Data & Machine Learning
Data Engineering & AI Infrastructure
Pipelines, warehouses, and vector stores that make your data usable, plus the cloud infrastructure to run it all.
Every stalled AI project we are asked to rescue has the same root cause: the data isn't reachable, isn't trustworthy, or isn't fresh. We build the layer underneath: ingestion from your operational systems, a modelled warehouse with tested transformations, embeddings and indexes kept in sync, and the infrastructure and CI/CD to run it reliably. Unglamorous, and the reason everything above it works.
What's included
- ELT pipelines
- Warehouse modelling
- Vector store sync
- Cloud infra & CI/CD
What you get out of it
- One trustworthy source of truth instead of six conflicting exports
- Data quality tested on every run, with failures alerting before anyone builds on them
- Infrastructure defined in code, reproducible, and documented for your team
AI is a data problem wearing a model costume
Retrieval quality, forecast accuracy, and agent reliability all bottom out at the same place: whether the underlying data is complete, current, and correctly joined. We treat pipelines like application code: version controlled, tested, monitored, with schema contracts and alerting, so the systems built on top have something solid to stand on.
dag: warehouse_daily · 08:00 -03extract: postgres, stripe, hubspot, s3 -> rawtransform: 64 models · 212 tests✓ 209 passed ⚠ 3 warn ✗ 0 failedpublish: marts.customers, marts.ordersembed: 1,842 changed docs -> vector indexfreshness: all sources < 45min ✓
What we build
Ingestion & ELT
Reliable extraction from databases, SaaS APIs, files, and event streams, incremental where possible, with backfills, retries, and schema-change handling that doesn't silently drop columns.
Warehouse modelling
Layered, documented transformations producing marts your analysts and models can both trust, with tests on every assumption you'd otherwise find out about the hard way.
Vector & search infrastructure
Embedding pipelines and indexes kept in sync with the source of truth, including deletions and permission changes. That is the part most RAG prototypes never build.
Data quality & contracts
Freshness, volume, uniqueness, and distribution checks that fail loudly, plus schema contracts between producers and consumers so an upstream change doesn't quietly break a model.
Cloud infrastructure & CI/CD
Infrastructure as code, containerised services, staged deploys, secrets management, and observability, set up so your team can operate it after we hand it over.
Technologies we work with
- Python
- dbt
- Airflow
- Postgres
- BigQuery
- Databricks
- Spark
- Snowflake
- Kafka
- Terraform
- Docker
- AWS
Frequently asked questions
Not always, but you need a reliable path to the data. For a narrow first use case we can read from source systems directly; once a second or third use case appears, a warehouse stops being optional.
AWS most often, and we are comfortable in GCP and Azure. We build with infrastructure as code and avoid gratuitous vendor lock-in so the choice stays yours.
Yes. We would much rather fix and extend what you have than argue for a rewrite. A migration is a recommendation we only make when the current setup is genuinely blocking you.
Least-privilege access, encryption in transit and at rest, PII masking in non-production environments, documented retention, and data flows mapped for LGPD and GDPR review.
Next step
Need data engineering & ai infrastructure?
Tell us what you're trying to build. We'll give you an honest read on the approach, the effort, and what it costs to run.