AI strategy & roadmap
We help you decide what to build, what to buy, what to delay. Frank conversations about where AI fits and where it's a distraction.
AI LAB · 06
From prototypes to production
MLOps and AI strategy — for teams ready to leave the prototype phase.
Most AI projects fail not because the model didn't work, but because the surrounding infrastructure didn't. We build the deployment pipelines, monitoring, governance, and cost controls that turn experiments into reliable production systems. And we help you decide what to build next, what to buy, and what to delay.
What we build
The boring infrastructure that wins.
Six things we deliver to AI teams — turning a pile of notebooks into a system you can run a business on.
Model deployment
Package, version, and deploy models with rollback, canary releases, and zero-downtime updates. Whether on Kubernetes, serverless, or edge.
Monitoring & drift
Dashboards for accuracy, latency, cost, and data drift. Alerts when reality stops looking like training data.
CI / CD for ML
Automated training pipelines, model registries, eval gates, and reproducible experiments — so deploys aren't a leap of faith.
Governance & safety
Model cards, bias audits, PII handling, audit logs, prompt-injection defence, and compliance documentation for regulated workloads.
Cost & latency tuning
Token budgets, model caching, quantisation, batching, and routing logic — cutting AI bills 40–70% without losing accuracy.
Use cases
Three engagements that paid for themselves.
Where MLOps quietly turned a chaotic AI effort into a competitive advantage.
MLOps overhaul
Customer had 14 models in production, all deployed manually, none monitored. We built a unified MLflow + Kubernetes pipeline with eval gates, drift monitoring, and automatic retraining triggers.
4×deploy frequency
−68%production incidents
AI cost optimisation
Monthly OpenAI bill ran to $42K. We added prompt caching, switched 70% of traffic to a smaller fine-tuned model, and routed only complex requests to GPT-4. Same quality, fraction of the cost.
−61%monthly bill
0user complaints
From sandbox to production
AI team had three working prototypes but no path to production. We helped scope a 6-month plan, picked one project, productionised it, and built the governance to scale.
12wksto production
3 →AI products live
The stack we use
Cloud-native, vendor-pragmatic.
We work across AWS, Azure, GCP, and self-hosted. We pick the smallest stack that fits your team's operating capacity.
Orchestration
- Kubernetes + KServe
- AWS SageMaker
- Azure ML
- Vertex AI Pipelines
Experimentation
- MLflow
- Weights & Biases
- Comet, Neptune
- Optuna, Ray Tune
Monitoring
- Evidently
- Arize, Fiddler
- Prometheus + Grafana
- LangSmith, Langfuse
Governance
- Model registries
- Eval harnesses
- PII redaction
- Audit + access logs
How we work
Six steps from chaos to control.
Most engagements start with a 3-week maturity assessment — a fixed-price discovery sprint that produces a concrete roadmap and a budgeted plan.
01
Maturity assessment
Where is your AI today? We map the stack, identify gaps, and define what 'production-ready' actually means for your team.
02
Reference architecture
Draft a target architecture that fits your cloud, your team size, and your governance requirements.
03
Pipeline build
Stand up training pipelines, model registries, deploy targets, and eval gates. Boring infrastructure done right.
04
Observability
Dashboards, alerts, and the on-call playbooks for the team operating the models.
05
Governance & safety
Model cards, bias audits, PII rules, prompt-injection defence, and compliance documentation.
06
Knowledge transfer
We don't lock in client teams. We document, train, and hand over — and stay available for the boring questions.
Frequently asked
MLOps and strategy questions.
What is MLOps?
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MLOps is the set of practices and tools that take a machine learning model from a notebook to reliable production. It covers training pipelines, model versioning, deployment, monitoring, governance, and retraining. Done well, MLOps is what separates 'we have AI in a demo' from 'we have AI running our business'.
Do I need MLOps if I'm only using LLM APIs?
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Yes, even more so. LLM-based products bring their own MLOps challenges: prompt versioning, eval harnesses, cost monitoring, latency budgets, drift in user behaviour, prompt injection defence. The discipline transfers, even if the tools are different.
What does AI governance involve?
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AI governance covers the policies, controls, and documentation that make AI use defensible — model cards, bias audits, access controls, PII handling, audit logs, evaluation criteria, and human-review queues for high-stakes outputs. For regulated industries we also produce compliance documentation aligned with frameworks like the EU AI Act and NIST AI RMF.
How long does an MLOps project take?
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A focused engagement (single team, single cloud) typically takes 6 to 12 weeks. Enterprise-wide MLOps platforms with multi-team governance and compliance run 4 to 9 months. We usually start with a 3-week maturity assessment that produces a concrete roadmap.
How much does MLOps consulting cost?
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MLOps engagements at Appsmediaz typically range from $20,000 for a focused implementation to $200,000+ for enterprise-wide platforms with governance. AI strategy advisory starts at $5,000 for a 2-week assessment. We provide fixed quotes after an initial discovery call.
Explore the rest of the AI Lab
Stuck between prototype and production?
Book a free 30-minute call with a senior AI architect. We'll diagnose the bottleneck, sketch a roadmap, and tell you honestly what a fix would cost.
Schedule a call