Build AI agents your enterprise can govern end-to-end.
Alquimia is the sovereign Low-Code platform to deploy and operate production-grade AI agents — open source, composable, and running on the infrastructure your enterprise controls. Full visibility, no lock-in.
Low-Code / No-Code without sacrificing depth
Build agents in plain language, ship them to production, drop into code when you need to. The same platform takes you from prototype to scale without rewriting a thing.
Sovereign and composable
Run on your infrastructure — on-prem, private cloud, or hybrid. Replace any model, any tool, any integration. Your stack stays yours.
Governable end-to-end
RBAC, audit trails, secret management, and behavioral evals via Gaussia. Every agent decision is traceable from prompt to action.
The platform layer for AI agents in production.
Most AI agent tools today help one developer build one agent. That works for personal projects and prototypes. It does not work when an organization needs dozens of agents, governed by different teams, deployed across different environments, and observed in real time.
Agentic Platform is the layer underneath. Six components designed to work together so your organization can create, deploy, govern, and distribute AI agents at production scale.
Studio
No-code agent creation, lifecycle management, prompt and tool configuration. Where agents are designed.
Read the docsRuntime
Production execution. Agent-to-agent orchestration, delegation, event-driven inference. Where agents run.
Read the docsSDK + CLI
For engineering teams that want to extend the platform, integrate custom tools, or wire agents into existing systems.
Registry
OCI-backed publish and pull. Version, namespace, and distribute agents across teams and environments.
Observability
OpenTelemetry traces, Trust Lens metrics, token analytics. Every inference is inspectable.
Explore GaussiaGovernance
RBAC, SSO via Keycloak, secrets via Vault, multi-tenant Agentspaces. Enterprise primitives by default.
If your reference for AI agents today is a personal assistant or a developer framework, this is a different category.
Three layers. Built to be inspected.
Agentic Platform sits on top of an enterprise foundation and exposes its capabilities through APIs, SDKs, CLIs, and connectors to your channels.
The same agents you design in Studio run in production through the Runtime, are versioned in the Registry, observed in real time through the Observability layer, and governed by enterprise primitives at every step.
See full architecture→- L01SurfaceHow the platform is reachedAPIsSDKCLIChannels
- L02CoreWhere agents are designed, governed and runStudioRuntimeRegistryObservability
- L03FoundationEnterprise primitives below the platformKubernetes / OpenShiftRedisS3VaultOpenTelemetry
Solutions in production.
Modernize customer support with conversational AI
Deploy AI agents on your channels — WhatsApp, Slack, email — that handle ticket queries, classify incidents, and route them to the right team in real time.
Automate document and claims workflows
Process structured and unstructured documents with auditable AI agents. Every classification, extraction, and decision traceable from prompt to outcome.
Triage incidents and run IT playbooks
Build AI agents that triage incidents at creation, run runbooks against your existing observability stack, and surface root causes with the evidence behind them.
Industries we serve.
Chips filter the Solutions hub by industry. The ember mark indicates a deep industry page.
Modernizing customer support with AI on sovereign infrastructure.
ArSAT, Argentina's state-owned telecommunications company, deployed Alquimia Agentic Platform on Red Hat OpenShift AI in their own datacenter. Two AI agents are running in production today:
- 01Conversational support over WhatsApp. Customers query ticket status in natural language and receive real-time information without commands or rigid forms.
- 02Automated incident triage. Every ticket is classified, prioritized, and routed at creation, introducing an early decision layer that improves speed and consistency of resolution.
On-prem deployment
All sensitive information — tickets, operational data, customer context — stays inside ArSAT's perimeter, with no exposure to external services.
Multi-model architecture
Models are managed and versioned through the platform, avoiding rigid dependencies on a single external provider.
Auditable decisions
Every interaction is logged for governance, traceability, and compliance.
Open source by design.
Open code, replaceable components, no vendor lock-in. We craft Gaussia, our open evaluation suite, for the community — so every behavioral metric we publish is reproducible in your environment.
From the team.
Running AI agents on Red Hat OpenShift AI: lessons from a sovereign deployment.
GPU economics, hardware observability, model explainability — three runtime-layer choices that decide what governance is feasible above the agent layer.
From a notebook to a fleet: why AI agents need a platform layer.
The first AI agent is not the hard one. The fourth is. When one agent becomes a fleet, the platform underneath them is what makes it operable.
Why governing AI agents end-to-end is now a board-level concern.
When AI agents make decisions a person used to be accountable for, governance reaches the boardroom. The six properties every audit committee should ask about.
Bring your problem. We'll walk you through it.
We work with enterprise teams building AI agents on their own infrastructure. A short call is enough to see if Agentic Platform is the right fit for your case.
Get in touch