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See how your team gains the freedom to do more.
The migration you've been postponing isn't getting easier.
St. James's Place cut ETL migration effort by two-thirds with Maia, turning days of work into hours. The backlog clears. Engineering capacity goes back into the roadmap. Your AI initiatives, the ones waiting behind pipeline debt, can finally start.
$100K saved in consultancy spend. No pipeline rewrite.
Edmund Optics had a marketing pipeline stall for a year and burn $50K in failed consulting attempts. With Maia, they built a fully functional replacement in an afternoon. The work consultants couldn't deliver in months, Maia handled before end of day.
Informatica to Maia: 6 weeks becomes 1.
Organisations replacing Informatica with Maia cut ETL migration effort by two-thirds, turning days of work into hours. St. James's Place proved it in financial services, one of the most governed environments in the world. Your engineers can do the same.
Map. Convert. Validate. Deploy. Maia handles all four.
Maia maps your estate, converts the logic, validates output, and delivers production-ready pipelines before anything touches live. St. James's Place cut ETL migration effort by two-thirds. Engineers review what Maia built. They don't inherit a migration to finish.
Real results. Not projections.
St. James's Place cut ETL migration effort by two-thirds. Edmund Optics saved $100K in consultancy spend and tripled pipeline output with a two-person team. These aren't projections. They're live results from teams running Maia in production.
Trusted by the world’s most ambitious data teams
Your data team isn’t slow. Your process is broken.
Data engineers at legacy ETL accounts spend 60–70% of their week on maintenance, incident response, and migration backlogs, not on the data products the business is demanding. Maia changes the equation.
Without Maia
With Maia
Paying two licence fees for one job
One platform. One licence. Pipelines that run autonomously
Migration backlog growing every quarter
Legacy ETL migration completed automatically in a fraction of the timeline
Six-figure consulting cost
Data team spending time on strategic, net-new data products, not maintenance
AI roadmap stalled behind pipeline debt
AI initiatives have the necessary data infrastructure to move forward
Multi-year, six-figure SI engagement for migration
Eliminate expensive SI contracts for migration
Insufficient data to fulfill business requests
Business and analytics teams self-serve data, within governance
Schema drift breaking critical business dashboards
Schema drift detected, remediated, and logged automatically
The numbers your finance team will notice
Maia customers have reclaimed 22,000+ hours of manual data engineering effort - the equivalent of 11 years of FTE work, redirected back into the roadmap.
22,000+
hours of manual data engineering work saved
11 years
of FTE effort reclaimed since launch
$100K–250K
average customer
savings
savings
100x
data engineer throughput increase
Freedom to Modernize
Legacy ETL costs more to run and maintain
than to replace
Every quarter you keep an on-prem ETL running, the gap between what your team can build and what the business is asking for grows wider. The migration you've been postponing isn't getting easier. It's getting more expensive.
The problem
“Don’t touch it, it might break” is not a data strategy
The pipelines nobody moves aren't functional assets, they're business liabilities. Every renewal cycle, they cost more - in licence fees, in maintenance overhead, and in the AI projects your team doesn't have capacity to build. You've made the platform decision. What you haven't done is move.
How Maia solves it
Your migration analyzed, converted, and ready to deploy
Maia's agents analyze your existing on-prem pipelines, including Informatica PowerCenter, Alteryx, among other legacy ETL tools. Their business intent is reproduced into documented, validated, AI-native cloud pipelines. The conversion process is stable - the same input produces consistent, reviewable output. Your engineers review what Maia built before it ships. They don't inherit a manual migration task to finish.
What it costs you not to
One migration. One less licence. Full data team capacity back.
The impact shows up fast. Nature's Touch reduced pipeline build time from 2 days to 10 minutes. That kind of capacity can resolve your backlog and move directly into your roadmap. Teams have one fewer renewal, one fewer platform to administer and maintain. The AI initiatives that have been waiting on data debt can finally start.
See what your migration actually costs.
See what your migration actually costs.
Your legacy
pipelines, migrated autonomously in weeks
pipelines, migrated autonomously in weeks
No manual rewrite. No six-figure, multi-year consultancy contract. Full migration projects completed in weeks.
