MLOps & AI Infrastructure
MLOps is the practice of running AI systems in production reliably: deployment, versioning, monitoring, evaluation, retraining and cost control. It is what separates a model that worked once from a service your business can depend on, and it is where many AI projects stall.
The problem
When deployment is a person
One engineer knows the sequence
Releasing a change involves steps in a particular order, a config value that has to be updated by hand, and a check that only they know to perform. None of it is written down. The release cadence is effectively that person's calendar, and nobody wants to think about what happens when they take a fortnight off.
The provider changed the model and your output changed with it
Nothing in your codebase moved. Behaviour did — slightly longer responses, a different tone, a format that now fails validation once in fifty. You found out from a user, and reconstructing when it started took longer than fixing it.
Prompts live in six places and none of them is version control
A string in application code, a value in an environment variable, a paragraph somebody edited directly in a vendor console last quarter. No history, no review, no way to roll back, and no way to answer what the system was actually doing on the day a customer complained.
What a real fix takes
Prompts are production code. Until they are versioned, reviewed and rolled back the way code is, you do not have a deployment process — you have a person, and your risk profile is their availability. The second thing nobody wants to hear is that uptime monitoring tells you almost nothing here. Infrastructure failures are the easy case; they are loud. The expensive failure is a system that stays up and gets quietly worse, and catching that requires continuous evaluation against a held-out set and a chart of the business metric, neither of which any standard observability stack gives you by default.
Outcomes
What changes
Deployment stops being an event
Versioned, tested, repeatable releases instead of a manual process only one person understands.
You find out when quality degrades
Drift detection, evaluation on live traffic, and alerting on the business metric — not just on uptime. Models fail quietly; infrastructure failures are the easy case.
Cost becomes visible and controllable
Token and compute spend attributed per feature, with routing and caching to manage it.
Rollback when something breaks
Version pinning and traffic splitting, so a bad release is a five-minute problem rather than an afternoon.
What we build
What we build
Deployment pipelines
with CI/CD for models and prompts alike — prompts are production code and deserve the same versioning, review and rollback.
Serving infrastructure
sized to real load, batch or real-time, with autoscaling and sane failure behaviour.
Monitoring and observability
Request tracing, latency, error rates, token spend, output quality sampling, drift detection on inputs and predictions.
Evaluation in production
Continuous scoring against held-out sets and live sampling, so quality is a number on a dashboard rather than an impression.
Experimentation infrastructure
A/B and shadow deployment, so changes are validated on real traffic before full rollout. Essential given how often model providers update models beneath you.
Cost management
Model routing by task, caching, batching, and spend attribution per feature and team.
Retraining pipelines
with data validation, automated evaluation, and a promotion gate that a human approves.
Runbooks and on-call handover
The operational documentation that makes the system supportable by people who did not build it — which is the actual test of whether a handover happened.
What you receive
Three operational assets
The release pipeline with a working rollback path
Versioned, tested, repeatable deployment for models and prompts alike, with the rollback demonstrated rather than assumed. A rollback path that has never been exercised is a hypothesis. This turns a bad release from an incident into a five-minute decision.
The runbook
Written for someone who did not build the system and is on call at three in the morning: what to do when latency triples, when a provider deprecates a model version, when spend spikes, when evaluation scores drop but nothing has errored. Each with the symptom, the check, and the action. It is the artifact that makes the system genuinely transferable to your team rather than nominally handed over.
The cost attribution report
Spend broken out by feature, team and model, with budgets and alerting. Most organisations engage us partly because AI cost has become an unexplainable line, and you cannot control spend you cannot see — attribution comes before routing and caching, always, because it is what tells you where the routing would help.
How it works
How it works
- Weeks 1–2
Assessment
Current state of your AI systems, deployment process, monitoring gaps and cost visibility. Most organisations discover here that they cannot answer basic questions about their production models.
- Weeks 2–4
Foundation
Deployment pipeline, versioning, environments, secrets and access.
