Source-to-target mapping with AI in mind
We document every data source, field mapping, transformation rule, and business rule before writing code — and flag which assets are candidates for ML feature engineering or RAG pipelines.
Data · AI-Ready Data Integration & Migration
Move your data once, and make it ready for AI.
We connect disparate systems and migrate data off legacy platforms onto a unified, governed foundation — so analytics and AI workloads run on data you can trust. Engagements run 4–8 weeks.
Book a free 30-minute data consult
Tell us about your systems and we’ll set up a short call.
Prefer email? Write to hello@mojotech.com.
No obligation; we’ll follow up within one business day.
We’ll only use your details to follow up on this request.
The problem
AI is only as good as the data under it.
Every analytics and AI project inherits the quality of the data underneath it. Integration and migration work is where that foundation is made or broken. We don’t just move data — we make it trustworthy, traceable, and ready for downstream AI.
Get migration right once, and everything you build next starts on solid ground.
When data arrives clean and governed, teams stop reconciling sources and start building on them.
Our approach
Build the foundation AI workloads require.
Governance & quality by design
Every pipeline is built with data quality checks, lineage tracking, and access controls from the start — not bolted on after.
Operational handoff built to last
Runbooks, monitoring hooks, and documentation so your team owns what we build, and AI workloads downstream have a reliable, observable data supply.
Tools we work with
- Snowflake
- Databricks
- BigQuery
- Redshift
- dbt
- Fivetran
- Apache Kafka
- Python
- and more
Process
Structured, with sign-off gates between phases.
Discovery, design, build, validate, cut over — with AI-readiness checks built into each phase, not added at the end.
Discovery & source profiling
Deliverables
- Source profile report
- Risk register
Architecture & mapping design
Deliverables
- Target schema
- Source-to-target mapping
- Architecture diagram
Pipeline build & testing
Deliverables
- Production pipelines
- Test suite
- Code in your repo
Validation & cutover
Deliverables
- Validation report
- Cutover runbook
- Rollback plan
Stabilization & handoff
Deliverables
- Runbooks
- Monitoring setup
- Knowledge transfer
Engagement options
Two shapes, scoped to your sources.
Before we start
What we need from you, and what shapes scope.
What we need from you
Access to source system owners
Schema documentation, if available
Business rules documentation
A QA reviewer for data sign-off
What affects scope
Number and complexity of source systems
Source data quality
Historical data volume
Whether your target platform is already chosen
Downtime tolerance for cutover
FAQ
Common questions.
What does it cost?
We scope and price after the consult, once we understand your sources, target platform, and data quality. Most engagements fit Standard or Complex.
What if our sources are undocumented or messy?
That’s common. Phase 1 profiles every source and flags risks before design, and scope reflects what we find.
Will there be downtime during cutover?
We plan cutover around your downtime tolerance, validate at full volume in staging first, and keep a rollback plan ready.
Which platforms do you work with?
Snowflake, BigQuery, Redshift, Databricks, and others, with Fivetran, Airbyte, dbt, Kafka, and more. If you haven’t chosen a target, we can help.
What happens after handoff?
Your team gets runbooks, monitoring, documentation, and knowledge transfer, with code versioned in your repo.
Free 30-minute consult
Find out what it takes to get your data AI-ready.
Bring your source systems and target platform. We’ll talk through scope, risks, and which engagement fits.
Book a free 30-minute consultNo obligation; we’ll follow up within one business day.