Product & platform
Ingestion, transformation, warehousing and the analytics on top. Most disappointing AI projects are a data problem wearing a model costume, so this is often the work that has to happen first.
We trace the numbers you already report back to where they come from. That usually finds the real problem, and it is almost never the model.
The warehouse is modelled for the questions being asked, and the data contracts are written at the same time. A contract is what makes a pipeline fail loudly instead of quietly.
Pipelines are code, with tests, partitions and backfills. Every transformation is reproducible from raw, because a number nobody can rebuild is a number nobody should trust.
Freshness, volume and referential checks block downstream assets rather than emailing somebody. Cost is attributed per pipeline, so an expensive query is visible before the invoice is.
Your analysts work in the warehouse without going through us. Lineage and documentation ship with it, because a pipeline one person understands is an outage waiting for a holiday.
A partitioned asset and a blocking contract check, so nothing downstream builds on data that failed it and nobody finds out a fortnight later.
"""A pipeline that fails loudly rather than quietly."""
import dagster as dg
DAILY_FROM_LAUNCH = dg.DailyPartitionsDefinition(
start_date="2026-01-01",
)
@dg.asset(
partitions_def=DAILY_FROM_LAUNCH,
)
def orders(context: dg.AssetExecutionContext) -> dg.MaterializeResult:
day = context.partition_key
rows = warehouse.load("raw.orders", where=f"day = '{day}'")
return dg.MaterializeResult(metadata={"rows": len(rows)})
# `blocking` is the whole point: downstream assets do not run
# on data that failed its contract, so nobody builds a report
# on top of it and finds out a fortnight later.
@dg.asset_check(asset=orders, blocking=True)
def every_order_has_a_customer() -> dg.AssetCheckResult:
orphans = warehouse.count(
"orders o left join customers c on c.id = o.customer_id "
"where c.id is null"
)
return dg.AssetCheckResult(
passed=orphans == 0,
metadata={"orphans": orphans},
)A retrieval assistant over clinical policy and procedure documents, answering staff questions with span-level citations and refusing to answer when the documents do not support one.
A document-extraction pipeline for a retail group, classifying and extracting structured records from supplier invoices and delivery notes arriving in every format a supplier felt like using.
Not always, but you do need the specific data the feature reads to be reachable, clean and fresh enough. We scope that narrowly during Discover rather than proposing a platform programme in front of a feature you wanted this quarter.
Batch unless the decision the data feeds genuinely cannot wait. Streaming roughly doubles the operational surface, so it should be paid for by a real requirement rather than by the architecture diagram looking more modern.
Metrics defined once in a versioned semantic layer, tests for freshness, volume and distribution on every model, and alerting on the tests. Data pipelines are treated as software — reviewed, versioned and deployed like any other service.
Yes. Snowflake, BigQuery, Databricks, Redshift or Postgres — we build into what you already run and pay for. Migrating a warehouse is a project of its own and should be justified on its own terms, not smuggled into a feature build.
A first call is 30 minutes and costs nothing. Bring the problem rather than a spec — the useful part is usually working out whether this is the right shape of solution at all.
contact@algologix.coWe reply within 24 hours.