Proven in research,
finished in operation
Not demo-ware — technology that works inside the security, regulatory, and operational constraints enterprises actually have. Core engines, frameworks, and runtime connect into one stack, so a research result travels through the product and lands as a result in the field.
Operable
Operable
AgenticOps-based technology that runs steadily in a real enterprise environment, past the PoC stage
Independent
Vendor-independent
On-premise, model-agnostic design with no lock-in to a particular model or cloud
Connected
Connected knowledge
Ontology, knowledge graph, and GraphRAG connecting internal knowledge precisely
Extensible
Extensible
The MCP runtime plus Runtime SDK and API let you extend tools and agents freely
/ Engines
Engines
The XGEN engines make the AI follow the correct fact rather than the similar sentence (Ontology), and govern the whole execution around it (Harness). Precise knowledge context plus a verified execution environment is what makes a result you can rely on.
Ontology — a knowledge engine that follows relationships to the right fact
Vector search stops at finding fragments that look similar. XGEN Ontology RAG follows the relationships between your data, tracing not just what exists but why it is so and what it connects to. It behaves as a knowledge graph rather than a document search.
| Vector RAG | XGEN Ontology RAG | |
|---|---|---|
| Retrieval | Pinned Top-K (fixed) | Dynamic Top-k (adaptive) |
| Traversal | Similarity-based fragments | Relationship-based facts |
| Provenance | Weak source tracking | Complex question → traceable evidence path |
| Storage | Vector DB · dimensional | Graph DB · factual relationships |
A vector finds the sentence that looks right; an ontology follows the fact that is right
Harness — engineering the entire environment the AI works in
What moves an AI is not the model but its environment. Harness Engineering goes past prompt and context to govern the whole environment the LLM actually works in, so execution stays dependable.
Prompt Engineering
Carefully designing the sentences that tell the LLM what to do
Context Engineering
Designing what the LLM consults and which tools it reaches for when answering
Harness Engineering
Governing the whole working environment — instruction, context, action, and verification
A feedback loop that keeps improving — below-threshold scores trigger a retry, blocking hallucination and error
- Consistency — a fixed 9-stage / 3-phase pipeline
- Token efficiency — cascade compression and progressive disclosure keep context from being wasted
- Control down to a single message fragment, without depending on the LangChain core
/ Frameworks
Frameworks
The frameworks that connect the engines to actual work — operational intelligence, graph and hybrid retrieval, and context design, combined to fit the job.
Operational intelligence
AgenticOps
The layer that binds Ontology and Harness together, so precise knowledge context and a verified execution environment produce a result you can trust.
Knowledge graph retrieval
GraphRAG
Traverses document-level relationships instead of similar sentences, finding the answer across a wider field. It reasons as a knowledge graph, not a document search.
Vector + graph
Hybrid RAG
Hybrid retrieval that combines vector similarity with graph relationships, holding precision and breadth at the same time.
Context design
Context Engineering
Designing the context itself — what the LLM consults and which tools it uses when it answers.
/ Architecture
Architecture
Trustworthy Enterprise AI comes from the architecture, not the model. XGEN defines one blueprint — from base infrastructure through design principles, reference architecture, and platform structure — so it transplants safely into a real enterprise environment
Explore the architectureFoundation
The infrastructure and data layer, including on-premise and network-separated deployments
Design principles
The design standards running through security, governance, and scalability
Reference architecture
An Enterprise AI blueprint you can apply directly to a rollout
XGEN platform
The platform structure connecting engines, frameworks, and runtime