EVIDENCE-GATED KNOWLEDGE INFRASTRUCTURE
Your foundation model improves itself every week. The knowledge it runs on does not. ROES delivers evolution as a service: real-world change becomes evidence-backed knowledge updates — machine-verified, human-gated, auditable, reversible. Agents and ontologies that co-evolve are the means. Knowledge that stays current is the point.
WHY ENTERPRISE AI STALLS · FOUR BREAKPOINTS
Regulations, products, and failure modes change weekly — static knowledge bases and one-off ontologies do not. The AI keeps answering with yesterday's knowledge, risks slip through, and domain experts repeat the same maintenance by hand.
General models generate fluent answers yet cannot reliably reason over your entities, relations, rules, and permissions — and every department defines the same concept slightly differently, so agents understand the business inconsistently.
Accuracy, latency, and cost dashboards cannot answer the question that matters: did this change actually improve the business result?
Prompts, models, ontologies, and rules each drift on their own. When something breaks, no one can trace it, reproduce it, or roll it back.
ROES's premise: an ontology should not be a static modeling deliverable, and an agent should not be a fixed workflow. Both must learn — inside one auditable loop.
MECHANISM · THREE AGENTS, ONE LOOP
Continuously discovers, extracts, and disambiguates entities from authoritative sources, then proposes verifiable ontology patches.
Checks facts, structure, relations, constraints, temporal consistency, and source quality; proposes restructures or casts vetoes.
Runs frozen benchmarks, adversarial tests, and real-outcome evaluation across intrinsic, task, and business layers.
STAGED EVOLUTION LOOP
Agents never modify the production ontology directly. Every change lands as a candidate patch with its evidence attached, and publishes only after re-validation and human approval — versioned, reversible, auditable. Experiment orchestration, permissions, evaluation, versioning, approval, staged rollout, rollback, and audit all live in the RSI Harness.
POSITIONING · THE RSI STACK
Recursive self-improvement (RSI) is not one system but a layered stack. AI companies keep advancing the model layer; ROES owns everything above it — the agent loop and the knowledge it runs on.
The orchestration layer. Assembles agents, tools, scheduling, and gates — and improves itself through the same governed propose → experiment → gate → promote loop it enforces on everything else.
The knowledge layer. Evidence-gated ontology evolution: every change carries source, timestamp, and content hash — machine-verified, human-gated, and released as an immutable, supersededable version.
The foundation layer. Model-level self-improvement belongs to the AI companies. ROES trains no models; it turns any model's progress into safe evolution of your semantic system.
ROES does not train foundation models and does not compete on the model layer. Above the model, it owns evidence-constrained, governed evolution of agents and ontologies.
GET IN TOUCH · GUESTBOOK
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