Predictive models and agentic systems look different, but enterprises need the same things from both: performance, interpretability, validation, governance, reproducibility and evidence. GeometriX supplies them from a shared foundation.
Build models whose behavior can be understood, not merely measured. Interpretable model zoo, wrappers for models built anywhere, calibration and explainability.
Explore Model Studio →Accuracy tells you how a model performs on average. Assurance tells you where, when and why it fails — eight test families plus challenger comparison.
Explore Model Assurance →A governed orchestration loop that owns prompt construction, tool dispatch, memory and policy enforcement — with behavioral contracts and gated tools built in.
Explore Agent Studio →Don’t ask another LLM whether your agent is correct. Test it against evidence — DOE-driven campaigns scored on graph-verified ground truth.
Explore Agent Assurance →Deterministic runtime governance that surrounds the agent loop: policy pipeline, claim verification, signed policy bundles, drift monitoring, audit evidence.
Explore Governance →The shared substrate beneath the agentic products: GMS geometric memory, the KAL adapter mesh, and graph-verified ground truth.
Explore the technology →The agent Harness ships with 33 registered tools: 8 for geometric knowledge stores, 24 for model development and validation, plus persistent geometric memory. An agent can load data, train an interpretable model, diagnose its weaknesses and record the evidence without leaving the loop.
h = Harness(HarnessConfig(stores_dir="gms_stores")) h.register_gms_tools() # geometric expert stores: create, ingest, query, search h.register_modeva_tools() # load data, train, diagnose, explain, interpret, compare h.run("Load BikeSharing, train XGB depth-2, run FANOVA, diagnose accuracy")
Nothing gets replaced. GeometriX integrates with your cloud ML platforms, MLflow, LangChain, LangGraph, CrewAI and AutoGen; with your LLM providers, from Anthropic and OpenAI to Bedrock, Azure and local models; and with the graph and vector stores you already operate, including Neo4j, PostgreSQL with pgvector, SPARQL endpoints and MCP sources.
