/services / llmops-ai-platform

LLMOps & AI Platform Engineering

Get AI from prototype to dependable production — deployment, observability, cost control, evaluation and the full model lifecycle (MLOps for LLMs).

engagement.spec
modelDedicated squad
cadence2-week sprints
stackCloud-native
time-to-value4–6 weeks
handoverFull IP & docs
// OVERVIEW

What this means for your business

The gap between an impressive AI demo and a dependable production system is wide — and it's where most initiatives stall. LLMOps closes it: reliable serving, continuous evaluation, cost control and safe releases. We stand up the platform and practices that let your teams ship AI changes confidently and run them at scale, with the observability and economics to keep them healthy.

// WHAT YOU GET
Production model serving & deployment
Evaluation & monitoring pipelines
Token-cost FinOps dashboards
CI/CD for prompts & models
Reusable AI platform & golden paths
SLAs, alerting & incident runbooks
01 // WHAT WE DELIVER

From promising pilot to production-grade AI

Model Deployment

Reliable serving for LLMs and ML models.

Eval & Monitoring

Quality, drift and safety tracked in production.

Cost & Token FinOps

Control spend without sacrificing performance.

CI/CD for Models

Versioning, testing and safe rollouts.

AI Platform Engineering

Reusable platform and golden paths for AI teams.

Reliability & SRE

SLAs, observability and incident readiness.

02 // HOW WE WORK

From pilot to dependable production

01

Assess

Review the pilot and the production gaps.

02

Platform

Stand up serving, evaluation and monitoring.

03

Operate

Automate releases and observability.

04

Optimise

Tune cost, latency and quality.

03 // OUTCOMES
prod
ready in weeks
↓40%
AI run cost
99.9%
availability
05 // FAQ

Frequently asked questions

Why do so many AI pilots never reach production?

Demos ignore the hard parts: reliability, evaluation, cost, security and change management. LLMOps provides the serving, monitoring and release discipline that turns a one-off prototype into a system you can depend on.

How do you control AI running costs?

We instrument token usage, apply caching, routing and right-sizing of models, and surface cost per feature — typically cutting AI run cost substantially without hurting quality.

How do you catch quality or drift problems?

Continuous evaluation and monitoring score outputs in production for quality, drift and safety, with alerts — so you find regressions before your users do.

Can this run in our own cloud?

Yes. We build on your cloud (AWS, Azure or GCP) and your tooling, leaving you with a platform your team owns and can extend.

Stuck in pilot purgatory?

Let's get your AI safely into production.

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