AI Operations (MLOps) reliable, compliant models.
Keep AI models monitored, cost-optimized, and compliant in live production. Latency tracking, drift detection, automated fallback models, and token cost caps.
Shipping a model to production is 10% of the challenge. MLOps is the operational discipline that guarantees reliability, cost control, and compliance.
Models in production rot silently. Shifts in user behavior, market trends, and upstream data formats degrade accuracy without throwing server errors. Steigern Tech builds rigorous MLOps architectures that alert you to data drift before users notice.
Multi-provider failover routing
Unified gateway automatically switching traffic between OpenAI, Anthropic, and local models if an upstream provider experiences outages.
Strict token spend capping
Granular per-tenant rate limits, semantic prompt caching, and real-time alerts preventing accidental billing spikes.
Continuous quality monitoring
Automated telemetry tracking concept drift, latency degradation, and hallucination rates in live production streams.
Six operational pillars, rock-solid reliability.
AI operations bridges data engineering, DevOps, and machine learning: automated testing of data distributions, automated model re-validation, and zero-downtime blue/green model swapping.
Multi-LLM gateway & failover
Intelligent proxy layer routing requests across models with automated load balancing, retries, and fallback mechanisms.
Semantic caching & budget caps
Store semantic embeddings of common prompt answers, slashing API expenditures by up to 50% for high-frequency queries.
Latency & hallucination telemetry
Full observability with Prometheus, Grafana, and LangSmith recording every token latency, error rate, and model cost metric.
Automated regression testing
Continuous integration pipelines testing new prompts and model weights against business test suites before canary deployment.
Model registry & version lineage
Track exact dataset versions, hyperparameter configs, and evaluation metrics with MLflow for rapid point-in-time rollbacks.
Audit logging & compliance
Comprehensive immutable audit trails ensuring data handling adheres to SOC 2, HIPAA, and GDPR enterprise standards.
From brittle scripts to resilient MLOps, with 24/7 observability.
We bring software engineering discipline to AI systems. Automated testing, reproducible model builds, and real-time observability from day one.
MLOps maturity audit
Audit training pipelines, deployment mechanisms, and observability gaps across current production models.
Model registry & CI/CD design
Implement MLflow / DVC for versioned datasets, model weights, and automated evaluation tests in GitHub Actions.
Automated evaluation gates
Configure automated regression tests and threshold assertions before any model can be promoted to staging.
Telemetry pipeline POC
Deploy real-time inference logging with latency, token usage, and accuracy monitors in a staging environment.
Canary deployment setup
Build blue/green routing for zero-downtime model deployments with automated rollback on latency or error spikes.
Drift monitoring & alerting
Configure Evidently AI / Prometheus alerts for data distribution skew, input anomalies, and output decay.
MLOps maturity audit:Audit training pipelines, deployment mechanisms, and observability gaps across current production models.
full CI/CD pipeline, canary deployment infrastructure, and live telemetry dashboards
telemetry maintenance, drift threshold tuning, and automated retrain loops
MLOps Outcomes that guarantee uptime.
Transforming fragile AI experiments into reliable, cost-controlled enterprise software services.
99.99% Operational application uptime
Zero service disruption during cloud provider outages via instant, automated fallback to secondary model gateways.
50%+ Reduction in recurring API bills
Aggressive semantic caching and token optimization preventing duplicate API queries for common customer questions.
Proactive accuracy degradation alerts
Automated detection of concept drift and model decay before end-users experience degradation in production.
Reproducible model lineage
Every live production response linked back to the exact training dataset, prompt version, and model checkpoint.
Granular cost attribution per department
Accurate cost tracking by API key and department, preventing budget overruns with enforceable hard monthly caps.
Zero-downtime model upgrades
Canary deployment pipelines testing new model checkpoints on 5% of traffic before completing full rollouts.
Production-grade MLOps & observability tooling.
Deployed with MLflow, Kubeflow, Prometheus, Grafana, Evidently AI, DVC, and Docker for bulletproof reproducibility and monitoring.
Idea to Market–Ready Product
We turn your idea into a secure, compliant, and test-ready product in weeks, not months.
Inquiry & Scoping
We rapidly decompose requirements, pressure-test commercial viability, and align technical scope before capital is committed.
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Ready to build custom AI
that does real work?
Share your operational bottlenecks, dataset needs, or automation goals. An AI solutions architect will provide an architectural roadmap within 24 business hours.