ARI is the complete environment for designing, testing and deploying reliable AI workflows. With a visual editor, native building blocks, automatic testing and one-click deploy — all under your control. Pay per production invocation: zero fixed costs per seat.

No black boxes

Deterministic workflows

Production-grade governance

Control Layer

Built for production.

HOW TO WORK WITH ARI

From problem to production, step by step.

ARI structures the AI development process into clear phases. Each phase has dedicated tools — so nothing is skipped and you iterate methodically.

Step 1 — Design

Define the objective and map the flow on the visual node editor. Describe what goes in, what should come out, and how you will measure success — before you even start building.

Step 2 — Develop

Configure inputs, set metrics, and build workflow variants. Connect nodes and building blocks, compare results, and refine each node until the flow behaves exactly as you want.

Step 3 — Validate

Choose the most suitable LLM model and launch automated test cycles. The Research Tree records every version: you can compare different approaches and roll back at any time.

Step 4 — Publish

When results satisfy you, publish with one click. ARI generates API documentation and integrates the flow into existing systems — SaaS, private cloud, or on-premise.

Step 5 — Monitor

Keep track of performance, LLM costs, and resource usage from a single real-time dashboard.

THE ARCHITECTURE

Flows, nodes, building blocks.

A flow is a complete business process — it defines what comes in, what should come out, and how. Each flow is made up of nodes and building blocks, at the same level. Nodes are customizable processing units (logic, integrations, AI, data). Building blocks are pre-configured, field-tested flows, ready to use — reusable across multiple flows with one click.

Input

Nodo

Logica · AI

Nodo

Dati · Integr.

Building

Block

Output

Nodo

Building Block

BUILDING BLOCKS

Components built to adapt to all use cases

RAG / Knowledge Manager

ARI's RAG retrieves relevant information from corporate document sets — PDF, Word, web pages, databases — and provides it to the LLM as verified context. Every answer includes source citations, so you can trace where every piece of information comes from. Native hybrid search: vector search (semantic) + BM25 (keyword) combined. Most RAG platforms force you to choose one; ARI runs both and merges the results.

ARIdb DataVoice

ARIdb DataVoice transforms natural language questions into precise queries across different, unrelated databases, relating them on request. No need to write SQL: ARIdb DataVoice understands your data structure and retrieves exactly what is needed.

Decision Maker

Decision Maker evaluates options, applies configurable criteria and makes decisions autonomously — explaining every step of the reasoning. Not a black box: you can see exactly how it arrived at the conclusion and configure the criteria it uses.

Data Transformer

Takes heterogeneous data — from different formats, systems and sources — and normalizes it toward a data model you define. Handles format conversions, cleaning, deduplication and enrichment, without requiring complex ETL pipelines.

Web Crawler

ARI's Web Crawler doesn't just scrape static pages. It navigates websites autonomously, overcomes technical barriers, fills out forms, extracts dynamic content and replies to complex queries based on what it finds online.

Human In The Loop — Human-In-The-Loop

Not all processes can be fully automated — nor should they. Human In The Loop inserts checkpoints in the workflow where a human decision, approval or verification is needed. The flow pauses, notifies the right person, and resumes after the response.

IN AZIONE

ARI Platform - live.

Watch how to build an AI workflow end-to-end: from the visual node editor, to automated tests, to production deployment. A complete process, in a single environment.

Full control over the AI lifecycle.

FEATURES

Everything you need to bring AI into production.

Not just an editor. ARI is a complete environment with tools for every phase of the development cycle.

🌳

Research Tree

Complete history of all experiments. Every run, every variant, every result — organized in a navigable tree. Compare different approaches, go back to a previous version, understand what worked and why.

🧪

Automatic Testing

Define a set of test cases, launch automatic execution, and get a quality score. You can automatically compare multiple LLM models on the same workflow to find the best cost/quality ratio.

📦

Versioning & Rollback

Every publish creates a stable version. You can go back to any previous version at any time. Ideal for enterprise environments where production stability is critical.

📊

Real-Time Monitoring

Dashboard with execution metrics, single run status, latency, cost per run. All in real-time — so you always know what's happening in your production workflows.

💰

LLM Cost Control

Full visibility on costs per provider, per workflow, per run. Set spending thresholds, receive alerts, and optimize model allocation. No surprises on the bill.

🔒

Access Privileges

Four native roles: Maintainer, Developer, Tester, Executor. Each role has defined access and permissions. You can add collaborators without losing control over who can do what.

🔌

API & Autodoc

Each published workflow exposes an automatically documented API endpoint. No manual documentation to write: ARI generates everything, including call examples and output schema.

🔑

Secret Management

API keys, tokens, credentials — all managed securely in ARI, without hardcoding in workflows. Secrets are encrypted and never exposed in logs or results.

LLM-AGNOSTIC

The best model for every task. Not just for what's available.

