Ingestion
Connectors into your source systems: application databases, EHR APIs, support platforms, e-commerce events, device telemetry. Every record lands with its source, its timestamp and its ingestion lineage intact.
Every number traceable. Every report defensible.
Your dashboards are only as trustworthy as the pipeline underneath them. We build the governed data layer that makes the numbers something you can sign your name to.
Bronze, silver and gold zones. Versioned transformations. Quality checks that fire before bad data reaches a dashboard. Every metric traceable back to a defined rule and a validated source. Delivered in four-week cycles, deployed into your environment.
The dashboard is not the problem. The pipeline is.
You already have a BI tool. Someone built the dashboards. The charts render. Yet when a board member asks why a number moved, the honest answer is that it will take an analyst three days to find out, because nobody is fully certain which system introduced the discrepancy.
That is not a visualization problem. Every trust gap in analytics traces back to what happened before the data reached the chart.
- 01
The number nobody can defend
Two dashboards report the same metric differently because they pull from different sources with different filters, and the logic lives in someone’s saved query rather than anywhere documented.
- 02
The gap nobody flagged
A source system stopped sending data on a Thursday. Nobody noticed until the monthly review, and by then three weeks of reporting was quietly wrong.
- 03
The model trained on partial data
A predictive model is running on data with systematic collection gaps nobody has quantified. The model is not wrong; the training data is incomplete, and no one knows by how much.
Four layers between your source systems and a number you trust.
Bronze → Silver → Gold
Raw data preserved immutably in bronze. Cleaned, normalized and conformed in silver. Analytics-ready and business-defined in gold. Every transformation is versioned code, not a saved query in someone’s account, so the logic behind a number is inspectable and reproducible.
Quality gates
Automated checks on freshness, volume, distribution and completeness that fire before data moves downstream. When a source stops sending or a field starts arriving null, you know that day, not at month-end close.
Reporting and AI-readiness
Dashboards connected to the gold layer, with a data quality scorecard alongside them so readers can see the confidence behind what they are reading. And because the pipeline is governed, the same layer is ready to support AI workflows without a rebuild.
Built on tools your team can maintain.
We do not build proprietary black boxes. Every engagement uses established, well-documented open-source and platform tooling, so when the cycle ends your team can extend and maintain what we built without depending on us.
| Layer | What we use | Why |
|---|---|---|
| Data warehouse | Databricks or BigQuery | Both handle healthcare-scale data with the governance and access controls regulated environments require. We are a certified Databricks partner. |
| Ingestion | Airbyte | Open source, 300+ connectors, self-hosted so your data never transits a third-party SaaS you did not vet. |
| Transformation | dbt Core | Versioned, tested, documented SQL transformations. The industry standard, and the reason your logic is reviewable rather than buried in someone’s saved query. |
| Data quality | Soda Core | Automated freshness, volume and anomaly checks written as code and run on every pipeline execution. |
| Orchestration | Docker · Kubernetes · Terraform | Infrastructure as code. Reproducible environments. Deployed into your cloud account, not ours. |
| Reporting | Metabase, or your existing BI tool | We connect to whatever you already use. If you have Tableau or Looker, keep it; we make the data underneath it trustworthy. |
You own the code, the infrastructure and the documentation at the end of every cycle. No proprietary lock-in and no dependency on us to keep it running.
For teams whose data grew faster than their infrastructure.
Digital health platforms, Series A to C
You are generating meaningful product and clinical data, your investors want operational reporting, and your engineers are spending time on pipelines instead of product.
Biotech and research organizations
Assay data, screening results and instrument output that need to be reproducible and traceable months later, when a collaborator or a regulator asks how a result was produced.
Connected device and physical AI companies
Device telemetry is becoming a strategic asset, sometimes training data that partners depend on. That raises the bar on lineage, quality scoring and provenance well beyond internal analytics.
Healthcare operators with reporting obligations
Organizations reporting quality metrics, utilization or outcomes to payers and regulators, where the reported number needs an inspectable derivation behind it.
One pipeline. Four weeks. Deployed in your environment.
We scope each cycle to a defined set of source systems and one analytics outcome. Narrow enough to complete fully. Real enough that your team uses it the following week.
- Week 1
Sources + architecture
We agree the analytics outcome and its metric definitions, connect the source systems and land raw data in bronze with lineage intact.
You getRaw data flowing with lineage, and a documented metric specification. - Week 2
Transformations
We build versioned silver and gold transformations and reconcile them against the numbers your team reports today.
You getA governed data model producing your key metrics, and a first demo. - Week 3
Quality gates
We add freshness, volume, distribution and completeness checks, access controls and the data quality scorecard.
You getAutomated quality gates and a scorecard alongside the data. - Week 4
Reporting + production
We connect your BI tool to the gold layer, deploy into your cloud account, walk your team through it and document everything.
You getA production pipeline your team owns, plus a written scale roadmap.
At the end of week four you decide whether to run another cycle: more sources, more metrics, or the AI workflows the governed layer now supports. How we work →
Reasonable questions.
We have engineers. Why would we not build this ourselves?
You probably could. The question is opportunity cost: every sprint your team spends on ingestion and transformation infrastructure is a sprint not spent on product. We have built this pattern repeatedly, so we are not learning on your timeline. And at the end of the cycle it is your code in your environment, so there is no long-term dependency.
Do we have to replace our BI tool?
No. We connect to whatever you already use: Tableau, Looker, Power BI, Metabase. This is the layer underneath your dashboards, not a replacement for them. If your visualization layer is fine and your data is not, this is the right fix.
We already use an AutoML or predictive analytics tool. Does this conflict?
It complements it. AutoML platforms are good at what they do, but a model can only be as accurate as the data feeding it. If there are structural gaps in collection or inconsistencies across sources, the tool cannot detect that; it will train confidently on incomplete data. This fixes the input problem underneath.
Does this touch PHI?
Where required, yes, and it is built for that. Downstream consumers that do not need protected values never see them. We execute a BAA before any data access, work in isolated environments in your cloud, and tokenize PHI at the ingestion boundary so protected data never reaches the AI layer directly. More on security & PHI →
Who owns what we build?
You do: the code, the infrastructure definitions and the documentation. Everything is deployed into your cloud account using open-source and platform tooling your team can maintain independently.
Tell us what is hard to measure.
If we can scope it into a four-week cycle, we will tell you exactly what that includes and what it costs. If we cannot, we will tell you that too.
Book a 30-minute scoping call →- We’ll ask three questions
- 01Which source systems hold the data?
- 02What question are you trying to answer?
- 03What has been blocking it?