchestration, so critical processes are predictable and easier to change safely.
- Controlled tool accessWe connect agent permissions to your identity and access controls so AI can use only approved tools, data, and environments.
- Cost visibilityWe track model and infrastructure use by product, use case, and business unit, with limits that make unusual consumption visible.
02
Institutional Memory & Knowledge Systems
Enterprise RAG & ContextWe connect AI systems to the policies, products, history, and terms your teams actually use. Answers come from named sources rather than a generic internet search.
- Named sourcesWe connect AI to documents, systems of record, and knowledge repositories with clear rules for contribution and approval.
- Retrieval-Augmented Generation (RAG)We tune retrieval, ranking, and context for the accuracy, latency, and compliance requirements of the use case.
- Citable answersWe keep a path from an answer back to the document or record that supports it when the workflow requires one.
03
AI‑Ready Data Foundation
Structured Inputs & ExtractionWe turn documents and other unstructured content into data that AI and analytics systems can use. The design fits the data platforms you already operate.
- 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
Related work
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
Questions people usually ask
Implementation details
user@corvus:~/executive-faq
$ query implementation-review
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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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We extend your existing security and compliance framework. Orchestration, data access, and logging follow your identity, access, and risk controls so AI use is visible and reviewable.
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No. The control, memory, and data layers sit above the model, so you can choose a commercial, open-weight, or proprietary model for each task without changing the surrounding controls.
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The timeline depends on the use case and the state of your data. A focused first release often fits within 90 to 120 days when foundational work and one valuable workflow move together. Early gains usually come from fewer duplicate integrations, clearer operations, or lower model spend.
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Yes. Many organizations start with data spread across systems and formats. We prioritize the domains and document types that matter to the first use cases, then design extraction, validation, and integration patterns that can grow over time. Data readiness improves as part of delivering a useful system, not as a separate project that delays it.