AI doesn't fail at the model layer. It fails at the data layer.
Reliable AI needs reliable data. We build the pipelines, infrastructure, and model context layers that give your AI systems clean, structured, production-grade data — from source system to inference.
The infrastructure that makes AI systems actually work.
Every layer of the data stack — from raw source extraction to model-ready outputs — built to production standard and handed over to your team.
Data Pipelines
Batch and streaming pipelines that move data from source systems to AI-ready stores — reliably, observably, and on schedule. Designed for the volume and latency your use case actually requires.
MLOps Infrastructure
Model training, versioning, deployment, and monitoring infrastructure so your models run consistently in production — not just on a laptop in a notebook.
MCP Server Development
Model Context Protocol (MCP) servers that give AI agents structured, real-time, authorised access to your business data — the ERP, the CRM, the document store — without brittle custom integrations.
Cutting edgeVector Databases & RAG Architecture
Embeddings pipelines, vector stores, and retrieval-augmented generation infrastructure for AI systems that need to reason accurately over large internal knowledge bases.
Data Quality & Observability
Monitoring, alerting, and data quality gates so your AI systems do not silently degrade when upstream data changes. Failures surface immediately — not in a compliance audit six months later.
Cloud Data Infrastructure
Cloud data architecture on AWS, Azure, or GCP — scoped to your actual compliance and residency requirements, not a template lifted from another industry.
One protocol to replace every brittle custom integration.
Most AI integrations are brittle — a custom connector to each system, each one a point of failure. MCP (Model Context Protocol) is Anthropic's open standard for giving AI agents structured, authorised, real-time access to business data. One protocol. Every system. Stack10 builds production-ready MCP servers that connect your AI systems directly to your business data — live, structured, and authorised.
If your AI systems need to work with live data from your ERP, CRM, or document store — rather than stale exports — MCP is the right architecture.
Data Engineering is the right engagement if…
- A regulatory deadline is approaching and your data stack has no auditability, lineage, or observability built in.
- Your AI pilot works in demo but breaks in production — and the root cause is the pipeline, not the model.
- Your AI agents are making decisions on data that is 24 hours stale because there is no live data layer.
- Different runs produce different outputs and no one can explain why — the model is fine, the data feeding it is not.
- Your ERP, CRM, and document store do not talk to each other and every integration attempt has been a brittle one-off.
Every data system is built through the Stack Build Method — agreed scope, story-point delivery, full IP transfer.
Story-point pricing agreed before work starts. Senior engineers only — no juniors on delivery. Security architecture and data residency defined in week one. Full IP transfer on completion.
reduction in manual compliance processing — four interconnected AI systems built on a unified data layer, APRA-compliant and in production within ten weeks.
Start with your data problem, not a solution assumption.
Get in touch and we will assess your current data stack, identify the gaps that are blocking your AI initiative, and tell you exactly what a first engagement would include — and what it would cost.