Repeatable
Workflows become operational assets with defined inputs and owners — not prompts pasted between colleagues.
Platform / Technical Overview
Enterprise AI orchestration you can govern, audit, and own.
VDF AI is the orchestration layer beneath your AI programme: AI Networks where multiple agents collaborate on one request, enterprise retrieval grounded in your own systems, and a governance model that records every decision — running on your infrastructure, not somebody else's.
- Execution
- Governed multi-agent Networks
- Grounding
- Enterprise RAG + knowledge graph
- Deployment
- On-prem, hybrid, air-gapped
Engineers on the call, not a qualification script. No cloud tenancy required to evaluate.
SEEMR 4 live dimensions Audit every route logged
01 — The gap
Most AI programmes do not stall on model quality.
They stall on everything around the model.
What a pilot proves
That a model can produce a useful answer once, for one team, with a human watching. This is the easy part, and it is why pilots almost always succeed.
What production demands
That the same answer is reproducible, permission-aware, cost-bounded, explainable to an auditor, and safe to run without a human watching. That is an orchestration problem, not a prompting problem.
Auditable
Every routing decision, retrieval and tool call is recorded, so a decision can be reconstructed months later.
Governable
Models, tools, agents and permissions are controlled centrally instead of per application team.
Sovereign
Deployment mode is your decision: your data centre, your private cloud, or fully disconnected.
02 — How it runs
From request to execution, without chaos
One request enters. The platform decides how to satisfy it, coordinates the work, checks the result, and learns from the outcome — with a record at every step.
- 01
Decompose
Intent is parsed and the request is broken into typed sub-tasks with their dependencies mapped.
- 02
Select
The right agents, tools and models are chosen at runtime against cost, latency and capability.
- 03
Collaborate
Agents work in parallel, review each other's output, and refine until the result holds up.
- 04
Learn
Outcomes are logged and evaluated; routing, personalities and the knowledge graph adapt.
03 — Capabilities
Eight layers that make this enterprise-grade
Read it as a spec sheet. Each layer states what it does, what it is composed of, and where to go for the engineering detail.
AI Networks
Multi-agent execution with a supervisor, not a prompt chain
A Network is a governed unit of work. A request is decomposed into intent and sub-tasks, the right agents and tools are selected at runtime, and the result is validated before it is returned — with the whole path recorded.
- Decomposition
- Intent parsing → typed sub-tasks → dependency graph
- Selection
- Agents, tools and models chosen per task at runtime
- Validation
- Cross-agent review and refinement before response
- Record
- Every route, tool call and output persisted for replay
Goes deeper
Enterprise RAG
Answers grounded in your systems, with retrieval you can inspect
Retrieval is treated as an engineered pipeline rather than a vector lookup. Sources are indexed with their access model intact, context is scored before it reaches a model, and the store can live entirely inside your perimeter.
- Sources
- Jira, GitHub, GitBook, Confluence, PDFs, DOCs, sites, APIs
- Retrieval
- Multi-source semantic search with hybrid re-ranking
- Quality
- Context scoring plus organisation-specific memory
- Locality
- On-premise vector storage — no external index required
Goes deeper
Governance & audit
The control plane that makes an AI programme defensible
Governance is not a dashboard bolted on afterwards. Tools, agents and models are registered before they can be invoked, permissions are enforced at the orchestration layer, and every action leaves an evidence trail an auditor can follow.
- Registry
- Controlled tool and agent catalogue — nothing runs unregistered
- Routing rules
- Explicit, reviewable model-routing policy
- Access
- Role-based permissions applied per agent and per tool
- Evidence
- Full audit log of AI actions, plus cost and quality tracking
Goes deeper
SEEMR self-evolving router
Four dimensions that improve without an engineering ticket
SEEMR — the Self-Evolving Model Router — runs four live dimensions continuously in production: model governance driven by a contextual bandit with five learning modes, agent personalities, the knowledge graph, and cost and energy optimisation.
All four dimensions are live. Autonomous RAG restructuring is on the public roadmap. Zero engineering overhead on the live dimensions after setup.
- Model governance
- Contextual bandit across five learning modes
- Personalities
- Agent behaviour adapts to observed outcomes
- Knowledge graph
- Entity and relationship enrichment over time
- Efficiency
- Autonomous cost and energy optimisation
Goes deeper
Model & tool abstraction
Swap the engine without rewriting the workflow
Workflows are defined against capabilities, not vendor SDKs. That decoupling is what lets a model be replaced, a local model be mixed with a hosted one, or a task be routed to a cheaper option — without touching business logic.
