AI Data Engineering
Outcomes
AI projects stop stalling on data
The pattern is familiar — a promising pilot spends three months waiting on access, discovers the data is inconsistent, and quietly dies. Fixing the foundation removes that failure mode across every future initiative.
One version of the truth
Consolidated data with agreed definitions. A surprising number of AI disagreements are actually disagreements about what "active customer" means.
Quality you can see
Automated checks on completeness, validity and freshness, with alerts. Silent data quality failures produce confidently wrong AI output.
Governance that permits rather than blocks
Clear lineage, access control and retention, so security and compliance can approve use rather than defaulting to no.
What we build
Ingestion pipelines from your operational systems, SaaS platforms, files and third-party sources, incremental and monitored.
Transformation layers with tested, documented, version-controlled business logic — usually dbt — so definitions are explicit rather than folklore.
Data quality framework. Automated tests for completeness, uniqueness, referential integrity, distribution and freshness, with alerting and an owner.
Storage architecture appropriate to your scale — warehouse, lakehouse, or a well-designed Postgres, which is the right answer more often than the industry admits.
Unstructured data pipelines for documents, images, audio and text: extraction, processing, embedding and indexing. Increasingly the more valuable half of the work, and the half most data teams have never built.
Governance and access. Catalogue, lineage, role-based access, PII classification and retention policy — designed against the regulation you are actually subject to, including GDPR and India's DPDP Act.
How it works
Weeks 1–2 — Landscape assessment. What data exists, where, in what condition, who owns it, and what the AI use cases actually require. Requirements-driven, so we build what is needed rather than everything.
Weeks 2–4 — Architecture and priority pipelines. Target design plus the pipelines feeding the nearest-term use cases. Value before completeness.
Weeks 4–8 — Build out. Remaining pipelines, transformation layer, quality framework, monitoring.
Weeks 8–10 — Governance and handover. Access controls, catalogue, documentation, and training your team to operate it. Data platforms outlive projects and need an internal owner.
Ongoing. New sources, evolving definitions, quality monitoring.
Technology
Warehouses and lakehouses: Snowflake, BigQuery, Databricks, or Postgres where the scale genuinely does not warrant more.
Transformation: dbt for SQL-based logic with testing and documentation built in.
Orchestration: Airflow, Dagster or Prefect.
Ingestion: Fivetran or Airbyte for standard connectors, custom where the source is unusual.
Unstructured: document processing, embedding pipelines, vector stores integrated with the structured layer rather than siloed beside it.
Quality: Great Expectations, dbt tests, or a purpose-built framework.
Where this applies
Necessary before almost any serious AI work, and most valuable where data is spread across many systems that disagree with each other.
Less urgent where you have a single clean source and one narrow use case. Do not build a data platform to support one model.
How we scope and price
Fixed scope, quoted after a landscape assessment. Cost is driven by the number and awkwardness of source systems, the current state of the data, and governance requirements. We strongly recommend scoping to the use cases you actually intend to pursue — the most expensive version of this work is the one that builds a complete platform nobody has a use for yet.
- Snowflake
- BigQuery
- Databricks
- Airflow
- Dagster or Prefect
- Great Expectations
Frequently asked questions
Some of it, proportional to the use case. A narrow project on one clean system needs very little. A programme spanning functions needs real foundations. We scope to the plan rather than defaulting to a platform build.
Often for structured, analytical use cases. Usually not for AI involving documents, images or conversation, since that data typically sits outside the warehouse entirely and needs its own pipeline.
Priority pipelines commonly deliver in four to six weeks; a fuller platform runs longer. We sequence so the first AI use case is unblocked early rather than waiting for completion.
You should. We build it to be operable by your team and include handover and documentation. A data platform only your vendor can maintain is a liability.
Designed in — PII classification, access control, retention, and residency constraints shaping the architecture from the start rather than being retrofitted after a compliance review.
The pipeline work is familiar. What is different is the unstructured side — embedding pipelines, document processing, vector indexing — and quality requirements driven by model behaviour rather than dashboard accuracy.
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