We keep your LLM systems working after launch and keep improving their answers and cost for as long as they run.
Before we build anything, we map every LLM system your business units run and price what it’ll take to operate them under one process.
We build the test that decides every release. Each new version answers the same real user questions as the current one, and you compare the two side by side.
After launch, we keep raising answer quality on the questions your users actually ask.
Every change to a model, prompt, retrieval setting, or guardrail ships the same way: as a release you can trace and reverse.
When a provider retires or replaces a model, we move every product that depends on it to a tested replacement through your change process.
One operating layer serves every business unit. Each new LLM use case starts on the standard your platform team has already approved.
Finance gets a cost per request it can allocate. Engineering gets the levers to bring that cost down.
When an LLM system fails, the alert lands in the same on-call process as the rest of your production stack, with a runbook ready for each failure type.
An AI agent plans steps, calls tools, and writes to your systems, which means a model update or a prompt edit can change its actions as well as its words. If your agents aren’t live yet, enterprise AI agent deployment comes first. Once they run, we put every agent under the same release, testing, and cost controls.
We inventory each model, prompt, owner, and monthly spend, and leave it with you as a working register.
You get a phased plan: which systems come first, what each phase costs, and which risks it removes.
We collect real user questions, agree on pass criteria with your domain experts, and wire the scoring into your CI pipeline.
Prompts, models, and configuration move into version control with staged rollout and one-step rollback. Your first system goes live under the new process.
Runbooks, cost reports, and the model register pass to your platform team, and we pair with them on real incidents.
On the systems you pick, we turn production failures into test cases each month. Every release then has to beat the last one on quality or cost.
Production LLM work is a regular part of what we do. For one generative AI platform, for example, we fine-tuned Llama 3 and Mistral on proprietary data and built the framework that kept those models accurate after launch.
Relevant is ISO 27001:2022 certified, HIPAA-ready, and GDPR covered. Release IDs and answer traces give your auditors a record they can follow.
96% employee retention. The engineers who build your test sets and release process are the same people who stay for handover and ongoing tuning.
We’ve delivered Open Banking, HL7 FHIR, and PSD2 projects, so data residency and audit questions get answers from work that’s already shipped.
Hosted or open-weight, on your cloud and with your tools. We build LLMOps around the providers you already use and don’t lock you into one vendor.
200+ companies have built and scaled products with us. The projects below are the part you can check.
We set up the tests, releases, and cost controls, then keep raising answer quality while your systems serve users.
LLMOps services cover what a production LLM system needs after launch:
Where we start depends on what’s already running. If several teams have LLM features live, the work usually opens with an inventory. With one critical system, it opens with that system’s test set.
MLOps manages models your team trains: data pipelines, training runs, and model registries. LLMOps manages systems built mostly on models someone else trains and updates. The focus shifts to prompts, retrieval, provider versions, evaluation of open-ended answers, and cost per request. Many enterprises need both. If your LLM features call hosted models through an API, LLMOps is the part that applies to them.
For agents, we test whole tasks instead of single answers. Tools and permissions are versioned together with prompts, each step traces back to its release, and you track cost per completed task. Getting agents into production is a separate job, covered by enterprise AI agent deployment, while AI agent monitoring and orchestration watches and coordinates them live. LLMOps decides which version of each agent is allowed to run and makes the next version better.
Yes, it does. A RAG system answers from the data it retrieves. Moving source data during a data warehouse migration, changing a schema, or switching document stores can shift answers even when the model and prompt stay the same. The test set that gates your releases confirms that retrieval still finds the right sources after the move. Run that check before the old data source shuts down, while you can still compare the two.
Yes, we build on your current providers, hosted or open-weight, and connect evaluation and release gates to the CI, observability, and ticketing tools your teams already run. New tooling comes in only where there’s a gap, and the assessment names each gap before you buy anything.
Scope sets the price:
We go through each of these on the first call and itemize them in the proposal. Want a rough estimate of your own scope? Count the LLM features in production and the teams that own them. That number drives more of the effort than any single system. If you’d rather talk it through, contact us.
AI
AI
AI
