AI use-case and data-readiness diagnostic
In a focused engagement, rank AI opportunities by business value, data fitness, workflow fit, risk, and implementation cost. Leave with a decision memo, prioritized map, and next-project recommendation.
Build the Data AI Operating System your health-tech company needs.
ref(health) helps health-tech startups turn messy healthcare data, model choices, and executive pressure into a governed operating capability—from decision-ready analytics to AI workflows a team can run.
- 21 years
- Healthcare operating context
- dbt fluent
- Analytics engineering that can be trusted
- Model orchestration
- Multi-provider depth matched to the job
The buyer problem is not "add AI."
The problem is knowing which healthcare workflows can absorb AI, which data can support it, which controls are missing, and where a senior data leader can make the work measurable.
Payer, provider, wellness, navigation. The work is grounded in how healthcare organizations actually operate.
Analytics plus translation. Data teams, business owners, and executive decisions need one operating language.
Practical AI delivery. The work pairs model capability with governance, measurement, documentation, and adoption.
Start with a diagnostic. Build the operating system next.
Focused engagements for health-tech founders and teams that need a defensible next move on data, analytics, or AI. Larger builds follow the decision.
Company Data AI Operating System
Design the operating layer that connects data products, model strategy, agent workflows, governance, measurement, and executive decisions. The output is a practical system a team can run, not a pile of AI experiments.
Healthcare data landscape assessment
Map sources, owners, metrics, quality failure points, redundant work, single points of failure, and modernization priorities across claims, clinical, member, product, provider, and operational data.
dbt analytics engineering accelerator
Convert brittle SQL, unclear metrics, and dashboard mistrust into tested models, lineage, docs, reviews, and a workflow business teams can understand.
Model orchestration and agentic workflows
Choose when to orchestrate multiple models across labs and providers, when to go deep with one provider, and where agents can safely help with research, documentation, operating rhythm, analytics triage, and knowledge work.
Payer and provider modernization support
Support teams working through provider data, interoperability, prior authorization, quality, care navigation, member engagement, cost-of-care, and operational analytics pressure.
Fractional data and AI leadership
Add senior judgment for roadmaps, operating models, executive narratives, vendor decisions, hiring plans, and the translation layer between data delivery and business value.
Start small, stay useful. The $2,500 diagnostic is deliberately narrow. Roadmaps, builds, and fractional leadership are scoped to the decision, data, and team in front of us.
Start with the decision.
The right answer depends on the business question, the data, the workflow, and the controls. These are common starting points for a paid engagement.
What is a Data AI Operating System?
It is the operating layer that connects business decisions, data products, model choices, workflow automation, governance, measurement, and adoption. It gives a healthcare team a way to run AI work as a capability instead of a collection of experiments.
When should a healthcare company use multiple AI providers?
Use multiple providers when the work benefits from different model strengths, independent checks, resilience, or a deliberate risk and cost posture. When policy, integration, delivery speed, or an existing commitment matters more, going deep with one provider can be the better path.
What does a healthcare data landscape assessment cover?
It maps sources, owners, critical metrics, quality failures, lineage, redundant work, single points of failure, and modernization priorities across claims, clinical, member, provider, product, and operational data.
Why use dbt for healthcare analytics?
dbt gives analytics teams a shared workflow for versioned SQL models, tests, documentation, lineage, review, and ownership. That makes metrics easier to inspect and easier for business teams to trust.
AI capability needs an operating system.
Useful AI work is not one prompt, one model, or one vendor. It is the ability to choose the right model strategy, wire it into data and workflow, and govern the result.
01
Model/provider strategy. Orchestrate multiple models across OpenAI, Anthropic, Google, Microsoft, xAI, and other providers when fit, cost, resilience, or independent checks matter—or go deep with one provider when security, integration, or a current commitment makes that the right call.
02
Operating agents. Build chief-of-staff patterns, task guidance, strategy personae, contingency planning, and executive review loops that reduce repetitive work and preserve human judgment.
03
Data landscape analyzers. Inspect sources, models, ownership, risks, and documentation gaps before a team overbuilds or automates the wrong work.
04
Documentation and resilience. Put rigor around repository documentation, handoffs, redundancy, role coverage, single points of failure, and keeping team knowledge aligned.
Buy the outcome, not a GitHub tour.
Public repositories can support technical diligence, but they are not the sales story. The useful proof is a decision that got clearer, repetitive work that got smaller, a working artifact, clear controls, and a team that can keep operating what was built.
Recent capability work includes AI-supported work and continuity planning, operating copilots, data-landscape analysis, documentation workflows, and decision analytics that makes uncertainty visible. These are capability examples, not invented client case studies.
In live healthcare work, these patterns have reduced tedious, low-value tasks, opened capacity for human judgment and creativity, and supported workforce and capacity planning.
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01
Decision memo
What to fund, stop, govern, build, or buy.
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02
Working artifact
A model, agent, dashboard, analysis, or workflow tested against real work.
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03
Control package
Ownership, risk notes, measurement plan, documentation, and handoff.
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04
Operating cadence
The rhythm that keeps the Data AI Operating System useful after handoff.
The working method.
Clear enough for executives, technical enough for delivery teams, and documented enough to survive handoff.
- Frame the money question. Define the decision, owner, cost of delay, and proof needed to fund the next move.
- Map the system. Trace data, workflow, risk, governance, and adoption constraints before tools enter the room.
- Build the smallest credible artifact. Ship a model, agent, dashboard, diagnostic, or prototype that can be tested against real work.
- Install the operating rhythm. Leave tests, documentation, lineage, decision logs, model guidance, and a team that understands what changed.
Bring the healthcare data or AI decision with money behind it.
Start with a focused diagnostic from $2,500. Share the business problem, systems involved, deadline, and decision owner; the intake will shape the first working session.