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We focus on domains where decisions are time-critical and error-intolerant. Each solution combines training, evaluation, and serving—with safety and cost guardrails built in.
Three domains, one engineering approach
Applied AI where reliability matters most. We work with organizations operating in high-stakes environments where system failures carry operational, financial, or safety consequences. Our solutions are built on shared infrastructure—consistent data pipelines, evaluation frameworks, and serving architecture—adapted to the specific demands of each domain.
Every implementation follows the same engineering discipline: versioned datasets, reproducible training runs, pre-deployment evaluation gates, and runtime monitoring with automated rollback. This consistency reduces integration risk and accelerates time to production across diverse use cases.
Supply Chain & Logistics
Demand forecasting, inventory optimization, routing and ETA prediction, anomaly detection, and retrieval-augmented generation over product catalogs and standard operating procedures.
Energy & Utilities
Load and price forecasting, renewable energy integration planning, predictive maintenance for generation and distribution assets, grid planning simulations under variable conditions.
Manufacturing & Industrial IoT
24/7 computer vision quality control, yield optimization, fault classification from sensor streams, digital twins for production lines and entire plants.
From data to decisions—reliably
Our approach treats AI deployment as a systems engineering problem. Each phase—from data preparation through production serving—includes explicit quality gates, cost controls, and audit mechanisms. This structure gives technical teams the visibility and control they need to maintain system reliability at scale.
Data foundations
Versioned datasets with documented provenance and privacy-by-design. Every dataset snapshot includes lineage metadata, schema validation results, and access audit logs. Data preparation pipelines are reproducible and support regional residency requirements.
Training
Reproducible pipelines with scheduled and ad-hoc runs, budget caps enforced through pre-approved compute envelopes. Training jobs log hyperparameters, resource utilization, and intermediate checkpoints. Cost attribution is tracked per workload with configurable alerting thresholds.
Evaluation
Offline evaluation harnesses, red-team test suites, and human-in-the-loop approval gates before any production release. Evaluation includes domain-specific metrics, adversarial test cases, and comparative benchmarks against baseline models. No model ships without passing defined quality thresholds.
Serving
Low-latency inference endpoints with quotas, caching strategies, distributed tracing, metrics collection, structured logging, and automated drift monitoring with rollback capabilities. Runtime monitoring detects distribution shifts, performance degradation, and cost anomalies—triggering alerts or automated failover to previous model versions.
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Engineering rigor, operational clarity
Effective AI systems require more than accurate models—they need operational discipline, clear accountability, and predictable cost structures. Our platform provides the tooling and processes that engineering teams expect from production infrastructure.
We maintain provider-neutral architecture to prevent lock-in while offering regional data residency options for compliance requirements. Every workload operates under defined service level objectives with cost guardrails enforced at runtime.
Policy artifacts—including model cards, system cards, change logs, and audit trails—are generated automatically and maintained as part of the deployment process. These artifacts support compliance reviews, incident investigations, and knowledge transfer.
Provider-neutral architecture
Deploy across cloud providers or on-premises infrastructure without vendor lock-in. Regional data residency options support jurisdiction-specific requirements for data sovereignty and privacy regulations.
Clear SLOs and cost guardrails
Per-workload visibility into latency, throughput, error rates, and compute costs. Budgets are enforced through pre-approved envelopes with automatic alerts when thresholds approach limits.
Policy artifacts
Automatically generated model cards, system cards, change logs, and audit trails. Documentation includes training data characteristics, evaluation results, known limitations, and operational parameters.
Built for collaboration
Comprehensive handover documentation, operational runbooks, and optional skills transfer programs. Teams receive the artifacts and knowledge needed to maintain and extend systems independently.
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Frequently asked questions
Technical buyers evaluating applied-AI solutions often ask about integration patterns, cost management, security posture, and operational handover. Below are direct answers to the questions we hear most often.
Can you integrate with our existing data lake or warehouse?
Yes. Our data pipelines connect to common lake and warehouse patterns including Delta Lake, Iceberg, Snowflake, BigQuery, Redshift, and S3-based architectures. Connectors support incremental sync, schema evolution, and partition-aware reads. We work with your existing data infrastructure rather than requiring migration.
Do you replace existing planning or SCADA tools?
No. We augment and, where appropriate, wrap existing operational tools with improved forecasts, anomaly detection, and validation checks. Integration occurs at the API or message bus layer, preserving established workflows while adding AI-driven insights. Your teams continue using familiar interfaces.
How do you manage cost at scale?
Through budget caps, pre-approved compute envelopes, and per-workload cost attribution with configurable alerts. Training and inference jobs include resource quotas enforced at runtime. Cost dashboards provide visibility into spending by project, model, and time period—enabling proactive budget management.
How do you handle security and access control?
Least-privilege access policies, multi-tenant isolation, encryption in transit and at rest, and auditable change controls. Role-based access control integrates with existing identity providers. All data access, model deployments, and configuration changes generate audit events with tamper-evident logs.
What does handover look like?
Handover includes architecture documentation, operational runbooks, troubleshooting guides, and training data/model lineage. Optional build-operate-transfer engagements include skills uplift programs where your team shadows operations before taking full ownership. The goal is self-sufficiency, not dependency.
Tell us about your use case
Share your problem statement, data domains, timeline constraints, and any specific technical or compliance requirements. We'll respond with an assessment of fit, implementation approach, and next steps.
Our engineering team reviews each inquiry to determine whether our platform addresses your specific needs. We prefer transparent conversations about feasibility, timelines, and resource requirements over premature commitments.
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Build reliable AI systems in high-stakes domains
Nadi Systems provides training, evaluation, and serving infrastructure for supply chain, energy, and manufacturing applications—designed with safety guardrails, cost controls, and operational clarity from the start.
01
Assess your requirements
Review problem scope, data characteristics, integration points, and success criteria with our engineering team.
02
Design the solution
Define architecture, data pipelines, evaluation frameworks, service level objectives, and cost budgets.
03
Implement with oversight
Build, test, and deploy with staged rollout, monitoring, and documented handover procedures.
04
Operate and optimize
Monitor performance, investigate incidents, retrain models, and refine based on production feedback.
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