Capabilities

Continuous AI/ML and LLM-agent scanning of 100% of transactions for anomalies and control failures, replacing sample-based audit testing.

Centralized coordination of AGVs and AMRs for material transport — dispatching, routing, traffic management, and handoff with fixed automation.

A comprehensive registry of all AI systems in use or development, classified by risk tier per EU AI Act and organizational risk appetite.

Organizational policies defining when, how, and under what constraints AI may be used — covering development, procurement, deployment.

Systematic evaluation of risks posed by individual AI systems — bias, safety, privacy, security, reliability — with documented impact analysis.

Machine-learning surrogates trained on physics simulations that deliver near-instant predictions at a fraction of traditional solver cost.

ML systems that continuously monitor, classify, and surface competitively relevant signals from structured and unstructured data at scale.

AI agents that automatically complete, review, and score vendor risk assessments — reducing assessment cycle time from weeks to minutes.

ML-driven analysis of supply-chain sustainability risks, hotspots, and decarbonization opportunities across thousands of suppliers simultaneously.

ML models that screen thousands of potential acquisition targets against strategic criteria, identifying high-fit opportunities faster than manual.

ML-driven detection that builds behavioral baselines for users, devices, and networks, surfacing anomalies invisible to signature-based rules.

Apply ML to correlate, deduplicate, and prioritize alerts in real-time, routing enriched incidents to the correct responder automatically.

A centralized platform for designing, publishing, and documenting APIs with consistent standards that accelerate internal and external integration.

API-first integration strategy enabling real-time, programmatic data exchange with partners and ecosystem participants beyond traditional EDI.

ATS-anchored hiring enforcing structured scorecards and interviewer training that drives 2x more accurate decisions and reduced discrimination risk.

A comprehensive digital record of every product deployed in the field, tracking configuration, service history, and lifecycle status.

A measurement framework tracking asset-level and portfolio-level performance metrics — availability, reliability, OEE.

A centralized registry of all physical assets tracking identity, location, condition, ownership, and lifecycle stage from acquisition through.

AI-driven close execution with 99%+ auto-matching, auto-posted journal entries, and ML anomaly flagging — humans review exceptions only.

ML spend taxonomy classification — the data foundation enabling category strategy, tail spend management, and Scope 3 estimation at scale.