FLAGSHIP PRODUCT

Build AI Networks That Think, Execute, and Learn.

VDF AI Networks is a visual orchestration platform for designing and running multi-agent AI workflows. Describe a goal — the platform decomposes it, routes it to the right agents, executes it, and learns from every run.

Network Builder

Visual drag-and-drop canvas with 14+ node types for any AI workflow

Execution Engine

Real-time orchestration with retry, circuit breaker, and SLO tracking

Full Observability

Cost analytics, energy monitoring, and living knowledge vault

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OVERVIEW

From Natural Language to Running Network

Describe your goal in plain language. VDF AI Networks decomposes it into a structured multi-node network, assigns the right agents and tools to each step, and executes the entire workflow — tracking cost, latency, and energy throughout.

Every network is versioned, observable, and improvable. Execution history feeds a living knowledge vault that makes future runs smarter.

Visual drag-and-drop canvas with 14+ pre-built node types
LLM-powered task decomposition from a single sentence
Multi-provider execution: OpenAI, Anthropic, Azure, Ollama, and more
Per-node cost, latency, token, and energy tracking
Retry policies, circuit breakers, and fallback routing
Living knowledge vault with proof of provenance

NETWORK LAB

Visual Network Canvas Builder

Design complex AI workflows without writing orchestration code. The Network Lab gives you a full visual canvas backed by a powerful execution engine.

Task Decomposition

Type a goal in plain language. The LLM decomposes it into a structured network of nodes and connections — no configuration required to get started.

Drag-and-Drop Canvas

Position nodes freely on an infinite canvas. Connect them with sequential, parallel, or conditional edges. Undo/redo support for every change.

Network Templates

Start from pre-built templates for Project Management, Analytics, Support, and more. Customise and save your own templates for reuse across teams.

Network Validation

Built-in validation surfaces configuration errors before execution. Cycle detection, required-field checks, and connection compatibility verified automatically.

Network Versioning

Every saved network creates a version. Compare versions, roll back to previous configurations, and track what changed between deployments.

Duplicate & Branch

Clone any network into a new draft. Experiment with alternative architectures without affecting the live version. Promote branches when ready.

NODE LIBRARY

14+ Node Types for Any AI Workflow

Every node is a composable building block. Combine them to build pipelines from simple chains to complex branching multi-agent systems.

Agent Nodes

Agent

Single AI agent with configurable model, system prompt, tools, and guardrails

Multi-Agent

Coordinated agent group with planner, decomposition strategy, and resource policies

Data Nodes

Data Source

External data connectors with retrieval settings, embedding models, and query templates

Data Transform

Pre/post-transform pipelines for filtering, mapping, and enriching data between nodes

Control Nodes

Webhook

HTTP trigger with configurable paths, methods, and auth policy

Run Code

Python or JavaScript execution with configurable runtime, handler, limits, and secrets

Wait

Timing and delay operations for rate limiting, polling, and async coordination

Finalize

Output aggregation with configurable targets: Slack, email, HTTP, or storage

Integration Nodes

Tool

Invoke registered tools — web search, file I/O, APIs — with parameter configuration

MCP Action

Model Context Protocol actions for standardized tool invocation across providers

Notifier

Alert and notification outputs for Slack, email, webhooks, and custom endpoints

Storage

Persist execution data, artifacts, and intermediate results to connected storage backends

Callback

Asynchronous callbacks for long-running operations and external system confirmations

EXECUTION ENGINE

Production-Grade Execution with Full Resilience

Run networks with confidence. Every execution is tracked step-by-step, with automatic recovery from failures.

Step-by-Step Tracking

Every node execution is tracked with individual status (queued, running, success, error, timeout), input/output, token usage, and cost.

Retry & Backoff

Configurable retry policies with linear or exponential backoff per node. Never lose a run to a transient provider timeout.

Circuit Breaker

Automatic provider circuit breaking on repeated failures. Agents route around unhealthy providers without manual intervention.

Fallback Routing

Define fallback targets — a different model, agent, or tool — for any node. Error-specific branching routes failures to dedicated recovery paths.

SLO Tracking

Define service level objectives per network (e.g., p95 latency < 3s). Monitor SLO compliance in real time and receive alerts on breaches.

Execution History

Complete run history with filtering by status, time range, and network. Related run clustering and full provenance proof for every execution.

