Data engineering services for one trusted number.

Everything between your source systems and the people and AI that use the data: ingestion and streaming, a lakehouse or warehouse, transformation and modeling, quality checks and data contracts, a semantic layer for metrics, governance and access control, and retrieval for AI. At DigyAi the work starts with a data audit of one to two weeks and lands as code in your repository, with every metric definition written down.

Data an AI system can find, read and trust for a specific use: governed, current, described by metadata and lineage, and served in the form the model needs, whether that is a table behind a metric, a document split into passages with embeddings, or a feature computed the same way in training and in production. Readiness is judged per use case, so the audit starts from the AI work you plan.

Not for every use. A narrow automation can read one source system directly. Anything analytical, cross-functional or agent-facing needs one governed layer, so the dashboard and the agent answer with the same number and can show where it came from. Gartner expects organizations to abandon 60% of AI projects unsupported by AI-ready data through 2026.

Data engineering builds the pipelines, storage, quality checks and models that make data reliable and available; data science and AI work on top of that layer to predict, classify and answer. Without the first, most of a data scientist's time goes to cleaning exports, and an AI model learns from whichever version of the truth it happened to find.

Most enterprises do both. An experienced partner builds the platform and its patterns quickly, and your team owns and extends it. We work in your repository and your environment, pair with your engineers from the first sprint and hand over runbooks and definitions, so the platform never depends on us.

It depends on how the data is read. A warehouse suits modeled reporting, a data lake keeps raw files of any shape cheaply, and a lakehouse puts open table formats such as Apache Iceberg or Delta Lake over the lake, so one copy of the data serves reporting, machine learning and AI. Most enterprises we work with land on a lakehouse, or on a warehouse that reads open tables.

As fresh as the decision that uses it. Financial close and board reporting run well on hourly or daily batches; fraud checks, pricing, stock levels and AI agents that act on live state need streaming measured in seconds. We set a freshness target for each table and pay for streaming only where a decision needs it.

Snowflake, Databricks, Google BigQuery, Microsoft Fabric, Amazon Redshift and Postgres, or an open lakehouse on Apache Iceberg with Trino or Spark. Kafka and Debezium for streams and change data capture, dbt or SQLMesh for transformation, Airflow or Dagster for orchestration. If your team already runs a platform, we build on it.

A semantic layer defines each business metric once, with its formula, filters and grain, and serves it to every tool that asks. For AI it matters twice: an agent that queries the semantic layer uses your definition of revenue instead of inventing one from raw tables, and every answer can cite the metric and the version it used.

With checks at every step: a contract at each source for schema, ranges and freshness, tests on every model on every run, and monitors for volume and anomalies. Rows that break a contract are held and reported to their owner before they can reach a total, and every incident becomes a new test.

In your own environment, cloud or on-premises, in the region you choose. Personal data is tagged and masked by role, named engineers work through access you grant, logged in your own audit trail and revocable at any time, and production data is not copied to personal devices. Your NDA and DPA, with EU standard contractual clauses or the UK IDTA where GDPR applies, are signed before discovery starts.

One domain at a time, with old and new running side by side. Each table is reconciled on row counts, totals and key metrics before any report moves, and the old path is switched off only when the numbers match. If a check fails, the reports stay on the old path until it passes.

No. Tables stay in open formats in your own storage, code and definitions live in your repository, and access runs through your identity provider. Any engine that reads Iceberg or Delta can query the data, and your team can run the platform without us.

Five things drive the cost: how many sources you have and the condition they are in, data volume and freshness targets, governance and audit requirements, the legacy jobs and reports to migrate, and the platform you choose. The audit ends in a written proposal for each phase, and each phase is scoped and approved in writing before it starts.

The data audit takes one to two weeks and ends in a dated plan, set mostly by the number and condition of your sources. Pipelines then go live source by source, each with its checks, so the first trusted numbers reach people well before the whole platform is finished.

It runs to the freshness targets agreed for each table, with monitoring, an incident runbook and a monthly report on cost per pipeline. We can run it for you as a managed service, run it alongside your engineers, or hand it over completely.