Stack 02 · Data & Analytics
Models, dashboards and automation inherit the quality of the data they receive. Aviox builds ingestion, transformation, storage, streaming and quality controls so downstream systems operate from defined, observable data.
How we choose
A warehouse nobody trusts is an expensive copy of the data you already had. Trust comes from tests and lineage, not from the brand on the invoice.
The question we get asked is which warehouse. The question that actually matters is whether anyone can tell you where a number came from. We have walked into plenty of estates with a modern warehouse and no lineage, no tests and three competing definitions of revenue — and the AI roadmap sitting on top of it was never going to work.
So we build the unglamorous parts: orchestration with retries and alerting that reaches a human, transformations as version-controlled code with tests, a single product and customer hierarchy, and lineage from dashboard back to source row. Then the models and the assistants have something solid to stand on.
Non-negotiables
The toolchain
Each row says what the tool is used for and links to related pages. Named tools are examples of what can sit in each layer, not a list of partnerships or certifications.
Scheduling, dependencies, retries and backfills, with alerting that reaches a person rather than a dashboard nobody opens. A common backbone for the pipelines we hand over.
Transformations as version-controlled, tested, documented code. The lineage graph it produces is usually the first time a client can see how a number is actually built.
Where workloads are spiky and compute needs to scale independently of storage. Strong governance features and a clean separation that makes cost attribution honest.
Our choice where the estate is already on Google Cloud or the workload is large, append-heavy analytics. Same dbt layer on top, so the warehouse stays a replaceable component.
A common transactional database for what we build and, with pgvector, often the retrieval index too, which keeps permission scoping simple.
Where the shape of the data genuinely varies per record — document extraction output, event payloads, nested content models that would be painful to normalise.
For decisions that genuinely need sub-second data: fraud scoring on the transaction path, shipment events, session behaviour. Not used as a general-purpose message bus by default.
Response caching, rate limiting and work queues — including caching model responses, which is one of the cheapest inference cost reductions available.
Freshness, uniqueness and business-rule tests wired into the orchestration, plus column-level lineage. This is what makes a data estate genuinely AI-ready rather than merely modern.
Built on this stack
The useful question about a technology list is what has been built with it. These are examples of patterns that apply this stack.
Governed ingestion per channel, one agreed hierarchy, tested transforms, then signals or models on top where the data supports them.
Streaming where a decision needs sub-second data, with a shared feature store so the model in the request path and the model in the warehouse read the same definitions.
Cohort and learner trajectories built on the same warehouse pattern, showing who needs attention and the evidence behind the signal.
Note Products we run ourselves patterns are starting points configured to your data and rules Read the case study
Request a quote
Talk to an engineer about your architecture, or get a quote for the work.
In the field
Three sectors where this particular layer carries the most weight. Each page shows the five-step automation pattern in that sector's own vocabulary.
Catalog enrichment, demand signals and commerce-data integration.
Explore Industry 02Lending, KYC and servicing workflows with auditable review.
Explore Industry 04POD processing, exception triage and freight-invoice audit support.
ExploreAlso All ten industries run on some combination of these four stacks
Connected
No system we build uses only one of these four layers. Here is the service line that leads the work, and the stacks that sit either side of this one.
Services that use this stack
Governed, cloud-native pipelines with lineage and quality tests that make your data genuinely AI-ready.
Explore Service 04Forecasting, propensity and risk models with feature stores, drift monitoring and explainability built in.
Explore Service 05Model CI/CD, versioned datasets, canary rollouts and cost-aware inference — the plumbing that keeps it up.
ExploreThe other stacks
Questions
Asked most often by data and analytics teams.
Yes. We build on the platform you already use where it fits, and recommend a change only when the existing one is the constraint.
With tests at each stage of the pipeline, clear ownership of each dataset and alerts when freshness or quality drops, so problems are found before reports are.
Yes. Access control, lineage and documentation are part of the build so people can see where a figure came from and who can use it.
Yes. Reliable pipelines serve dashboards and models alike, which is why data quality comes first.
Request a quote
Tell us the systems, the data and what depends on it. We will suggest where ownership, tests and quality checks would pay back first.
