Probabilistic Perception
Extract structured signals from multimodal chaos: documents, drawings, operational logs, images, policies and business events.
Neuro-symbolic enterprise AI
AI is probabilistic. Enterprise workflows are deterministic. We bridge the gap.
Golem Tech builds neuro-symbolic architectures for finance, logistics, and regulated operations. We allow you to use the reading power of modern AI, but we force it through a rigid, mathematical sieve before it touches your systems of record.
Probabilistic perception.
Modern AI reads documents, images, policies and operational signals.
Modern AI reads documents, images, policies and operational signals.
Deterministic control.
Approved workflow rules bound the model to policy-admissible values and actions.
Approved workflow rules bound the model to policy-admissible values and actions.
Sovereign execution.
Fine-tuning and governed inference deploy in controlled environments.
Fine-tuning and governed inference deploy in controlled environments.
Physical constraint, not post-hoc checking.
Most vendors let AI generate freely and try to catch invalid output later. We incorporate approved rules into model adaptation, producing a policy-bound model whose workflow action space is structurally constrained before deployment.
Deterministic Governance
Constrain every generated value against mathematical, policy and engineering rules before the output reaches the workflow.
Auditability by Design
Attach provenance, model lineage, policy version and validation path to every governed payload for audit and supervision.
Empirical proof. Industrial benchmarks.
Measured on bounded workflows where probabilistic perception is constrained by deterministic rules before operational use.
6.38%Lower routing cost
Demonstrated in standardized routing benchmarks versus a classical genetic baseline, validating constrained optimization before logistics integration.
8.06%Lower routing cost
Demonstrated in standardized routing benchmarks versus a local-search baseline, with operational constraints enforced throughout evaluation.
>99%Control precision
Measured on finance workflow benchmarks covering invoice validation and reimbursement-policy logic under strict rule constraints.
0.0%False acceptance
Measured after deterministic engineering-rule checks in controlled technical-validation benchmarks.
Logistics Operations Audit
Benchmarked historical routes against constrained optimization scenarios to quantify mileage, empty kilometers, fill rate, service-window adherence, waiting time and carbon impact before live integration.
Finance Compliance Pilot
Validated invoice and reimbursement proposals against structured policy rules, producing blocked decisions, exception reasons, review status and audit-ready evidence.
One neuro-symbolic engine. Multiple deployed workflows.
Golem’s core architecture powers high-stakes environments where AI must be constrained by strict rules, from systematic trading and capital markets to industrial logistics and supply chain onboarding.
Golem GenAI
Neuro-symbolic generative intelligence for enterprise documents, workflow assistance, data questions and operational decision support.
- Grounded answers with traceable sources and policy lineage
- Fine-tuned responses constrained by deterministic rules
- Packaged for governed inference and enterprise deployment
Golem Systematic Trading
Systematic trading infrastructure where signals, execution logic, risk limits and supervisory controls are constrained by deterministic governance.
- Rule-bound signal and execution workflows
- Inference-time risk limits and exception controls
- Traceable decisions for supervision and audit
Golem Advisory Controls
Advisory control infrastructure for investment processes where generated recommendations must be explainable, suitability-constrained and reviewable by qualified professionals.
- Investor-profile, mandate and policy execution constraints
- Portfolio recommendation provenance
- Advisor-facing review and evidence capture
Golem Logistics
Digital twins and constrained optimization for transport networks where cost, service level, carbon impact and operational rules interact.
- Offline benchmark before integration
- Scenario simulation on historical route data
- Container-ready connection to existing systems
Golem Vision Control
Multimodal extraction combined with deterministic engineering checks when drawings, photos or field documents must become reliable structured data.
- Engineering plausibility constraints
- Engineering constraints for inadmissible configurations
- Sovereign training and inference options
Golem Enterprise Control Layer
A complementary control layer for accounting, reconciliation and operational workflows before final validation in enterprise systems of record.
- Validation, blocking or governed review
- Certified payloads for downstream systems
- Policy updates without changing the core system
Engineered for workflows that reject probabilistic drift.
Golem converts probabilistic model outputs into governed enterprise payloads through policy-constrained model adaptation, qualification, provenance and controlled deployment.
Neuro-symbolic AI
Deterministic inference
Finance controls
Systematic trading
Advisory controls
Constrained optimization
Multimodal onboarding
Deterministic Governance
Cryptographic provenance
End-to-End Managed Pipeline.
Golem packages the Lifecycle & Training Orchestrator and the Governed Inference Service into sovereign deployments. Fine-tuning, policy validation, model registry, governed inference and endpoint serving can run through Azure Marketplace, AWS Marketplace on Amazon EKS, Google Cloud GKE, Swiss Cloud, or on-premise environments.
Policy and data ingress
Structured policies, datasets and operational evidence enter through secure files, controlled folders, managed APIs or private storage.
Lifecycle & Training Orchestrator
Training jobs, model registration, artifact tracking and policy validation are managed as a controlled lifecycle rather than ad hoc experimentation.
Governed Inference Service
Governed inference endpoints are deployed as sovereign, containerized services for Azure Marketplace, AWS Marketplace on Amazon EKS, Google Cloud GKE, Swiss Cloud, or on-premise runtime control.
Choose your deployment path.
Deploy the Bounded Agentic AI Workflow Engine through the cloud path that fits your infrastructure and procurement model. Azure, AWS and Google Cloud include dedicated product, architecture, and implementation resources; Google Cloud also includes the GKE deployment and architecture guide.
Design
Define the structured policy, domain constraints and evaluation dataset that govern acceptable outputs.
Train
Launch managed fine-tuning against approved base models using the client’s rules and supervised examples.
Constrain
Incorporate the approved workflow topology into model adaptation so only policy-admissible values, states and actions are available for governed execution.
Serve
Deploy the governed inference endpoint with traceable payloads, model metadata and controlled portability.
Deploy one governed inference workflow.
Define the policy asset, model-adaptation scope, governed inference boundary and deployment target for one high-stakes workflow.
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