Models drift, LLM costs creep, agents hit edge cases, and quality slips when no one's watching. ML/LLM Ops is the operational layer that keeps your AI healthy in production - monitoring, observability, evaluation, retraining, and cost and performance tuning - for the models you've trained and the LLM and agent systems you've shipped alike.
An AI that worked at launch doesn't stay that way. Models drift as the world changes, LLM costs creep as usage grows, agents hit inputs no one tested, and quality degrades quietly - so you hear about it from a customer, not a dashboard. Most teams ship AI and then fly blind.
Model accuracy decays as the world moves on, with nothing watching for it until something breaks.
LLM token spend climbs with usage, and no one can see what's actually driving the bill.
No tracing or observability, so when something goes wrong you can't see why, or where.
Edge cases and regressions reach your users before anyone on your side catches them.
From the pipelines that ship models to the guardrails that keep generative AI honest - run as production engineering, end to end.
Automated training, validation and deployment pipelines with CI/CD for ML - reproducible builds, model lineage and version control baked in.
Centralized, governed feature stores that kill training-serving skew and make features reusable across teams and models.
Containerized serving with shadow deployments, canary releases and A/B testing - scaling to millions of predictions at low latency.
Continuous monitoring of performance, data drift and concept drift, with automated retraining triggers before accuracy slips.
Prompt versioning, eval harnesses, RAG pipeline monitoring and hallucination and toxicity guardrails - plus cost-per-token observability.
Audit-ready model cards, lineage, bias and fairness evaluation and explainability - built into the pipeline, not bolted on.
Opinionated internal MLOps platforms that give your data scientists secure, standardized, self-service infrastructure.
A structured two-week evaluation of your ML operations against best practice, with a prioritized 90-day roadmap out the other side.
Ops isn't a one-off - it's a loop that never stops running. Instrument, watch, evaluate, improve, repeat. Tap through each stage, or let it play.
We wire in tracing, metrics and cost tracking, so everything your models and agents do in production is visible - every run captured, classic ML and LLM alike.
A generic view of the layer we run under production AI - the shape holds whether it's a trained model or an LLM-and-agent system. Everything in production is instrumented once; an observability runtime traces, evaluates, watches for drift, tunes cost and retrains; and the signal comes back out as alerts, dashboards and the evidence your governance needs. Guardrails and tenant isolation wrap the whole thing.
Every run traced end to end - what the model or agent did, which tools it called, how it reached an answer. Debug it, improve it, prove it.
One instrumented layer - watch it in real time, and let it act on its own when drift or cost crosses the line.
That's the point of running it as one layer. The same observability that answers "is it still accurate, and what is it costing?" in real time also runs autonomously around the clock - catching drift, tuning spend and firing the retraining that keeps everything you've shipped alive. The run that keeps your AI honest.
The tools we run at each layer - pipelines and feature stores through serving, monitoring, LLMOps and governance. Vendor-agnostic on purpose: we fit the toolchain you already run, and show the strong alternatives alongside.
Grounded in our own MLOps & LLMOps practice, with common alternatives shown alongside. The governance row is frameworks and standards, not tools. We fit the toolchain you already run.
Two very different builds - one embedded and conversational, one large-scale and autonomous - both with the ops layer built in from the first line.
A multi-agent financial-planning companion where every model and agent run is traced end to end. Cost is tracked per conversation with prompt-cache savings, heavier and lighter models are routed by task to keep spend down, and evaluation harnesses check the system keeps picking the right tool for the job.
Read the case study →A large-scale multi-agent platform whose monitoring pipelines run autonomously around the clock. Dozens of agents are traced and evaluated continuously, drift and cost are watched with no one in the loop, and the same observability feeds the audit trail. That's the same layer we run for your AI.
We operate AI we engineered, so we know the system end to end - not just the metrics on a screen.
Classic ML monitoring and retraining, plus LLM observability, evals and cost control - not two tools and two teams.
We treat token spend and latency as something to engineer down - routing, caching, tiering - not just something to watch.
The same observability and evals that keep AI healthy also feed your model-risk evidence and audit trail.
ISO 27001 processes and a partner stack to match - Claude Partner Network, Snowflake and Databricks - for the data-and-AI foundation underneath.
Usually a CTO, VP of Engineering, Head of ML, Data or AI, a Head of Platform or SRE, or a technical founder.
The monitoring, tracing and evaluation that keep your AI healthy are the same evidence that proves it's controlled - for US frameworks like the NIST AI RMF, the EU AI Act, and your customers' reviews, wherever you operate. Run well, your ops layer feeds your governance rather than sitting apart from it.
More on this: AI Governance →Ops is the layer under everything in production - the models we train, the agents we build, the copilots in your product. Most teams come to us when they've shipped AI and need eyes on it; the ops layer then keeps it accurate, fast and accountable for the long run.
Yes. We instrument, monitor and tune AI built by anyone - and harden it where it needs it, even if we never touched the original build.
Both, in one layer. Classic ML monitoring and retraining, plus LLM observability, evaluation and cost control.
Caching, routing the right model to each task, tiering heavier and lighter models, and tracking spend per request - the same approach we run in production today.
A full trace of every run - what the model or agent did, which tools it called, how it reached an answer - so you can debug, improve and prove it.
The monitoring and evals are the same evidence that proves your AI is controlled - see AI Governance.
Ongoing by nature - it's the run. But we can also do a one-time instrumentation and audit to get you visibility fast, then decide what to run continuously.
Tool-flexible. We use established observability, monitoring and evaluation stacks, chosen to fit your environment rather than forced on it.
Book a 30-minute discovery call. We'll find where you're flying blind - and put the monitoring, evals and cost controls in place to fix it.