- Providers
- Change LLM provider without rewriting orchestration logic
- Placement
- Mix hosted and locally-served models in one Network
- Economics
- Route to cost- or energy-optimised models per task
- Longevity
- No vendor lock-in as models and regulation move
Goes deeper
Performance, cost & energy
AI that cannot be measured cannot be governed
Cost and energy are treated as first-class runtime signals, not a monthly invoice surprise. Every request carries performance metadata, and model efficiency is comparable across the estate rather than anecdotal.
- Per request
- Latency, token and cost metrics on every call
- Caching
- Cache hit-rate monitoring and concurrency tuning
- Comparison
- Model efficiency measured against real workloads
- Autonomy
- Continuous cost and energy optimisation in production
Enterprise integrations
First-class participants in the Network, not read-only connectors
An integration that can only read is a search box. Integrations here are registered tools an agent can act through — under the same permission model and audit trail as everything else in the Network.
- Jira
- Semantic search, backlog operations, delivery insight
- GitHub & GitBook
- Code, repository and living-knowledge intelligence
- Slack & Zoom
- Conversational orchestration and meeting intelligence
- Custom
- Internal services and tools over MCP
Deployment & sovereignty
Your infrastructure, your models, your perimeter
Deployment mode is a first-class choice, not an enterprise-tier upsell. The same platform runs in your data centre, in your private cloud, or fully disconnected — with the models you host and data that never crosses your boundary.
- On-premise
- Complete deployment inside your own data centre
- Air-gapped
- Operation with no outbound network dependency
- Your models
- Customer-hosted LLMs; no mandatory cloud service
- Data path
- Content stays inside your infrastructure
Goes deeper
04 — System architecture
What actually runs, layer by layer
Orchestration, retrieval, governance and deployment as separable layers — which is what makes each one measurable, replaceable and controllable on its own.
Input layer
User request Natural-language queries, API calls, webhooks
Decomposition engine
Intent parser NLU and context extraction
Task classifier Multi-label classification
Dependency analyzer Graph-based planning
Hierarchical planner
Strategy layer High-level goal setting
Tactical layer Sub-task sequencing
Execution layer Atomic action planning
Orchestration core
Task queue manager Priority scheduling, load balancing
Agent coordinator Dynamic agent allocation and monitoring
On-the-fly agent creator Just-in-time agents for new domains and tasks
State manager Distributed state tracking
Model routing
Model registry Hosted, open-weight and custom models
Smart router Cost, latency and capability-based routing
Load balancer Rate-limit handling, failover
AI agent network
Domain agentSpecialised expertise
Knowledge agentInformation retrieval
Analysis agentData processing
Execution agentAction execution
MCP server layer
Tool server Jira, GitHub, GitBook, Slack, custom APIs
Resource server Databases, file systems, external services
Prompt server Template management, injection prevention
RAG pipeline
Document ingestion PDF, MD, DOCX, web sources
Chunking strategy Semantic, recursive, sliding window
Embedding generation Hosted or locally-served embedding models
Vector store On-premise or managed vector storage
Retrieval engine Hybrid search with re-ranking
Knowledge graph
Graph database Entities, relationships, ontologies
Self-evolving learning
Feedback loop Quality scoring, reinforcement
Pattern recognition Emerging behaviour, optimisation
Deployment options
Reusable modules Packages and APIs for internal reuse
End-to-end execution trace
- 01IntakeRequest received
- 02DecomposeIntent and sub-tasks
- 03PlanExecution graph
- 04RouteModel and agent selection
- 05RetrieveRAG and knowledge graph
- 06ExecuteAgents and MCP tools
- 07LearnOutcome logged
- 08RespondSynthesised output
- Input & decomposition
- Orchestration
- Routing & tools
- Knowledge & memory
- Deployment
The same architecture runs unchanged in a connected data centre and in a fully disconnected one. That is the point of separating the layers.
Read the reference architecture05 — Control
Choose the perimeter. The platform doesn't change.
Deployment mode is a procurement and risk decision, so it is a first-class option rather than a tier. Pick the column that matches your constraints.
On-premise
Your data centre
Full deployment inside your own infrastructure, with LLMs you host. Nothing egresses by default.
- Customer-hosted models
- Internal identity and RBAC
- Your storage, your backups
Private / hybrid
Your cloud tenancy
Run in your own private cloud, with the option to burst specific tasks to hosted models under explicit routing policy.
- Per-task routing policy
- Mixed local and hosted models
- Cost and energy optimisation
Air-gapped
No outbound path
Operation with no external network dependency, for classified, regulated and critical-infrastructure environments.
