One version of the numbers
A modelled warehouse with agreed definitions, so "revenue" means the same thing in finance and in marketing and nobody has to reconcile two spreadsheets first.
Service
Data & AI: Pipelines, Warehousing, Governance & MLOps
Pipelines that make your data usable.
Most companies do not have a data problem so much as a plumbing problem. The numbers exist, but they are in six systems that disagree, arrive late, and require somebody to assemble them by hand each month, which is why the reporting is always slightly out of date and quietly distrusted.
We build the plumbing. Ingestion, transformation and a warehouse that acts as one source of truth, with the governance to make it trustworthy and the MLOps to get models into production and keep them honest once they are there.
Data Pipelines
Getting data out of the systems it is trapped in and into somewhere it can be used. Batch or streaming, depending on how fresh the answer actually needs to be.
Transformation is where the meaning gets applied: deduplication, normalisation and the business rules that turn six systems’ worth of records into something comparable.
Includes- ETL / ELT
- Ingestion
- Streaming
- Transformation
Warehousing
A single place the numbers come from, modelled properly. Snowflake, Databricks or BigQuery, chosen on your volumes, your skills and what you are already paying for.
The modelling is the work. A warehouse with no agreed definitions is just a bigger pile of the same disagreements, moved somewhere more expensive.
Includes- Snowflake
- Databricks
- BigQuery
- Data modeling
Governance
What makes data trustworthy enough to act on. A catalogue so people can find what exists, quality checks so a broken feed is flagged where it enters, and lineage so any figure can be traced to its source.
Access control belongs here too: teams able to self-serve what they need without everyone holding a key to everything.
Includes- Data catalog
- Quality
- Lineage
- Access control
MLOps
Getting models out of notebooks and into production, then keeping them honest. Deployment, feature stores and versioning so a result can be reproduced.
Monitoring and retraining are the parts most often skipped. A model that was accurate at launch and never re-evaluated drifts quietly, and the failure is invisible until a decision goes badly.
Includes- Model deployment
- Feature stores
- Monitoring
- Retraining
Why it matters
Data nobody trusts is worse than no data at all.
Once two dashboards disagree, every number becomes negotiable, and meetings turn into arguments about whose figure is right rather than what to do about it. Trust is a technical property: it comes from lineage, quality checks and a single definition of what a metric means.
Here’s what proper data foundations return:
Reporting that is not manual
Pipelines that run on a schedule rather than a person. The monthly assembly job stops consuming a week of somebody capable.
You can trace any figure
Lineage and a data catalogue mean a number can be followed back to its source. Being able to answer "where did this come from" is what makes people act on it.
Bad data caught early
Quality checks at ingestion, so a broken upstream feed is flagged the day it breaks rather than discovered in a board pack three weeks later.
Models that stay useful
MLOps for deployment, monitoring and retraining. A model that was accurate at launch and never re-evaluated is a liability quietly decaying in production.
Access without a free-for-all
Governed access and clear ownership, so teams can self-serve the data they need without everyone having a key to everything.
The value is not in having data. It is in being able to ask a question on a Tuesday and get an answer you are willing to act on before the decision has passed.
Why Data & AI with Zefract
One team, one goal, nothing lost in the handoff.
The people building your pipelines are the same people building the products generating the data, so what gets captured is designed rather than reverse-engineered later from whatever happened to be logged.
Reporting taking a week to assemble?
Start with a data assessmentFAQ
Frequently asked questions
Almost always because there is no agreed definition. Two teams compute "revenue" or "active user" slightly differently, both are defensible, and every meeting becomes an argument about whose figure is right. A modelled warehouse with one definition per metric is what ends that, and it is a modelling problem rather than a tooling one.
A database is enough until you need to combine sources or ask questions across them, at which point querying production directly starts affecting the thing your customers use. The trigger is usually the second or third system, not a data volume. We will say if you are not there yet.
Quality checks at ingestion and lineage throughout. A broken upstream feed is flagged the day it breaks rather than discovered in a board pack three weeks later, and any figure can be traced back to its source. Being able to answer "where did this come from" is what makes people willing to act on it.
The practice of running models in production the way you run software: deployment, versioning, monitoring and retraining. You need it the moment a model informs a real decision, because a model that was accurate at launch and never re-evaluated drifts quietly, and the failure is invisible until something goes badly.
Snowflake, Databricks and BigQuery all work well and suit different situations. The deciding factors are your volumes, what your team already knows, what your cloud commitment looks like and how you expect to query it. The modelling work matters far more than the choice between them.
Next step
Ready when you have a brief.Or half of one.
Send whatever you have. You get scope, a timeline and a number back within three working days.
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