Seven years of production development. One platform that coordinates ML, GenAI, and Agentic AI in governed pipelines — from raw data to autonomous action.
ARPIA is not an AI tool. It is the execution layer that connects your data, coordinates your AI workers, and activates decisions in production — with full traceability at every step.
API Gateway connecting AppStudio, Agentic AI (Claude, ChatGPT, Cursor, Copilot), and Robots. Standards-based MCP protocol for seamless integration.
Knowledge Ontology, Reasoning Engine, Policy & Audit, Operational Workflow, Decision Logic, and AI Orchestration—the core intelligence layer.
Enterprise Data, Big Data, Social Data, Financials, Internet, Documentation, and External sources. Unified data storage and access layer.
Each layer is purpose-built and production-hardened. Together they form the only platform that takes you from raw data to activated strategy — governed end-to-end.
Four levels of traceability embedded in every pipeline — not bolted on afterward
Every pipeline traces Data, Model, Decision, and Audit events. ISO 42001 and SOC 2 Type 2 aligned. Governance is not a feature — it is the foundation every use case is built on.
Role-based access control, custom alerts for data misuse or policy violations, and full audit readiness. Every AI action is accountable — not approximate.
End-to-end observability across every AI worker, every pipeline step, every decision. See exactly what happened, when, why — and what the AI decided to do next.
AI-optimized digital twin of your data — mapped to the semantic business context AI needs to reason correctly
An AI-optimized digital twin of your production data — built on StarRocks / SingleStore. AI workers access full data without touching live systems. Production is protected. Reasoning is accelerated.
A living semantic layer that maps your data entities to business context. Relationships, rules, and domain knowledge encoded so AI workers understand not just the data — but what it means.
See how knowledge connects and evolves in real time. The ontology is not static — it grows with your business, learns from new data, and keeps every AI worker reasoning in context.
The pipeline design environment — compose ML, GenAI, and Agentic AI workers into governed workflows
Compose Python ML Workers, GenAI Sequential Workers, Agentic AI Workers, and Application Workers in a single governed pipeline. Each worker type does what it does best — coordinated, not isolated.
Build complex multi-step AI pipelines without deep engineering. Visual composition with full auditability. Kubernetes-orchestrated under the hood — enterprise-grade, no DevOps required.
Secure interface for external AI agents and robots to access governed knowledge via MCP protocol. Bidirectional endpoints — receive agent requests, reason internally, send curated governed responses.
Build intelligent, governed AI applications — no deep engineering required
Build internal AI-powered business applications — dashboards, decision tools, reports, workflow apps — without extensive coding. Governance is embedded by default in every app built on ARPIA.
For teams that want code-level control. Python, PHP, JavaScript SDKs with Git-based workflows and K8s orchestration. Full flexibility within the governed platform boundary.
Auto-generate secure APIs and data integration pipelines in real time. Connect business units, activate enterprise systems (ERP, CRM, HRIS) from any pipeline output — including sub-second activations.
End-to-end visibility of every decision — connected to your entire enterprise stack
The end-to-end intelligence map of your entire ARPIA deployment. See every pipeline, every worker, every decision — in one view. Not a dashboard. A living atlas of your AI operations.
Connect reasoning flows to SAP, Oracle, Salesforce, Snowflake, Databricks, and more via API/REST. Activate ERP strategies, update CRM records, trigger downstream systems — from any pipeline output.
Model-agnostic. Standards-based. Native MCP support for Claude, ChatGPT, and all MCP-compatible platforms. No vendor lock-in — ARPIA is the governed layer, not the AI provider.
Watch how ARPIA coordinates ML, GenAI, and Agentic AI workers in a single governed pipeline — from data ingestion to production activation.
7 years of production development. Deployed today in Latin America.
Most organizations operate at Level 2–3 AI maturity. ARPIA is built for — and deployed at — the top of the McKinsey / Gartner / OECD AI maturity frameworks.
ARPIA is assessed at Level 6–7 across the McKinsey AI Adoption Framework, Gartner AI Maturity Model, and OECD AI Governance Framework.
Choose how you want to deploy governed AI applications—no-code studio, developer platform, or direct MCP integration
Native no-code platform for building governed AI applications. Visual workflow builder, pre-built components, instant deployment.
Developer-friendly platform for building custom AI applications with full code control while maintaining governance.
Direct protocol integration with Claude Desktop, ChatGPT, Cursor, and any MCP-compatible platform.
Every capability designed to bridge data, reasoning, and action
ARPIA integrates with your existing data sources, AI platforms, and enterprise systems
SAP, Oracle, Salesforce, Microsoft Dynamics, Snowflake, Databricks
Claude, ChatGPT, OpenAI, Anthropic, Meta Llama, Mistral, Gemini
VS Code, Cursor, GitHub Copilot, n8n, LangChain
ERP, CRM, HRIS, Custom databases via API/REST
AWS, Azure, Google Cloud, On-Premise deployment
SSO, SAML, OAuth, Active Directory, Okta
See detailed breakdowns of ARPIA's six core capabilities: Governance Engine, Knowledge Ontology, Reasoning Visualization, MCP Integration, No-Code AppStudio, and Enterprise Security.
View Full Features →We understand the problem, design the AI pipeline, and build it on ARPIA — in weeks, not months. Then you run it. Then you scale it.