ARI works with any LLM provider — OpenAI, Anthropic, Mistral, Google Gemini, Meta Llama, open-source and self-hosted models. You can use different providers in different nodes of the same workflow, compare performance with automatic testing, and change models without touching the flow logic. You are never tied to a vendor. If a better model arrives tomorrow, ARI allows you to adopt it in minutes.

OpenAI · Anthropic · Mistral · Google Gemini · Meta Llama · Cohere · Self-hosted (Ollama, vLLM, etc.) · AWS Bedrock · Azure OpenAI

LLM OPEN-SOURCE

Self-hosted. No external dependencies.

ARI supports provisioning of self-hosted LLM models directly from Hugging Face or via runtimes like Ollama and vLLM. For air-gapped environments, closed perimeters or absolute data residency requirements: AI runs without connection to external providers.

Hugging Face · Ollama · vLLM · LM Studio · any GGUF/ONNX compatible model

HOSTING

Where you want. How you want.

ARI adapts to your infrastructure, not the other way around. Three deployment modes to meet any security, compliance and performance requirements.

SaaS

For who: Teams that want to start immediately without infrastructure.

How it works: Immediate access on managed cloud by Black Bytes. Automatic updates, zero maintenance.

Data: Ephemeral in production — no workflow data is persisted on our servers.

Private Cloud

For who: Companies with specific security requirements or data residency policies.

How it works: ARI runs on dedicated cloud (AWS, Azure, GCP) in the customer's perimeter. Full isolation, access via VPN.

Data: Fully in the corporate perimeter.

On-Premise

For who: Enterprise with own infrastructure or strict compliance requirements (fintech, healthcare, PA).

How it works: Installation on customer server. No data ever leaves the corporate infrastructure.

Data: 100% under customer control.

Learn more about Deploy & Privacy

ARI MODULES

Looking for a ready-to-use solution?

ARI Modules are pre-configured vertical products: ARIdb, ARIknow, ARIchat, ARIdoc. Activate the module, start right away. Dedicated interface, everything already set up.

ADOPTION

You've seen the platform. Now find your path.

Three adoption models designed for different contexts — from regulated enterprise to the SME that wants to start right away.

Comparison

Why Choose ARI

Comparison with alternatives

Feature	ARI	n8n	Make
🏢 DATA ORIGIN & SOVEREIGNTY
Legal HQ & Development	Switzerland (CH) — Black Bytes SA	Germany (EU)	Czech Republic (EU) — Celonis Group (USA)
Privacy & GDPR	GDPR-native, Data-sovereignty zero-trust	GDPR compliant	GDPR compliant
Platform Self-Hosting	SaaS, Private Cloud, On-Premise	SaaS, Self-hosted	SaaS
Open-Source LLM Deployment Run local models (e.g. Llama) without sending data externally	✓ Native support (Ollama, vLLM)	✓ Limited support	✗ Cloud API only
💰 AI GOVERNANCE & COSTS
LLM Cost Monitoring Granular visibility on every single execution	✓ Granular real-time monitoring	✗ Generic execution monitoring	✗ Operation consumption only
Research Tree History of all experiments and variants	✓ Research Tree for experiments	✗ Execution log only	✗ No dedicated tool
⚙️ DEVELOPMENT & INTEGRATION
Native RAG Building Block Knowledge Manager with source citation	✓ Native with source management	✓ Available (LangChain integration)	✗ Requires external integration
Automatically documented API Ready-to-use endpoints for every workflow	✓ OpenAPI and automatic documentation	✗ Manual webhooks	✗ Manual webhooks
🎯 TARGET & IDEAL USE
Target	Enterprise and Regulated Entities (Audit-ready)	Developers and Automators	Business users and Marketing

European low-code GenAI orchestration platform. Built for control, transparency and trust.

Black Bytes SA — Via Pretorio 13A, 6900 Lugano, Switzerland

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ARI FLOWS

For those who want to build custom AI processes.

AI that works. Not AI that improvises.

No black boxes

Deterministic workflows

Production-grade governance

Control Layer

Built for production.

HOW TO WORK WITH ARI

From problem to production, step by step.

ARI structures the AI development process into clear phases. Each phase has dedicated tools — so nothing is skipped and you iterate methodically.

Step 1 — Design

Define the objective and map the flow on the visual node editor. Describe what goes in, what should come out, and how you will measure success — before you even start building.

Step 2 — Develop

Configure inputs, set metrics, and build workflow variants. Connect nodes and building blocks, compare results, and refine each node until the flow behaves exactly as you want.

Step 3 — Validate

Choose the most suitable LLM model and launch automated test cycles. The Research Tree records every version: you can compare different approaches and roll back at any time.

Step 4 — Publish

When results satisfy you, publish with one click. ARI generates API documentation and integrates the flow into existing systems — SaaS, private cloud, or on-premise.

Step 5 — Monitor

Keep track of performance, LLM costs, and resource usage from a single real-time dashboard.

THE ARCHITECTURE

Flows, nodes, building blocks.

Input

Nodo

Logica · AI

Nodo

Dati · Integr.

Building

Block

Output

Nodo

Building Block

BUILDING BLOCKS