Freedom to Migrate
Legacy ETL costs more to run and maintain
than to replace
Legacy migration has always had a confidence problem. You can move the pipelines, but can you trust what comes out the other side? Maia is built to answer that question directly. The stable conversion process produces consistent, reviewable output, with lineage documented and quality gates built in. Your engineers review before it deploys. They don't inherit a migration to finish. They review one that's already been done for them.
Migrate
Describe your existing pipeline logic. Maia analyzes your Informatica PowerCenter or Alteryx ETL workloads and converts their business intent into documented, validated, AI-native cloud pipelines. Lineage is captured automatically. Quality gates are built in. Your engineers review before it ships, they don't rewrite after it breaks.
Stable conversion | Git-committed | Lineage auto-documented
Monitor
Schema drift doesn't wait for business hours. Maia monitors for changes continuously, auto-remediates where it can, and logs every change with a complete audit trail. Your engineers get notified with context, not woken up to diagnose something they didn't break.
Auto-remediation | Change log | Complete audit trail
When the pipeline fails in a regulated environment
Built for environments where getting it wrong isn't an option.
Migrating off Informatica or Alteryx in financial services, healthcare, or manufacturing isn't just a technology project. It's a compliance event. Every pipeline carries business logic that needs to be traceable, documented, and auditable before it goes anywhere near production. Maia is built for that.
Maia is running enterprise migrations in production environments, not sandboxes built to pass a proof of concept. Enterprise customers get a dedicated support team engaged from day one of the migration, not day one of the contract renewal.
See Maia in action
Governance without gridlock
Regulated migrations need an audit trail, not just an outcome. Maia enforces role-based access controls on every AI action, runs Git-based workflows your compliance team can actually sign off on, and logs every pipeline change from the first migration run to the last production deploy. Nothing moves without a record of how it got there.
Security your InfoSec team can approve
Maia's cloud infrastructure meets SOC 2, HIPAA, and GDPR compliance standards - all verifiable through stringent compliance documentation for your InfoSec team. Data is encrypted at rest and in transit. AI operations are governed and logged, processed under strict access controls, not stored where they shouldn't be. Scale that matches an enterprise migration.
Why Data Leaders Are Switching to AI Data Automation
Maia automates up to 90% of the pipeline work your engineers are currently doing by hand.
Freedom to BUILD
Your roadmap isn’t stuck. Your tools are.
When your data team don’t have to maintain complex legacy pipelines anymore, they start building the data products your AI initiatives actually need. Legacy data debt is replaced with agility and delivery velocity. Maia handles the execution, your team decides what to build next.
The backlog clears. The roadmap moves.
Maia handles the pipeline work. Your team focuses on what to build with it.
1
Pipeline creation on demand
Describe what you need. Maia builds the connection, writes the transformation logic, and delivers a ready-to-run pipeline.
2
Operational insights, on demand
When data requests come in from the business, they no longer sit in an engineering queue. Maia builds it. Your team governs it. The business uses it.
3
Natural language interface for self-serve data products
Predictable, validated pipeline conversion, with business logic preserved exactly. Nothing guessed, nothing silently dropped.
Featured Customer Story
From days to hours: How St. James’s Place cut ETL migration effort by two-thirds with Maia
Platform modernization stalls when legacy ETL migrations consume the engineering capacity needed to drive it forward. For St. James's Place (SJP), one of the UK's leading wealth management businesses, manually rewriting and validating pipelines was taking
days per job. In a proof of concept with Maia, the AI Data Automation platform, SJP cut that effort by roughly two-thirds - freeing engineers to focus on higher-value work and accelerating their path to a consolidated, modern data platform.
Read case study
“Maia reduced ETL migration effort by around two-thirds, taking work from days to hours… Platform consolidation will help us to reinvest in the team, to enable us to build out more on the SAP and AI roadmap of tomorrow”
KM
Kelly Maggs
Director for Data Architecture Platform and Engineering.
2/3 reduction
in legacy ETL migration effort per pipeline
Days → Hours
turnaround on individual pipeline conversions
Engineering capacity freed
and reinvested into SAP modernization and AI roadmap - without additional headcount
