Operate LLMs and agents
with production-software discipline.
Prompt versioning, evaluations, trace observability, and cost control — so a provider change or a bad prompt edit is a monitored event, not an incident you find out about from a customer.
The same discipline
as production software.
Prompts, retrieval, model routing, traces, token cost, evaluations, and tool-using agents — operated with the same rigor as any other production system, not left to run unmonitored until something breaks.
MLOps owns conventional model pipelines: training, registry, deployment, drift, retraining. This page owns everything specific to LLMs and agents sitting on top of that infrastructure. It matters most once a system crosses from one agent into a multi-agent architecture, where a trace has to be followed across several agents to find out which one actually caused a bad outcome, and for SaaS products embedding an assistant where every customer-visible failure is also a support ticket.
Eight layers
of operational control.
Where the line
actually sits.
Implementation,
or managed monitoring.
We build the observability, release-gate, and routing infrastructure, then hand it to your team.
We watch the dashboards alongside you on an ongoing basis.
Designed in from day one on a new build, not retrofitted after launch.
| Implementation | Managed monitoring | |
|---|---|---|
| Who watches the dashboards day to day | Your team, after handoff | Dwayo, with you looped in on incidents |
| Best fit | You have (or are hiring) in-house ownership | No dedicated owner yet, or you want it covered while that hire happens |
| Pricing shape | One fixed-milestone build | Build milestone plus an ongoing monthly fee |
| Exit | N/A — you already own it | Full handoff available at any point; nothing stays locked to us |
What a trace
actually shows.
When a customer reports a wrong answer, the trace is what turns "something went wrong" into a specific, fixable step.
Retrieval-quality monitoring runs on the same trace data in aggregate: a rolling score across recent queries flags when retrieval accuracy on a specific query type drifts below its baseline, which is usually the first visible sign that source documents changed or went stale.
When something
actually breaks.
| Trigger | Response |
|---|---|
| Error rate or latency spike past threshold | Automated alert, with the triggering traces linked directly for diagnosis |
| Retrieval or output-quality regression | Automatic rollback to the last known-good prompt/model config |
| Cost spike past budget threshold | Alert plus a rate-limit or routing fallback to cheaper models while it's investigated |
| Guardrail / PII violation flagged | Immediate escalation to a named owner, logged for the incident register regardless of severity |
Cost tracked,
not discovered at month-end.
Our LLM inference cost reduction case study describes the exact failure this service exists to prevent: per-document cost rising in lockstep with volume, invisible until the bill arrived. The fix — model routing, caching, and context trimming — cut cost by roughly 70% while tracking it on an ongoing dashboard instead of finding out monthly.
No sales pitch,
just how we work.
Not a blended bill you have to reverse-engineer.
Routing and fallback built in from the start.
Every agent step is reviewable after the fact.
You own the dashboards and configuration at completion.
Tools we've shipped
in real projects.
Before you
book a call.
How is LLMOps different from MLOps?
MLOps covers conventional model pipelines. LLMOps covers prompts, retrieval, model routing, generative evaluations, agent traces, and token economics. Most teams running both need both.
What happens when a model provider changes something?
Routing and fallback are designed in from the start, so a provider outage or deprecation triggers a configuration change and a monitored rollout, not an incident.
Do we need this if we already have monitoring?
General infrastructure monitoring tells you if a service is up. It doesn't tell you if an agent is still making good decisions or if retrieval quality has degraded.
Is this a one-time setup or an ongoing engagement?
Both are available — a fixed-scope build, or an ongoing managed-monitoring engagement.
What does a trace actually show when something goes wrong?
The full path a request took: intent classification, what was retrieved and its relevance score, every tool call and its arguments, and the final generation with a groundedness score. That's usually enough to pinpoint which step caused a bad answer, rather than guessing from the output alone.
What happens automatically when something breaks, versus what needs a person?
Error-rate spikes and quality regressions trigger automated rollback to the last known-good configuration. Guardrail or PII violations escalate immediately to a named owner regardless of severity — those never resolve silently.
Tell us what's
already running.
Book a 30-minute call — we'll tell you honestly what's missing from your current setup.