WHAT YOU CAN DO WITH VDF AI
Run an entire on‑premises AI platform — from data to autonomous networks.
VDF AI is one integrated platform for the full enterprise AI lifecycle: connect data sources, build RAG pipelines, generate fine‑tune datasets, create agents, compose multi‑agent networks, monitor every layer — all running 100% on your infrastructure.
20 CAPABILITIES, 5 PILLARS
Everything an enterprise needs to operate AI on‑prem
PILLAR 01
Data Foundation, RAG & Fine‑Tune Preparation
Turn raw enterprise data into model‑ready intelligence. VDF Data is the operational hub for source connectivity, exploration, vectorization, and fine‑tune dataset generation.
Auto‑build RAG pipelines from integrated apps
Plug in an application and VDF AI auto‑creates an end‑to‑end RAG pipeline — ingestion, chunking, embeddings, vector index — with zero glue code.
- Integration‑driven pipeline generation
- Re‑indexing on source change
- Quality scoring on retrieved context
Connect any data source
Register connector credentials and test connectivity from one place. Native support for the most common enterprise databases and SaaS systems.
- PostgreSQL
- MySQL
- Microsoft SQL Server
- Oracle
- SAP HANA
- Exasol, Presto, JDBC
- Jira, Confluence
- GitHub, GitBook
- PDFs, DOCs, websites
- Custom REST APIs
EDA, health checks, Feature Lists & Vector DB Builder
Validate data quality before vectorization. Curate feature subsets for training, evaluation, and reproducibility. Chunk and embed into pgvector — controlled and observable.
- EDA: profile columns, distributions, nulls
- Health check: blocks indexing on quality regressions
- Feature Lists: named, versioned feature subsets
- Vector DB Builder: chunk + embed into pgvector indexes
- Semantic Search: query indexed vectors and inspect ranked matches
Generate fine‑tune data from your sources
Export model‑ready fine‑tune datasets in the formats your model needs — without leaving the platform.
- OpenAI JSONL format
- Anthropic format
- CSV / generic exports
- Source‑linked provenance on every row
Data source library
One unified connector library across databases, apps, and APIs — each with credential management, connection testing, and audit logging.
PILLAR 02
Agent Creation & Model Registry
Build agents the way that fits your team — conversational or structured — and run them on whichever models you've registered, cloud or local.
Create agents via chat — or a manual form
Spin up agents two ways: describe what you need in the chat interface, or fill in a structured form for explicit control over tools, prompts, and routing.
- Conversational agent builder
- Form‑based agent editor with full field control
- Versioned agent definitions
- Reusable agent templates per theme/domain
Agent playgrounds
Iterate on agents in isolation before shipping them into networks. Test prompts, tools, and model routing with full traceability.
- Per‑agent test harness
- Inspect tool calls, model routes, and intermediate steps
- Replay runs with modified parameters
- Promote from playground to network
Register LLMs and SLMs — multi‑provider, on‑prem first
Mix cloud and local models in one catalog. Route by capability, cost, latency, or energy profile. Swap providers without rewriting agent logic.
- OpenAI, Anthropic, x.ai, OpenRouter
- DeepSeek, Hugging Face
- Ollama and other local runtimes
- Custom on‑prem endpoints
- Model capabilities and tags
- Cost and energy profiles
- Routing rules per theme/network
- Per‑role access & rate limits
Bring your own models. Keep your data where it belongs.
PILLAR 03
Networks, Tools & Custom APIs
Compose multi‑agent networks two ways — automatically on the fly from user intent, or manually in the Network Labs visual canvas — and plug in any custom tool or API your business needs.
Add customer APIs and custom tools
Register internal HTTP APIs and MCP‑backed tools alongside built‑in connectors. Tools become first‑class citizens in every network.
- HTTP / REST tool registration
- MCP tool servers
- Auth, secrets, and rate limit management
- Schema introspection for safe invocation
On‑the‑fly agent networks
VDF decomposes a user request into intent and sub‑tasks, then assembles the right agents, tools, and models in real time.
- Intent decomposition
- Just‑in‑time agent allocation
- Hierarchical planning (strategy → tactical → execution)
- Result synthesis with quality validation
Network Labs — visualized multi‑agent canvas
Design multi‑agent networks by hand on a visual canvas when you need precise control over flow, branching, and tool wiring.
- Drag‑and‑drop agent and tool nodes
- Explicit edges, conditions, and fallbacks
- Live test runs from the canvas
- Versioned network definitions
Themes, domains & custom intent rules
Group agents, tools, and networks into themes and domains. Define custom intent rules and per‑network system prompts.
- Theme / domain configuration containers
- Per‑network system prompt overrides
- Custom intent rules for routing
- Role‑based access at the theme level
Continuously learning tool & backend routing
Routing isn't static. Every run feeds back into how VDF selects tools and backends next time — closing the loop without engineering work.
- Tool selection learned from outcomes
- Backend routing tuned by feedback signals
- Per‑network learning isolation
- Audit trail for every routing decision
PILLAR 04
Knowledge, Memory & Self‑Evolving Learning
VDF AI doesn't just answer — it builds an organizational memory and continuously evolves how it routes, retrieves, and reasons.
Organizational knowledge graph & memory
Build and index a graph of your organization — entities, relationships, decisions, artifacts — and let agents reason over it with persistent memory.
- Entity & relationship extraction from sources
- Graph‑based retrieval alongside vector search
- Per‑agent and per‑network memory scopes
- Graph enrichment from continuous use
SEEMR — Self‑Evolving Model Router
A contextual‑bandit‑based router that learns which model to use for which task — across cost, latency, capability, and energy — with five live learning modes.
- Model governance with five learning modes
- Agent personality evolution
- Knowledge graph integration
- Cost & energy optimization in production
PILLAR 05
Operations, Apps & 100% On‑Premises
Everything you ship needs to be measured, tested, monitored, and operable inside your own walls. VDF AI is built that way from day one.
Professional chat interface
A production‑grade Portal as the unified UI shell for chat, agents, networks, and admin workflows.
Custom apps & apps on demand
Ship your own internal apps backed by VDF agents and networks. New apps can be generated on demand from existing capabilities.
Accuracy testing module
Evaluate agents and networks against golden datasets. Catch regressions before they reach production users.
Transparent end‑to‑end monitoring
Every request, route, tool call, and model decision is logged and inspectable. No black‑box AI.
Energy & resource monitoring
Track energy, cost, and CO₂ alongside performance. Routing decisions can optimize for efficiency, not just accuracy.
Well‑documented APIs for everything
Every service exposes a documented API. Build, integrate, automate — nothing is hidden behind the UI.
It all works together — 100% on your infrastructure
Data, agents, networks, knowledge, monitoring, and model routing are one integrated platform. And every component runs on‑premises.
- Fully on‑premises deployments
- Air‑gapped environments
- Customer‑hosted LLMs and SLMs
- No mandatory cloud dependency
- Data never leaves your infrastructure
Ready to see VDF AI running on your infrastructure?
Walk through the full platform in a live demo, explore individual products, or talk to us about a deployment in your environment.
GET IN TOUCH
You Have Questions
Tell us what you’re trying to achieve—governed AI Networks, enterprise RAG, deep integrations, or on‑premise deployment. We’ll help you map the right architecture, security posture, and rollout path. If you’re moving beyond AI pilots and need scalable, auditable execution, reach out—our team is ready to help.