Your team can build an LLM feature. Keeping it reliable once real users, prompt edits, and model updates arrive is a different job. Our consultants assess how your LLM features are tested, released, and watched today, then give your engineers a clear LLMOps design and the guidance to put it in place.
We review how your LLM features move from idea to production today: who changes prompts, how releases get approved, what’s tested, and what happens when an answer goes wrong.
Most teams know they need evals but aren’t sure what to measure. We help you define quality for each feature, decide which cases belong in the test set, and set thresholds that block a bad release.
We design how prompts, model configs, retrieval indexes, and tool definitions get versioned, tested, and promoted to production. The design fits the CI, cloud, and approval rules you already have.
Langfuse, LangSmith, MLflow, Promptfoo, Arize, or the tools you already pay for? We compare options against your data residency, hosting, and team skills, then recommend a stack you won’t have to replace in a year.
Already building your own LLMOps setup? Our architects review the design and the pipeline code, then point out where it will fail at scale or under audit.
The goal is a team that runs LLMOps without us. We run workshops, pair with your engineers on the first evaluation suite, and stay available as advisors while you roll the practice out.
We meet the people who build, own, and approve your LLM features. You share access to repos and tooling, and we agree on which features matter most.
Our consultants map how changes move to production today and where they can fail without anyone noticing.
We present the report, the target architecture, and the roadmap to your team, then adjust priorities together until the plan fits your capacity.
Your engineers build the evaluation suite and release gate for one high-risk feature, with our consultants pairing and reviewing along the way.
As the practice spreads to other features, we review designs, answer questions, and check that the gates actually hold.
Your team owns the process, the test sets, and the runbooks. We step back to occasional reviews, or out entirely.
The people advising you also run MLOps for client models in production and maintain the LLM features we’ve shipped.
We have five Claude Certified Architects on our team. One of them signs off on your target LLMOps design before it reaches your team.
ISO 27001:2022 certified, ready for HIPAA, aligned with GDPR. Repo access and production samples shared during the assessment stay under those controls.
We don’t resell tools or take referral fees. The stack we recommend is the one that fits your team.
Most clients’ teams build the setup themselves with our guidance. If you’d rather hand off the build, our engineers can take it on.
Client satisfaction at 98%. Your consulting work runs under the same delivery process those clients rated.
MLOps grew up around models you train yourself: data versioning, training pipelines, and retraining on drift. LLMOps keeps that discipline but deals with different moving parts. Most teams don’t train the model at all. They change prompts, retrieval settings, and tools, while the provider changes the model underneath them. Quality is also harder to measure, since there’s rarely one correct answer. That’s why evaluation and prompt versioning sit at the center of LLMOps. If you run classic ML models too, both practices can share one CI and one monitoring stack.
Yes, and often more than teams hosting their own models. With an API you don’t control when the model changes. You still own the prompts, retrieval, and tools, and those change more often than the model does. For an API-based product, a lean setup is usually enough: a test set per feature, versioned prompts, a CI gate, and alerts on live quality. The assessment shows how much of that you need.
This engagement is advisory: we assess, design, review, and train, and your engineers build. That way the knowledge stays in your team. If you’d rather hand the build over, our engineers can take it on through LLM development, working from the same design.
Very little. A list of your LLM features in production, access to the repos and tools involved, and an hour with each feature owner. If you have any test cases, incident notes, or provider dashboards, bring them too, but they aren’t required. To get a scope for your setup, contact us with that list.
It’s a good time. Setting up a test set and a release process for one feature is far easier than adding them after five features are live and nobody remembers why a prompt looks the way it does. Many teams use the assessment to define what the pilot needs before it goes to production. If that’s where you are, the wider path is covered by our AI prototype to production work.
We’ll help your team set up the tests, gates, and playbooks that make it one.
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