From notebook to production. AI that actually ships.
AI systems integration, data engineering, ML pipelines, and LLM deployment for organisations that need measurable AI outcomes, not stalled proofs of concept.
Typical engagement shape: Most engagements begin with a 2–3 week readiness and architecture phase, then move into focused implementation over 6–14 weeks depending on data quality and integration depth.
Book a ConsultationWhen this service is a strong fit
Strongest when AI must plug into real systems, real teams, and real operational workflows rather than remain isolated in a pilot environment.
Good fit
Probably not the right fit
What changes early in the engagement
A good service page should make the first month feel concrete, not vague.
Clarify the business workflow, decision point, or operational constraint AI is meant to improve
Assess data readiness, integration points, security constraints, and observability needs
Define the production architecture and the shortest credible path to measurable value
Typical deliverables
The output should help a buyer understand what they are actually paying for.
Data Platform Engineering
Design and build production-grade data platforms — data lakes, warehouses, streaming pipelines, and governance frameworks that scale with your business.
Use Cases
- Data lake and warehouse architecture
- Real-time streaming pipelines
- Data quality and governance
- Master data management
Tech Stack
Business Outcomes
- 10× faster query performance
- Single source of truth
- Automated data quality
ML Pipeline Architecture
Design and build MLOps pipelines from training to production deployment. Reproducible experiments, automated training, and production-grade model serving.
Use Cases
- Models stuck in notebooks
- No reproducibility across experiments
- Deployment bottlenecks and manual processes
- Model monitoring and drift detection
Tech Stack
Business Outcomes
- 5× model deployment velocity
- Reproducible experiments
- Production-grade ML
AI System Design & Deployment
End-to-end AI system design — from problem framing to production monitoring. We ensure your AI investments deliver real business value, not just experiments.
Use Cases
- AI ambition without execution capability
- Pilot-to-production gap
- Unclear AI ROI
- Production monitoring and alerting
Tech Stack
Business Outcomes
- Measurable AI ROI
- Production-grade systems
- Continuous improvement
Questions we hear before clients engage
Useful context for teams evaluating whether this service is the right fit.
Can Voodoo AI integrate AI into existing systems?
Yes. We specialise in connecting AI capabilities to real operational systems such as CRMs, internal tools, customer workflows, and data platforms so the result is usable in production.
What is the difference between your AI consulting and a typical AI agency?
Our focus is production delivery. We design the architecture, data flow, integration patterns, observability, and operating model needed to make AI reliable beyond the pilot stage.
Ready to get started?
Let's discuss how our data engineering & ai systems services can deliver results for your business.
Book a Consultation