- Offline model serving
- Sovereign data boundary
- Evidence for audit
06 — Evaluate
Pick the next step that matches where you are
Three different questions need three different conversations. Take the one that fits — the third needs nothing from you at all.
You have a target environment
Architecture call
Bring your constraints — hardware, network policy, identity, data residency — and we scope the deployment against them. Technical call, no slideware.
- Topology and sizing for your estate
- Integration and identity boundaries
- Air-gap and residency implications
You want to see it run
Technical walkthrough
A live Network executing a real request — decomposition, routing, retrieval, tool calls and the audit trail it leaves behind. Ask hard questions while it runs.
- A Network executing end to end
- The audit record it produces
- SEEMR routing under real load
You are building a shortlist
Do it yourself
Use our own evaluation material against every vendor you are considering, including us. No form, no call — take it and go.
- Enterprise AI agent RFP checklist
- Platform evaluation framework
- Flat vs pay-as-you-go cost modelling
07 — Fit
Honest about who this is for
A strong fit if you are
- An enterprise with data-residency or security constraints that rule out shared cloud AI
- Past the pilot stage and now accountable for AI in production
- Carrying four or five disconnected AI tools and no way to govern them together
- Preparing evidence for the EU AI Act or a sector regulator
- Building an operating model, not shipping a single feature
Not what this is
- A chat wrapper over someone else's API
- A single-model interface with a nicer UI
- A low-code automation toy
- A black-box assistant you have to take on trust
If a chat wrapper solves your problem, buy the wrapper. This is an orchestration and governance platform, and it is priced and deployed like one.
08 — Questions
The questions evaluators actually ask
What is an enterprise AI orchestration platform?
It is the layer that sits between a business request and the models, agents and tools that fulfil it. Instead of one model answering one prompt, an orchestration platform decomposes the request into sub-tasks, selects the right agents and tools for each, coordinates their execution, validates the result, and records the whole path. In an enterprise the orchestration layer is also where governance lives: model-routing policy, tool and agent registries, role-based permissions and audit logging all apply there rather than inside individual applications.
How is VDF AI different from an agent framework?
A framework gives engineers primitives to build an agent; you still own the runtime, the governance model, the retrieval pipeline and the audit trail. VDF AI ships those as the product. Agents and tools must be registered before they can be invoked, model routing is explicit policy rather than code, retrieval is an engineered pipeline with context scoring, and every action is logged for replay. The practical difference shows up at the second and third use case, when a framework project becomes a platform project.
Can VDF AI run fully on-premises or air-gapped?
Yes. On-premise, private-cloud and air-gapped deployments are supported directly, using LLMs you host yourself. There is no mandatory cloud dependency and no requirement for content to leave your infrastructure. See the on-prem reference architecture for the deployment topology, hardware considerations and integration boundaries.
How does VDF AI make multi-agent execution auditable?
Auditability comes from the orchestration layer owning the execution record. Every routing decision, tool invocation, agent hand-off and output is captured, so a given answer can be reconstructed after the fact — which model handled which sub-task, which documents were retrieved, which tool wrote which change. Cost, performance and quality metrics are recorded on the same request, so governance reviews and efficiency reviews read from one trail.
Which knowledge sources and enterprise tools does VDF AI connect to?
Knowledge sources include Jira, GitHub, GitBook, Confluence, internal document stores, PDFs and DOCs, websites, and custom databases and APIs. Integrations are registered tools rather than read-only connectors, so agents can act through Jira, GitHub, GitBook, Slack and Zoom under the same permission model and audit trail. Internal services can be exposed through the Model Context Protocol.
Does VDF AI lock us into a single LLM provider?
No — avoiding that is an explicit design goal. Workflows are defined against capabilities rather than a vendor SDK, so a provider can be replaced without rewriting orchestration logic, and hosted and locally-served models can be mixed inside the same Network. SEEMR routes each task to an appropriate model on cost, latency and capability grounds, which means adopting a new model is a routing change rather than a migration.
How is the platform licensed — per token or per seat?
Neither, on-premises: deployments are licensed as an annual capacity subscription you size in advance, with unlimited users, rather than a meter that bills after the fact. Cloud plans are per user. What that changes for orchestration is significant, because a governed multi-agent Network makes several calls per request — and the orchestration, routing, governance, audit logging and retrieval around those calls do not consume capacity at all. The flat-versus-token comparison sets out the arithmetic, and the pricing page covers cloud and on-prem licensing.
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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.