Big data moves
Consolidate and migrate.
We bring your SaaS tools, databases and exports into one governed model on Snowflake, BigQuery or Databricks, organised as bronze, silver and gold.
Data and ML engineering services
Your data, model-ready in days.
We work with your team to move your data, engineer the features and prepare the training data for your model, on your warehouse.
Adaly engineers lead the engagement. The Adaptive Data Engineer does the heavy lifting: an agent swarm that works every source in parallel and checks its own work. That is how we deliver a first governed, model-ready dataset in days, not months.
What we deliver
From scattered sources to a model that trains.
Most ML projects stall before the model: data spread across tools, ids that do not match and no one sure which numbers to trust. We take that work off your team's plate.
Feature engineering
Features your model can use.
We resolve entities across systems, define the features with your team and materialise them as feature tables, with registration code for your feature store.
Training data
Data you can trust to train on.
Every dataset is reconciled against its sources, tested and documented with column-level lineage, and personal data is hashed before it reaches the features.
How an engagement runs
Five steps, with your team at every decision.
You set the goal and approve the plan. We run the build, and you review the calls that need a human.
- 01
Scope
We agree the model, its target and the sources it needs, and you grant access through a warehouse role you control.
- 02
Move
Source agents land, profile and map every source in parallel. Raw stays read-only; staging and core models build in your warehouse.
- 03
Engineer
We resolve customers and other entities across systems and build the features your model needs, reviewed with your team.
- 04
Validate
Totals reconcile across sources and every key is tested. Nothing is handed over on a failed check.
- 05
Hand off
Everything ships as pull requests into your git repo, with docs, lineage and feature-store code. The pipelines keep adapting after we leave.
Why it is fast
Agents do the plumbing. Engineers make the calls.
The speed comes from the platform, not from cutting corners. The same checks that make the data trustworthy are what let us move quickly.
Every source, in parallel.
Specialist agents profile, map and contract each source at the same time, while an orchestrator coordinates the build and cross-checks the results.
Self-correcting by design.
Drift is flagged instead of guessed, builds resume from checkpoints, and every change is re-verified before it is published.
Review exceptions, not everything.
Routine mappings are accepted with their evidence. Your experts spend their time only on the judgment calls.
Yours from day one
Your warehouse, your repo, your rules.
The work happens in your warehouse, where your data already lives, and everything we build stays with you. How Adaly governs access.
Your warehouse.
Models build natively in your Snowflake, BigQuery or Databricks account.
Your access controls.
The work runs under the role you grant, so it only reads and writes what that role allows.
Your git repo.
Every model, test and deployment ships as a reviewable pull request.
Your definitions.
Your team approves the plan, the mappings and the features before anything is published.
Start an engagement
Tell us what you want to train.
Share the model you have in mind and where your data lives. An Adaly engineer will reply to scope the engagement.
- Big data moves and warehouse consolidation.
- Feature engineering for a specific model.
- Training data that is reconciled and documented.
Want to run it yourself?
The Adaptive Data Engineer that powers our engagements is in beta.