Platform · Matillion
Integration capacity stops being a hiring problem.
Matillion is the cloud-native ELT and automation layer that feeds the platform: version-controlled pipelines, push-down transformation on Snowflake, and Maia, agentic AI that builds and maintains them with your team.
Different ways of working, not a longer feature list.
Transformation logic is built visually and kept in version control. It is auditable, reviewable, and repeatable: the same business rules apply on every run.
Maia changes the unit of work. You describe the data product you need; the agents do the repetitive build and keep it current as sources shift.
New sources land in days, not sprints. The question moves from “who has capacity” to “what do we want to analyse next”.
Observability and agentic maintenance mean schema drift and source changes surface early, with context, instead of as a missing number in a board pack.
Maia: agentic AI for the data supply chain.
Ingestion has always scaled by adding engineers. Maia changes the unit of work: you describe the data product you need, and the agents build and maintain the pipeline with you.
Maia by Matillion
Agentic AI for data teams
Maia autonomously creates, manages and evolves data products for people and for the downstream AI agents that will consume them, at a scale hand-coding cannot reach.
- Builds and edits pipelines from intent
- Keeps data products current as sources change
- Produces work a human can review and version
- Adds integration capacity without adding headcount
Maia Context Engine
Your metadata and intent, made usable
The Context Engine gives the agents the picture they need: your schemas, lineage, naming and business intent, so what they build matches how your organisation actually describes its data.
- Grounds the agents in your metadata
- Captures intent, not just structure
- Keeps generated pipelines consistent
- Improves as the platform is used
Maia Foundation
The governed execution layer
Foundation is where Maia's work actually runs: push-down ELT on Snowflake, version-controlled and observable, governed like any other pipeline already in the platform.
- Push-down transformation on Snowflake
- Version control and observability built in
- The same governance as hand-built pipelines
- Production-grade, not a sandbox
Not the connector count. The reasons agent-built ingestion pays back.
Transformation
Integration capacity stops being a hiring problem. The team designs data products and reviews the agents' work instead of hand-writing every extract and load.
Business process improvement
Pipelines the whole team can read and review: visual, version-controlled, auditable. The same business rules run every time, and a change is a pull request, not a mystery.
Optimisation
Push-down ELT means Snowflake does the compute. There is no separate transformation cluster to size, run and pay for alongside the warehouse.
Cost saving
Less engineer time per source connected. Maia does the repetitive build, and one connector library covers legacy extracts and modern APIs alike.
Future-proofing
The pipeline output is built to be consumed by AI agents, not just dashboards. The data supply chain is ready for the workloads coming next.
Matillion Gold Partner and Authorised Reseller.
We build Matillion the Snowflake-first way, and we can move you off hand-coded ETL onto it as part of the same engagement.
Gold partner status
Matillion Gold Partner and Authorised Reseller. Pipelines and licences run through one accountable partner, not a separate vendor relationship to manage.
Snowflake-first patterns
Push-down ELT by default, built the way Snowflake performs best, not retrofitted from a generic integration pattern.
Migration, not just new build
We move you off hand-coded ETL and legacy tools as part of the same engagement, not a separate project carrying its own risk.
South Africa's home team
Local expertise, local context, local accountability. The pipeline is part of the platform we build with you, not a side project.
