DevOps · MLOps · AI

DevOps, MLOps & AI consulting for teams that ship fast.

We help startups and scaleups without dedicated platform or ML hires get to production faster — faster releases, lower cloud bills, and ML and AI features that actually run in production instead of sitting in notebooks.

Book a free 20-min call See what we do

Worked with teams at Sunbelt AI Gasco The Expo Group Carbee Shaw Communications Backgammon Galaxy

Working hands-on with Azure AWS GCP Kubernetes Terraform GitHub Actions

Three things we do, end to end.

Engagements typically run 4–12 weeks. We embed with your team, ship the work, and leave behind documented systems your engineers can run.

DevOps & Platform Engineering

Get your team off “deploys are scary” and onto a calm, repeatable release cadence.

Recent outcome: ~30% Azure spend reduction for a US healthcare client via Reserved Instances and right-sizing.

CI/CD pipelines on GitHub Actions or Azure DevOps
Infrastructure as Code with Terraform or Bicep
Kubernetes hardening and cost-aware scaling
Observability stack and on-call runbooks

DevOps services

MLOps & Data Platforms

Move ML from notebooks into reliable services your team can iterate on without 2 a.m. pages.

Recent outcome: shipped an end-to-end ML OCR pipeline on Azure Container Apps with one-click Terraform deploys.

Model serving and deployment pipelines
Training infrastructure on Kubernetes or SageMaker
Feature stores and drift monitoring
Data pipeline orchestration (Airflow, Prefect, Dagster)

MLOps services

AI Product Development

Ship the AI feature on your roadmap — with evals, guardrails, and a cost model you can defend.

Recent outcome: production WhatsApp AI agent with blue-green CI/CD and 20-second failover across 3 VMs.

RAG systems over your documents and data
LLM integration, prompt engineering, eval frameworks
AI agents and tool-using assistants
Fine-tuning and small-model deployment

AI services

How we work.

No statements of work that read like legal threats. No army of junior consultants. You work directly with the person doing the work.

Discovery call

A 20-minute conversation. We listen, look at your stack, and tell you whether we’re a fit. Free.

Scoped proposal

A short, fixed-scope plan with milestones, deliverables, and a price you can put in next month’s budget.

Build & ship

We work in your repos and your cloud. Weekly demos. No surprise invoices, no parallel pitches for change orders.

Handover

Runbooks, architecture docs, and a recorded walkthrough so your team owns it after we leave.

Capabilities & stack.

The tools we work in every week. We pick what fits your team, not what gets us a vendor referral fee.

Azure AWS GCP Kubernetes Terraform Bicep GitHub Actions Azure DevOps ArgoCD Helm Prometheus Grafana Loki OpenTelemetry Docker MLflow Airflow Prefect Kubeflow SageMaker Vertex AI Azure ML LangChain LlamaIndex OpenAI API Anthropic API Pinecone pgvector Weaviate PostgreSQL Redis

Recent engagements.

A few representative projects across DevOps, MLOps, and AI — named where we have client permission, anonymised elsewhere.

MLOps · Azure

End-to-end MLOps pipeline with Apple Store integration

Problem: An ML OCR feature was stuck in notebooks. No repeatable deploy path, no IaC, no failover, and the Apple Store integration was held together with manual scripts.
Approach: Built the full pipeline on Azure — Blob → Functions → ML OCR on Container Apps behind Front Door — all defined in Terraform. Wired the Apple Store integration into the same release flow.
Outcome: One-click deploys, infra as code, and a release path the in-house team runs without us. Client: Sunbelt AI (USA).

DevOps · Saudi Arabia

Blue-green CI/CD with 20s failover for a WhatsApp AI agent

Problem: A Flask + Gunicorn WhatsApp AI agent on three Ubuntu VMs had no automated deploys and no observability. Releases were manual and scary.
Approach: Jenkins blue-green pipeline across the three VMs, full Prometheus / Grafana / Loki stack on four VMs with 8 actionable alerts, runbooks for the on-call rotation.
Outcome: 100% failover under 20 seconds, deploys became routine, dashboards the on-call actually opens. Client: Gasco / Mub-Tech (Saudi Arabia).

DevOps · USA

Terraform-managed 16 environments across 4 apps

Problem: Sixteen environments stitched across four apps, mostly clicked into the Azure portal, source on TFS. Configuration drift was constant; every new environment took a week.
Approach: Migrated source from TFS to GitHub. Captured all 16 envs in reusable Terraform modules (App Services, SQL PaaS, Storage, Firewall). GitHub Actions blue-green deploys gated by SonarQube + Snyk with Teams notifications.
Outcome: New environments spin up in hours, not days. Security scans run on every PR. Client: The Expo Group (USA).

Recent writing.

Practical notes on DevOps, MLOps and AI — about one post a month, no marketing fluff.

MLOps

MLOps starter stack for a 5-person data team in 2026

Minimum viable stack — experiment tracking, model registry, pipelines, monitoring — without buying a platform.

11 May 2026 12 min read

DevOps

How much does it cost to migrate a small SaaS to Kubernetes?

Cost breakdown for a 10–30 person SaaS team — cloud, consulting, engineering time, and the hidden ones.

8 May 2026 14 min read

Hiring

Fractional DevOps consultant vs full-time hire

A clear framework for engineering leaders deciding between hire, retainer, or one-off engagement.

5 May 2026 10 min read

Want to talk through your stack?

A 20-minute call. We’ll tell you whether we’re the right fit — and if not, point you at someone who is.

Book a free 20-min call Or send us a message