ANALYTICS

Cost, Energy, and Performance Intelligence

Know exactly what every network costs — in dollars, milliseconds, and kilowatt-hours. VDF AI Networks is the only platform that tracks all three.

VDF AI Networks - Living Knowledge Vault

Cost Analytics

Per-execution cost broken down by model, node, and agent
Daily and monthly cost trends with budget tracking
Cloud cost comparison: AWS Bedrock, Azure OpenAI, Google Vertex
Estimated savings vs. running workloads on cloud providers directly
Optimization recommendations: agent switching, network pausing, load balancing

Energy & Sustainability

Total energy consumption in kWh per network and per execution
CO₂ equivalent emissions (grams CO₂e) per run
Separate prompt (input) and decode (output) energy measurement
Coverage ratio: percentage of runs with verified energy data
7-day energy and CO₂ trend charts

Performance Metrics

P50 and P95 latency per network and per node
Token throughput: input, output, and total token counts
Error rates and failure classification by root cause
Resource utilization: CPU, GPU, and memory percentages
Model routing decision rationale and confidence scores

Overview Dashboard

Mission success rate, active networks, and running executions
Recent activity feed with status, latency, and cost per run
Recent agent and model deployment history
Onboarding progress checklist for new teams
Cloud vs. VDF platform savings summary

LIVING KNOWLEDGE

Networks That Remember and Get Smarter

Every execution adds to a knowledge vault. VDF AI Networks indexes run artifacts, proofs, and insights — so future executions benefit from everything that came before.

Knowledge Clusters

Groups of related networks are automatically clustered by domain. Navigate your organization's AI knowledge by topic, not just by network name.

Run Artifacts

Every execution generates artifacts — outputs, logs, traces — stored and indexed in the vault. Query them across versions and time ranges.

Proof of Provenance

Every run generates a provenance proof — a verifiable record of which agents, models, and tools produced each output. Compliance teams get a full audit trail.

Knowledge Indexing

Index network knowledge with configurable chunking, overlap, and embedding model selection. Choose scope: single version, all versions, or custom selection.

Learning & Optimization

Model Governance uses a contextual bandit with 5 learning modes to optimize model routing, tool selection, and plan rewriting decisions continuously in production.

Evaluation Suites

Test networks with rubrics and datasets before deploying. Track accuracy scores across versions and receive optimization hints automatically.

Four Implemented Self-Evolving Dimensions

Four implemented self-evolving dimensions: Model Governance (contextual bandit, 5 learning modes), Agent Personalities, Knowledge Graph, and Cost & Energy Optimisation.

All autonomous. All running continuously in production.

Autonomous RAG restructuring is on the public roadmap.

Zero engineering overhead on the four live dimensions after setup.

GOVERNANCE

Enterprise-Grade Governance and Compliance

PII Redaction Guardrails

Configure per-agent guardrails to redact personally identifiable information from inputs and outputs before they reach any model or storage.

Safety Content Filters

Built-in content safety filters applied at the network level. Configurable severity thresholds with automatic rejection and audit logging.

Tool Allowlists

Define exactly which tools each agent can invoke. Prevent agents from accessing unauthorized systems or data — enforced at the execution layer.

Access Control

Network owners array controls who can view, edit, and run each network. Org-wide and team-scoped permission models supported.

Audit Logging

Full execution traces with input, output, model routing decisions, and rationale. Every run is auditable down to the token level.

Dead-Letter Queue

Failed executions that exhaust retries are routed to a dead-letter sink for manual review, replay, or escalation — nothing is silently dropped.

DEPLOYMENT

Cloud, On-Premises, or Hybrid

VDF AI Networks adapts to your infrastructure requirements. Deploy in the cloud for instant access or on-premises for maximum data sovereignty.

Cloud

Multi-tenant SaaS deployment. Fully managed, zero infrastructure overhead. Connect any cloud AI provider instantly.

Instant provisioning
Auto-scaling execution
Managed monitoring and updates

On-Premises

Self-hosted on your infrastructure. Full data sovereignty, private model endpoints, and integration with internal systems.

No data leaves your environment
Private AI provider endpoints
Custom identity and access management

Hybrid

Cloud orchestration with on-premises model execution. Route sensitive workloads to private infrastructure, general workloads to cloud.

Per-network routing policy
Encrypted data in transit
Unified observability across both