Stryk™
The AI Engine for Critical Business Operations
Decision Fabric connects real operational objects — mining equipment, banking transactions, industrial signals — with AI agents that make decisions about them. Continuous evaluation, per-decision traceability, executable guardrails. Zero to production in 30 days.
From data ingestion to autonomous decision — each module purpose-built for production AI systems that cannot fail.
The core intelligence layer that connects real business operational objects — equipment, transactions, signals — with the AI agents making decisions about them. The backbone of xStryk™.
The Decision Intelligence core: evaluates, traces, and executes AI decisions under strict operational constraints with verifiable, auditable output at every inference.
Continuous model evaluation with regression suites, slice analysis, and statistical drift detection — production-gated at every release so degraded models never ship.
Data pipelines with validation, full lineage, and quality gates — connecting ERPs, IoT sensors, and unstructured documents into a clean, governed feature store.
Observability, monitoring, and circuit breakers for production AI systems — with SLA-bound alerting and automatic model rollback before an incident becomes a crisis.
From zero to a real production AI system in 30 days. Data integration, Decision Fabric configuration, agent deployment, evaluation suites, and operational guardrails — all delivered.
Our Decision Intelligence infrastructure: deployed where mathematical precision and deterministic evaluation are imperative for operational survival. Deterministic convergence with legacy telemetry protocols.
Real-time digital representation of physical systems or business processes for predictive analysis, optimization, and simulation of complex scenarios.
Every decision evaluated. Every model monitored. Every alert traceable.
Migrate from legacy systems to modern Decision Intelligence infrastructure.
We build intelligent systems that move from prototype to operations: continuous evaluation, data QA, traceability, and measurable controls—adoptable in stages.
End-to-end orchestration: runs suites, captures traces, and produces release-ready evidence packs to operate AI as infrastructure.
Data contracts, automated checks, and statistical sampling for datasets that hold up under drift and audit.
Evaluation system for LLM/ML: suites per use case, failure-mode taxonomy, and actionable reporting to ship changes with control.
Real-time signals for quality and cost: monitoring, alerts, canary releases, and runbook-driven improvement loops.
Every deployment ships with verifiable artifacts: evaluation, data QA, release gates, and operational runbooks.
Domain metrics, edge cases, regressions, and thresholds to ship changes with control.
Rules, statistical sampling, and gold sets for production-grade datasets.
Procedures, owners, SLAs, and playbooks to run AI without improvisation.
Evidence per release and per decision: changes, approvals, and controls.
Real-time decision infrastructure: trusted data, evaluated models (suites + regressions), and continuous operations with control and evidence.
To turn AI into critical infrastructure: lineage-backed telemetry, evaluation suites with deployment gates, and an operating model with runbooks. Less "isolated innovation", more deterministic control.
From data to production, with measurable control.
We build pipelines and datasets with measurable quality, not wishful thinking.
Not "ethics" as a slogan—operable engineering. We mitigate risks, test robustness, implement guardrails and executable governance for intelligent systems in production.
We identify and mitigate novel risks that AI presents through systematic red teaming and adversarial testing.
We test and guarantee explainability and robustness to understand how and why systems make decisions.
Custom security features for your models: permissions, approvals, limits, and measurable allow/deny policies.
Executable governance and regulatory compliance to operate AI in regulated and critical environments with confidence.
Clear answers on xStryk access model, deployment and differentiation.
xStryk is the proprietary platform that powers all xSingular projects. Access to xStryk is provided in the context of a consulting engagement — its implementation, configuration and integration are part of the service. We do not offer standalone software licenses without technical support. If xStryk is not the right fit for your organization, we say so from the start.
xStryk deploys on the client's infrastructure: on-premise or in their Google Cloud Platform (GCP) account. There is no dependency on xSingular servers in production. The client retains full control of their data, models and decisions. This is part of our commitment to confidentiality and privacy-by-design.
The xStryk platform includes four modules: xStryk Engine (decision core and guardrails), xStryk Eval (continuous evaluation, regressions and drift), xStryk DataOps (data pipelines with contracts and traceability) and xStryk Ops (observability and circuit breakers). Initial configuration for the client context, integration with existing systems, technical team training and ongoing support are additional consulting services.
Unlike generic platforms such as Aera Technology or ConverSight, xStryk is not a pre-packaged solution: it is a platform designed for production AI systems with decision-level traceability, executable guardrails and continuous evaluation requirements. It is built on the Google Cloud ecosystem (Vertex AI, BigQuery) and is adaptable to the particularities of each industry — mining, banking, energy, critical infrastructure.
xSingular combines expert Decision Intelligence, MLOps and AI Safety engineering with xStryk, our proprietary platform. We don't sell APIs or generic models: we design, build, and operate AI systems for our clients' critical decisions.
30 minutes to evaluate your use case, define success metrics, and scope the engagement. No commitment.
