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Trustworthy AI for Impact
We empower enterprises to confidently leverage AI. Our advanced pipelines transform complex data into precise, reliable decisions, safeguarded by comprehensive safety and optimized with transparent cost controls.
What we build, end to end
Our engineering practice spans the full lifecycle of production AI systems. From ingestion and training through evaluation, deployment, and continuous operations, we build infrastructure that scales with your mission-critical workloads while maintaining strict governance and cost discipline.
Training at scale
Reproducible training runs with comprehensive checkpointing, scheduled and ad-hoc job orchestration, and budget caps that prevent cost overruns. Our training infrastructure supports distributed workloads across multiple nodes with automatic fault recovery and detailed resource utilization tracking.
Evaluation & safety
Offline evaluation harnesses with standardized benchmarks, red-team testing suites, human-in-the-loop review workflows, and structured incident response protocols. Every model undergoes rigorous pre-deployment testing against domain-specific safety criteria and adversarial scenarios.
Serving & operations
Low-latency inference endpoints with intelligent quotas, multi-layer caching strategies, comprehensive observability dashboards, and drift detection systems. Real-time monitoring ensures production models maintain performance standards and flag anomalies immediately.
Focus sectors
We concentrate on domains where decisions are time-critical and error-intolerant. Our sector expertise combines deep understanding of operational requirements with AI engineering discipline, ensuring systems that integrate seamlessly into existing workflows while delivering measurable impact.
Supply Chain & Logistics
Demand forecasting, inventory optimization, routing and ETA prediction, anomaly detection in shipment patterns, and retrieval-augmented generation over product catalogs and standard operating procedures.
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Energy &
Utilities
Load and price forecasting, renewable energy integration planning, predictive maintenance for generation and transmission assets, and grid planning simulations for capacity expansion.
Manufacturing & Industrial IoT
24/7 computer vision quality control, yield optimization across production lines, fault classification from sensor streams, and digital twin simulations for manufacturing plants and individual production lines.
From data to decisions, with guardrails
01
Unified data foundations
Versioned datasets with lineage tracking, validation checks at ingestion, and standardized schemas that enable reproducible experiments and auditable training runs.
02
Domain model training
Documented runbooks for model development, automated hyperparameter tuning within cost constraints, and systematic adaptation of foundation models to sector-specific requirements.
03
Pre-release evaluation
Comprehensive offline testing against held-out validation sets, adversarial examples, and domain expert review before any model reaches production endpoints.
04
Continuous monitoring
Real-time performance tracking post-deployment, automated drift detection, clear SLOs with alerting thresholds, and documented rollback paths when issues arise.
Every stage incorporates cost budgets, quality gates, and compliance checkpoints. Our engineering discipline ensures that models move from concept to production with full traceability and governance.
Safety and cost by design
Built-in governance at every stage
Privacy-by-design principles guide our architecture choices. We offer regional data residency options to meet local compliance requirements, ensuring sensitive training data never leaves designated geographic boundaries. Model risk management frameworks are embedded into development workflows, not bolted on afterward.
All systems operate under least-privilege access controls with role-based permissions, automated secrets management, and time-bound credentials. Security reviews occur at each pipeline stage, from data ingestion through model deployment.
Read our complete Safety & Governance framework →
Research rigor, engineering discipline
An ASEAN-headquartered applied AI lab
Nadi Systems bridges academic-grade inquiry with production engineering practices. Our team combines researchers who publish in peer-reviewed venues with platform engineers who have scaled systems to millions of requests per day. This hybrid culture ensures we stay current with AI advances while maintaining the operational discipline required for enterprise deployment.
Regional presence in ASEAN gives us direct understanding of local regulatory landscapes, infrastructure realities, and business practices. We design systems that work within actual constraints rather than idealized conditions.
Tell us about your use case
Start a conversation with our engineering team
Share your problem statement, relevant data domains, timeline constraints, and any specific compliance or operational requirements. We'll respond with an initial technical assessment, potential approaches, and next steps for a deeper discovery engagement.
Whether you're exploring AI capabilities for the first time or looking to upgrade existing systems with more robust infrastructure, we're here to provide engineering-led guidance.
Nadi Systems builds training, evaluation, and serving pipelines for supply chain, energy, and manufacturing—designed with safety and cost guardrails. Home • Solutions • Safety & Governance • Contact • Privacy
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