Services · Artificial intelligence · MLOps and AI platforms

Keep your models working after launch day.

We set up MLOps and AI platforms so models and LLM features can be trained, released, monitored and rolled back like any other software. That covers pipelines, a model registry, drift monitoring, evaluation and the LLMOps tools that track prompts, cost and latency.
The platform runs on Azure, AWS or Kubernetes in your own accounts, and your team can operate it after handover.
Get in touchAll AI services
Built for
CTOs, data leads and platform teams with models or LLM features in production, or about to be.
Typical timeline
A core platform with pipelines, registry and monitoring typically takes eight to twelve weeks, then grows with your use cases.
What you own
You own the infrastructure code, pipelines, dashboards and runbooks, all in your own cloud and repositories.
Rated
4.7 / 5 from 23 Google reviews

200+ companies across the US and the UAEFrom a first store to a listed developer

Modon
Majid Al Futtaim
HP
Belkin
Dulsco Group
OneClickDrive
Hanayen
HMS
Clocksprings
Ghezlan

What you get

What the platform covers

The goal is that releasing, checking and fixing a model is routine work, not a special project.
Explain your project

  • Training pipelines

    Data preparation, training and testing run as versioned pipelines that anyone on the team can rerun. Each run records the data, code and settings used.

    Any model can be rebuilt and explained months later.

  • Model registry

    Models are stored in a registry such as MLflow with their version, test results and approval status. Only approved versions can be deployed.

    Everyone knows which model is live and who approved it.

  • Deployment

    Models and LLM services are packaged with Docker and deployed to Azure, AWS or Kubernetes with staged rollouts. A bad release can be rolled back in minutes.

    Releases become routine and a mistake is quick to undo.

  • Drift monitoring

    We track changes in input data and prediction quality and alert when they move outside agreed limits. Alerts link to the data that changed.

    You learn a model is slipping before the business feels it.

  • Evaluation

    Every new model or prompt version runs against a fixed evaluation set before release. Results are compared with the version in production.

    Each release is backed by a test result rather than an opinion.

  • Prompt versioning

    For LLM features, prompts, model settings and tool definitions are versioned like code and released through the same review process. Old versions can be restored.

    A prompt change cannot quietly break a feature in production.

  • Cost and latency tracking

    We record tokens, cost and response time per request, feature and model, and show trends on a dashboard. Budgets trigger alerts.

    You see what each AI feature costs and how fast it responds.

  • Access and audit

    Access to data, models and endpoints follows your roles, and every deployment and change is logged. Logs support internal audit and your compliance reviews.

    You can show who changed what and when.

Who this is for

Signs you need MLOps

The need usually appears after the first model or LLM feature goes live.
01

Models live on one person’s laptop

Pipelines and a registry move training and releases into shared, repeatable infrastructure so the work does not depend on one person.

Your team keeps full access.

02

LLM costs and quality are hard to see

LLMOps tracking shows cost, latency and evaluation results per feature and per prompt version.

Works across OpenAI, Claude, Gemini and open models.

03

Nobody notices when a model gets worse

Drift and quality monitoring alerts your team and starts a retraining run when limits are crossed.

Limits are set with the model owners.

How we build the platform

  1. 01AssessWe review your current models, data, cloud setup and release process and agree what to fix first.
  2. 02FoundationWe set up infrastructure as code, pipelines, a registry and access controls in your cloud.
  3. 03MigrateWe move existing models and LLM features onto the platform one at a time.
  4. 04Hand overWe write runbooks, train your team and stay on for support while they take over.

Technology

What we build this with.

Models are chosen per use case for quality, cost and where your data is allowed to go. The systems around them run on the stack below.

01Web

  • React
  • Next.js
  • TypeScript
  • Node.js
  • Laravel
  • WordPress

02Data and cloud

  • PostgreSQL
  • MySQL
  • MongoDB
  • AWS
  • Azure
  • Docker

Questions about MLOps

Same idea, different things to track.

MLOps manages trained models: data, training runs, versions and drift. LLMOps applies the same discipline to language model features, tracking prompts, evaluation sets, model choices, cost and latency.

Services that usually go with this one.

Machine learning developmentPredictions from your own data, explained in plain terms.Custom AI and LLM developmentAI features built into your product, not bolted on.AI security, risk and governanceUse AI with controls you can explain to an auditor.

Tell us what the business needs. We will tell you what to build first.

Schedule a free consultation and get a written recommendation, with costs, before you commit to anything.

Get in touch

PROVEN RESULTS

4.7 / 5from 23 Google reviews