AI AI ops in production.

AIOps and MLOps engineering — the systems and practices that keep AI workloads running reliably in production. Monitoring, evaluation, retraining, incident response, and cost governance applied with the rigor production demands.

Overview

What it means in practice.

AI in production behaves like a distributed system, except the components are non-deterministic and degrade silently. Without proper operations, AI systems get progressively worse without anyone noticing — until a customer-impacting failure surfaces months of accumulated drift. We build the operational backbone that prevents that scenario.

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Ops
What we deliver

Capabilities & deliverables.

Every engagement gets shaped to fit, but these are the building blocks we rely on.

01

Production Monitoring

Latency, error rates, hallucination flags, content moderation alerts, and cost per request — observable in dashboards your on-call team trusts.

02

Continuous Evaluation

Automated test suites running against production traffic samples. Regressions surface before customers report them.

03

Retraining Pipelines

Scheduled and trigger-based retraining for ML models. Drift detection, dataset versioning, and rollback procedures all in place.

04

Incident Response

Runbooks for common AI failure modes — rate limits, hallucinations, prompt injections, data breaches. On-call playbooks tested in tabletop exercises.

05

Cost Governance

Per-feature, per-team, per-customer cost attribution. Budget alerts, model routing, and quota enforcement to keep spending predictable.

06

Compliance & Audit

Logging, data retention, access controls, and audit trails for regulated industries. AI operations that pass SOC 2, HIPAA, or PCI as required.

LangSmith Helicone Datadog Grafana MLflow Arize Weights & Biases PagerDuty
Why it works

The SD Technolabs approach.

Two decades of engineering practice, sharpened by the realities of production AI.

01

Treat AI like distributed systems

Retries, circuit breakers, dead-letter queues, idempotency. The boring infrastructure patterns that prevent AI outages.

02

Quality is observable

Hallucination rates, response quality scores, and user feedback aggregated into dashboards. Quality is debatable when measured.

03

Cost predictability

AI bills don't surprise you. Per-feature attribution, monthly budgets, and routing rules enforced at the gateway.

04

Incident-tested operations

Runbooks for the failure modes we've seen across dozens of production AI deployments. You inherit the playbook, not just the system.

Ready to start something good?

Let's discuss how this fits your business. We reply within one working day.

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