Your data team gets the keys
The reasonable concern when an outside firm shows up is that they are here to replace you, or to leave behind something only they can operate. Neither is the model.
This page is the technical brief. It is written to be printed and handed to whoever asks the hard questions.
This page is formatted to print as a one-pager (Ctrl/Cmd + P).
What DAX does alongside your team
Accelerate architecture
Decisions your team would otherwise litigate for months — storage layout, identity strategy, model grain — settled against patterns that have already run in production.
Bring healthcare-specific patterns
Claims normalization across payers, EMPI survivorship, ADT deduplication, claims lag handling. This is where generic data engineering stalls.
Build alongside the team
Your engineers are in the repo, in the reviews, and in the design calls. Not handed a finished box at the end.
Document the environment
Data dictionaries, lineage, runbooks, and architecture decision records written as the work happens, not reconstructed afterwards.
Transfer operational knowledge
Paired delivery, walkthroughs, and explicit handover of the on-call and monitoring picture.
Make the internal team more capable
The measure of success is your team onboarding the next payer feed without calling us.
We are not building a dependency on DAX. We are helping your team own and operate a capability the organization previously rented.
What sits in your environment when we are done
- Cloud accounts, subscriptions, and network boundary
- Database / warehouse and all storage
- Schemas and healthcare domain models
- Raw source data, retained and reprocessable
- Pipelines and orchestration code
- Transformations and semantic/metric definitions
- Data quality rules and exception tables
- Documentation and architecture decision records
- Monitoring, alerting, and runbooks
- The operational knowledge to run all of it
Answers before you have to ask
- Where does it run?
- Your cloud account, under your credentials, inside your network boundary. Not a tenant in someone else's platform.
- What happens to raw data?
- Every source file and HL7 message is retained in original form. When a definition changes, you reprocess history yourself rather than filing a vendor request.
- What is the identity strategy?
- One enterprise patient identity across clinical, claims, and ADT — deterministic plus probabilistic matching, survivorship logic, and source-ID crosswalks retained for audit.
- How are metrics defined?
- A versioned, attribution-aware semantic layer. A measure means the same thing in the dashboard, the extract, and the board deck, and changes are traceable.
- What about data quality?
- Row and field-level validation, cross-source reconciliation, volume and lag monitoring, with exceptions surfaced to the people reading the numbers rather than smoothed over.
- What if the engagement ends?
- Where contractually accurate for your engagement, the foundation keeps running in your environment and your team operates it. That is the design goal, not a concession.
The full stack, layer by layer, is published on the reference architecture page, and the domain-level detail lives under clinical, claims, and ADT.
See who this model fitsBring your hardest reconciliation problem.
The useful version of this call is technical. Come with your source list, your cloud constraints, and the thing that has been broken for two quarters.