01 Live dimension 01
Model Governance
Continuously improves model, tool, and workflow routing while preserving policy guardrails and auditability.
SEEMR FRAMEWORK
Self-Evolving Model Router (SEEMR) for governed AI systems.
SEEMR is VDF AI's Self-Evolving Model Router: the public architecture view covers four live dimensions, a LinUCB routing system with five operating modes, and autonomous RAG restructuring running in production today.
4 live dimensions 5 LinUCB modes Governed execution Autonomous RAG live
Enterprise prompts Organizational context Governance policies
SEEMR
Self-Evolving Model Router
How context, routing, and bounded learning stay aligned in production.
Model Governance
Agent Personalities
Knowledge Graph
Cost and Energy Optimisation
Implemented Autonomous RAG restructuring
Public by design Shows operating logic, not tuning recipes.
Live today Four self-evolving dimensions already run continuously in production.
Built for enterprise AI On-prem, hybrid, and regulated deployments stay in scope.
ROUTING COMPARISON
How It Differs from Basic Routing
SEEMR treats routing as a governed, learning control problem—not a fixed rule table pulled from config alone.
| Feature | Basic Router | SEEMR |
|---|---|---|
| Rules | Static | Adaptive |
| Learning | None | Continuous |
| Optimization | Single metric | Multi-objective |
| Feedback loop | No | Yes |
| Evolution | No | Yes |
FLOW VIEW
Conceptual Architecture
A simplified path from request to output: features inform policy, policy selects execution tiers, then results are aggregated—while feedback closes the loop around the SEEMR core.
ARCHITECTURE DIAGRAM
Four live dimensions, one routing core, five learning modes
The page is intentionally conceptual. It shows what the system optimizes for and where teams use it, without disclosing implementation thresholds, reward tuning, or fallback recipes.
Requests and tasks
Knowledge and provenance
Policies and constraints
Live feedback loops
Implemented today
The four live SEEMR dimensions
02 Live dimension 02
Agent Personalities
Keeps role-specific behavior consistent across teams, use cases, and operating environments.
03 Live dimension 03
Knowledge Graph
Connects entities, context, and provenance across fragmented enterprise systems so retrieval stays grounded.
04 Live dimension 04
Cost and Energy Optimisation
Steers execution toward better efficiency so quality, latency, and operational spend can be balanced in production.
Adaptive routing runtime SEEMR
Coordinates model choice, tool choice, and workflow adaptation across enterprise constraints.
Model selection Best-fit intelligence for each request.
Tool selection Policy-aware access to the right capabilities.
Workflow adaptation Execution paths improve as outcomes accumulate.
Routing intelligence
LinUCB operating modes
01
Auto
Adaptive default for mixed workloads that benefit from live exploration and exploitation.
Typical enterprise copilots, assistants, and multi-step workflows. 02
Pinned
Keeps routing fixed when a workflow has already been validated and should remain stable.
Production tasks with approved models, tools, or prompt chains. 03
Capability
Prioritizes hard technical constraints such as on-prem support, modality, or tool access.
Tasks that must meet exact execution requirements before quality tuning begins. 04
Energy
Biases routing toward more efficient execution when scale and operational footprint matter.
High-volume internal workloads where waste reduction is part of the KPI. 05
Regulated
Applies stricter policy-aware routing for sensitive environments, reviews, and controlled execution.
Compliance-heavy domains, restricted deployments, and governance-first use cases.Implemented
Autonomous RAG restructuring
A live self-evolving layer that reorganizes retrieval structures and evidence paths as enterprise knowledge changes over time.
Live today
Conceptual diagram only. Internal scoring logic, reward calibration, and fallback mechanics are intentionally omitted.
LEARNING VISIBILITY
Users can monitor how the system learns over time
The runtime is designed to stay observable. Teams can review learning behavior, track routing outcomes, and monitor optimization patterns without turning the public architecture page into a blueprint of internal tuning logic.
Product view for monitoring learning signals, routing outcomes, and ongoing system adaptation.
USE CASES
What this architecture is for
The page focuses on operating outcomes rather than internals: where governed routing and self-evolving enterprise context make a practical difference.
Regulated knowledge assistants
Grounded answers across internal systems while routing remains policy-bound and auditable.
Engineering and delivery intelligence
Multi-source retrieval, model selection, and workflow adaptation for planning, triage, and execution support.
On-prem and hybrid decision support
Capability-aware routing keeps execution aligned with local infrastructure, approved models, and latency limits.
Cost-aware operational AI
Energy-sensitive routing helps scale routine analysis, search, and summarization without defaulting to the heaviest model.
NEXT STEP
See how the runtime fits into the broader platform
Explore the technical overview for the full product context, or request a walkthrough focused on your deployment constraints.