- Weeks 4–6
Observability
Tracing, metrics, quality sampling, drift detection, alerting with meaningful thresholds rather than noise.
- Weeks 6–8
Evaluation and experimentation
Continuous evaluation harness, shadow and A/B infrastructure, promotion gates.
- Weeks 8–10
Handover
Runbooks, documentation, and training your team to operate it independently.
Ongoing
Optional managed operation where you would rather not staff it internally.
The assessment is the phase that lands hardest. Being unable to answer which prompt version is live, what last month's inference actually cost by feature, or how you would know if quality had dropped is the normal starting position rather than an unusual one — but it is still an uncomfortable two weeks, and it is worth knowing that going in.
Technology
What we work with
Technologies we work with, used where they help. The bias is towards extending what you already run rather than standing up a parallel stack, and some of the LLM-specific tooling here is commercial — where that is the case we name the cost before recommending it.
Orchestration and serving: Kubernetes where the scale justifies it, managed services where it does not. vLLM and TGI for self-hosted inference; provider APIs where hosted is appropriate.
Observability: OpenTelemetry-based tracing, Prometheus and Grafana, plus LLM-specific tooling such as LangSmith or Braintrust for prompt and output tracking.
Model and prompt management: registries, version pinning, and prompt versioning under source control.
Pipelines: your existing CI/CD extended rather than replaced, plus Airflow or Dagster for scheduled retraining.
Cloud: AWS, GCP or Azure, aligned to what you already run.
Where this applies
Where this applies
Necessary once you have anything in production, and urgent once you have more than one thing. Most pressing in fintech and healthcare, where being unable to reconstruct what a system did on a given date is a regulatory problem rather than an engineering inconvenience.
Premature if you have a single prototype and no users. Ship it first; the operational layer should follow real usage rather than anticipate it, and building it early means building it against guesses about load and failure modes that reality will contradict.
Why us
One team, not three vendors
The usual way
- A strategy consultant, a development shop and a marketing agency, each scoped separately.
- Months spent getting three vendors to agree, and blame to allocate when something breaks.
- The model ships and adoption becomes somebody else's problem.
With Nexiour
- One team across strategy, engineering and growth. You skip the coordination tax entirely.
- We own the whole outcome — from the first audit to the system that is running.
- Delivery across the UK, UAE, Germany and India, with overlapping hours in four markets.
What it costs
How we scope and price
Fixed scope, quoted after an assessment. Three things drive it.
How many systems are in scope
Each production system carries its own deployment path, evaluation set and failure modes.
Your existing infrastructure maturity
Where a solid CI/CD and observability practice already exists, this work extends it. Where it does not, some of the foundation has to be built before the AI-specific layer sits on it.
Whether self-hosted inference is involved
GPU serving adds capacity planning, optimisation and a continuous compute cost that a provider API does not.
Where you would rather not build an internal capability, ongoing managed operation is available as a retainer, quoted separately from the build.
FAQ
Frequently asked questions
Overlapping but not identical. Traditional ML centres on training pipelines, feature stores and retraining. LLM systems centre on prompt versioning, evaluation, cost control and handling provider-side model updates. Most organisations now need both.
Continuous evaluation against a held-out set, sampling of live output, drift detection on inputs, and tracking the business metric the system exists to move. Absent these, you find out from a complaint.
When you have one prototype, no users, and no production dependency on it. Building the operational layer before real traffic means designing for imagined load and imagined failures, and it delays the thing that would have taught you what the real ones are. Ship, then instrument. The exception is regulated work where an audit trail is required from day one.
Yes, and it is one of the most common reasons people engage. Attribution first — you cannot control spend you cannot see — then routing, caching and batching.
Behaviour can shift. Version pinning where supported, an evaluation suite that runs as a regression test, and shadow deployment before switching. Routine, provided the infrastructure exists.
Yes, as an ongoing retainer, or we build it and hand it to your team with runbooks and training. Both are common.
Usually not. Managed services handle most workloads at lower operational cost. We recommend Kubernetes when scale or existing standards justify it, not by default.
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