Your LLM Works in the Lab. Can It Survive Production?
LLMOps consulting builds the operational infrastructure that keeps LLM-based systems running reliably in production. Building a working AI prototype is the easy part. Running it at scale with consistent quality, cost control, monitoring, and automated updates is where most teams get stuck. LLMOps is the discipline that solves this.
We help teams establish LLMOps pipelines that cover model monitoring, automated evaluation, prompt versioning, cost tracking, and CI/CD specifically designed for LLM-based systems. If your AI product works in development but breaks, drifts, or costs too much in production, this service is for you.
Common LLMOps Problems We Solve
- Quality drift - Model outputs degrade over time as providers update their models or as your data changes. We set up automated evaluation pipelines that catch regressions before users notice.
- Cost overruns - LLM API costs grow unpredictably as usage increases. We implement cost tracking, budget alerts, model routing, and caching strategies that keep costs under control.
- Prompt management chaos - Teams lose track of which prompts are in production, what changed, and why. We set up prompt versioning with rollback capabilities and A/B testing.
- No observability - You cannot fix what you cannot see. We implement logging, tracing, and dashboards that show latency, error rates, token usage, and output quality at every step of your LLM pipeline.
What Our LLMOps Consulting Covers
- Pipeline architecture - We design your LLMOps stack: evaluation harnesses, prompt registries, model routers, caching layers, and deployment pipelines.
- Automated evaluation - We build evaluation datasets and automated testing that runs on every prompt change, model update, or deployment.
- Cost optimization - We implement model routing (use cheaper models for simple tasks, expensive ones for hard tasks), response caching, and token optimization.
- Monitoring and alerting - We set up production dashboards with alerts for latency spikes, error rate increases, cost anomalies, and quality drops.
- CI/CD for LLM systems - We build deployment pipelines that test prompt changes, validate model outputs, and roll back automatically on quality regressions.
Get Your LLMOps Right
Book a free LLMOps assessment. We will review your current production AI setup, identify operational gaps, and recommend the highest-impact improvements.