OPERATIONAL ANALYTICS
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, 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 4-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. And 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.
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.
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.
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.
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.
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 that produced 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 consumers can see the confidence level 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 |
|---|---|---|
| LayerData warehouse | What we useDatabricks or BigQuery | WhyBoth handle healthcare-scale data with the governance and access controls regulated environments require. We are a certified Databricks partner. |
| LayerIngestion | What we useAirbyte | WhyOpen source, 300+ connectors, self-hosted so your data never transits a third-party SaaS you did not vet. |
| LayerTransformation | What we usedbt Core | WhyVersioned, tested, documented SQL transformations. The industry standard — and the reason your logic is reviewable rather than buried in someone's saved query. |
| LayerData quality | What we useSoda Core | WhyAutomated freshness, volume, and anomaly checks written as code and run on every pipeline execution. |
| LayerOrchestration | What we useDocker · Kubernetes · Terraform | WhyInfrastructure as code. Reproducible environments. Deployed into your cloud account, not ours. |
| LayerReporting | What we useMetabase, or your existing BI tool | WhyWe 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 engineering team is 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 | Focus | What happens | What you get |
|---|---|---|---|
| Week 1 | FocusRequirements + Architecture | What happensWe work with your compliance team to map the actual requirements, connect to source systems, and establish the governance model | What you getWorking data pipeline and a documented rule specification for review |
| Week 2 | FocusRules Engine | What happensWe codify requirements into deterministic, versioned rules and validate outcomes against your real operational records | What you getLive rules engine evaluating your events — first demo to your team |
| Week 3 | FocusCopilot + Hardening | What happensWe layer cited explanations, add audit logging and access controls, and run the structured evaluation set | What you getGoverned copilot layer plus an evaluation report on accuracy and guardrails |
| Week 4 | FocusProduction | What happensDeployment into your environment, team walkthrough, documentation, and the roadmap for expansion | What you getProduction system your team owns, plus a written scale roadmap |
Reasonable questions.
-
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.
-
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.
-
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.
-
Where required, yes, and it is built for that. We execute a BAA before any data access, deploy into isolated environments in your cloud, and tokenize PHI at the ingestion boundary where downstream consumers do not need protected values.
-
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 challenging to measure
Thirty minutes. Three questions: which source systems hold the data, what question you are trying to answer with it, and what has been blocking it. 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.