Stand up a fully-historized, audit-ready warehouse in days, not the quarters it takes to hand-build one. You describe your tables, keys, and what each column means; FluxDI writes, tunes, and runs every pipeline, so your team never hand-codes history logic or chases a broken timeline again. Define once. Run on any supported engine.
Every AI initiative is only as good as the data beneath it. FluxDI builds the trustworthy, historized foundation your models depend on.
Each option leaves the hardest parts (accurate history tracking, consistent change-tracking policies, and moving data between engines) as recurring engineering work that drifts over time and lands on your team's nights and weekends.
Fast to start. Impossible to keep consistent across hundreds of tables. History tracking is bug-prone, and the bug usually surfaces in a board report, not in code review.
Expensive, visual-editor heavy. And the hard parts (full history tracking, change reconciliation, cross-engine data movement) still get hand-coded inside the boxes.
Solves the transformation layer beautifully. Doesn't solve source-to-warehouse history, cross-engine data movement, change-tracking enforcement, or schema-as-config.
A few simple facts about each table (its purpose, its keys, what its columns mean) are enough for FluxDI to generate the entire pipeline. Change the facts, and the pipeline updates itself.
Describe your tables, columns, keys, and what they mean, once, in one place.
A guided path from raw source data to business-ready marts, with rules enforced at every stage.
The right history-tracking pipeline picked automatically for every table you load.
Define, preview, run, and monitor. All in one modern web interface.
A clear, repeatable path every table follows, with the right history-tracking strategy chosen automatically along the way.
For tables where "what was true at any point in time" matters: pricing, contracts, employee records, anything you need to report on as-of a past date.
For tables where you only need "what we knew, and when we knew it": operational data where the source system already owns the timeline.
FluxDI streams data directly between supported engines, with automatic type translation so columns land in their correct shape on the target. Built for production-scale moves (migrations, replications, and ongoing syncs) without standing up an extra processing tier.
A modern web interface where data engineers define the work, data leaders monitor it in real time, and everyone shares the same view of what's running, what's passing, and what needs attention.
One dashboard for KPIs, table health, and the runs currently in flight.
See exactly what FluxDI will do, every step, every table, before anything executes.
Define columns, keys, and meaning in a spreadsheet-style grid. The platform handles the rest.
Watch each table load in real time. See errors the moment they happen.
Color-coded Production, UAT, and Dev, so nobody ever runs the wrong thing in the wrong place.
Upload a spreadsheet of your existing tables and FluxDI imports them in seconds.
Click any column and see exactly where every value comes from and where it flows, all the way from source to warehouse, across every transformation.
Every change is versioned automatically, with full history and audit trails, so you can see your data exactly as it was at any point in time.
Manage your servers, databases, and connections across PostgreSQL, Teradata, and DuckDB, all from one place.
Every change to every definition is logged, so you always know who changed what, and when.
FluxDI is currently in early access, being validated alongside a select group of partner organizations before general availability.
Years of architecting enterprise data warehouses across government, telecom, banking, and credit-bureau organizations taught us one thing: every engagement repeats the same loop. A data model is drawn. ETL is hand-built. KPIs drift between teams. History tracking gets bolted on as an afterthought. Sooner or later, someone is rebuilding the same thing in a slightly different way.
We got tired of solving that problem one project at a time. So we built the framework we kept wishing we had: metadata-first, production-grade by default, and cross-vendor by design.
What started as an internal accelerator, used in production to compress delivery timelines, optimize processing windows, and replace legacy hand-coded generators, is now a platform you can adopt.
One lesson stands out now that AI is on every roadmap: AI initiatives succeed or stall on the data beneath them, not the models on top. We have lived that firsthand. The teams that deliver AI are the ones that get that foundation right first, which is exactly what FluxDI is built to make possible.
If your team has rebuilt the same pipeline more than once, questions the numbers in every dashboard, or is staring down a warehouse migration without a clean path forward, we would like to talk.
Tell us about your use case. We'll send back a short demo that takes one of your tables and shows you exactly what FluxDI would build for it, end to end.
We respond within two business days. The demo runs against your schema, in a sandbox we provision, with no production credentials required.