Your EHR holds the answers. Your analytics stack doesn't know yet.
Epic, Cerner, and Meditech hold your most valuable clinical data — but it's fragmented, un-integrated, and disconnected from the AI systems you're trying to build. We bridge that gap. From HIPAA-compliant data architecture to clinical AI that integrates into real workflows, we span the full lifecycle — so you're not managing a strategy firm, an implementation firm, and a training vendor separately.
increasing AI spend in 2025
ROI — Deloitte, 2024
siloed systems daily
projected by 2026
Clinical analytics, EHR integration, AI-assisted documentation, population health management, revenue cycle analytics, and clinical decision support — built around Epic, Cerner, and Meditech environments.
Claims analytics, actuarial ML, fraud, waste and abuse detection, risk stratification, CMS regulatory reporting, and member intelligence — for commercial, Medicare Advantage, and Medicaid organizations.
Clinical trial data management, pharmacovigilance analytics, real-world evidence platforms, regulatory submission data (FDA, EMA), and medical device performance monitoring for life sciences organizations.
What's shaping healthcare
data and AI right now.
The regulatory, reimbursement, and technology landscape is shifting fast. Here's what's driving urgency — and where the opportunities are — for health system leaders.
The FDA's expanded predetermined change control plan (PCCP) framework allows AI/ML-based medical devices to update continuously without new 510(k) clearance — but only with rigorous monitoring and change documentation baked in. Health systems adopting AI-enabled diagnostics now face an ongoing data governance obligation that most are not operationally ready for.
CMS continues to expand alternative payment models — ACOs, bundled payments, primary care transformation programs — now covering a larger share of Medicare lives. These models reward outcomes that require real-time data pipelines most organizations simply don't have. Manual spreadsheet-based submissions are no longer adequate, and the reporting requirements keep tightening.
Ambient clinical documentation tools are being adopted rapidly across health systems. Clinician satisfaction with ambient AI has driven enterprise-wide expansion at most early adopters. The problem: downstream data quality, EHR write-back accuracy, and integration reliability remain inconsistent without a clean data engineering layer underneath the model.
Every challenge you're facing.
Exactly how we solve it.
We don't present a menu of services and leave you to figure out the fit. Here's what we hear from healthcare leaders every week — and the specific capability we bring to each one.
Epic, Cerner, and Meditech hold your most valuable clinical data, but extracting it for analytics requires months of manual HL7 mapping, and the output is rarely clean enough to act on.
We build HL7 FHIR pipelines directly into Epic, Cerner, and Meditech — normalizing, de-identifying, and routing clinical data into a governed lakehouse your analytics and AI systems can actually use.
Every analytics project triggers a compliance review. De-identification, BAAs, audit logging — valid requirements, but when they're not designed in from day one, they become blockers that add months and cost.
We design compliance into the architecture — not as a later review. Every engagement includes BAA coverage, de-identification protocols, RBAC, and audit logging as structural components, not add-ons.
You've invested in Power BI or Tableau. The dashboards are built. But clinical staff don't trust the numbers, don't know how to interpret them, or don't see how they connect to their daily decisions.
We close the gap between the tool and the person who uses it. Clinician-specific upskilling, executive data fluency workshops, and department-level literacy programs delivered through our NuScienta platform.
Rule-based claims review catches obvious anomalies, but modern billing fraud, telemedicine misuse, and upcoding patterns require ML to detect. Legacy rule engines miss the signals hiding in behavioral patterns.
We build and deploy machine learning models for healthcare-specific use cases — trained on your data, validated against your clinical standards, and integrated into the workflows where they create actual impact.
By the time population-level reports reach clinical leadership, the cohort has shifted. Patients have been discharged, readmitted, or transitioned. Interventions need real-time signals — not monthly batch reports.
We build population health dashboards that clinical leaders actually open — with real-time data feeds, HEDIS and CMS measure reporting, and chronic disease cohort management built into Power BI or Tableau.
Claim denials, undercoding, unbundling errors, and prior authorization delays cost health systems millions annually. Without analytics that can pinpoint where value is leaking — and why — these losses become accepted.
We combine BI analytics to identify leakage patterns with generative AI to automate the repetitive work — prior auth drafting, denial letter generation, coding validation — on IBM watsonx or Azure OpenAI.
Health systems don't have time for a 6-month search for a FHIR integration specialist or an ML engineer who understands clinical workflows. You need vetted, experienced talent that can start in weeks, not months.
We source vetted LATAM AI and data engineering talent with healthcare-specific experience — EHR integration, clinical ML, HIPAA-aware architecture — at 40–60% below US market rates, in US time zones.
What boutique means
in a healthcare engagement.
Big Four firms pitch senior partners and deliver with analysts. Specialist health IT vendors go deep in one layer and hand off the rest. We're structured differently — and it matters on HIPAA-regulated, clinically complex projects.
The people who scope your engagement are the ones who build it. No partner-led pitch followed by an analyst team you've never met. Every clinical data architecture decision gets senior eyes — because healthcare errors have clinical consequences.
Most consultancies hand you a roadmap and move on. Implementation goes to a separate team. We cover the full lifecycle — data strategy, EHR integration, AI model development, deployment, and training — as one team with continuous context.
Compliance isn't a sign-off step at the end of our engagements — it's a structural requirement built into every architecture decision from day one. BAA coverage, de-identification, RBAC, and audit logging are included, not invoiced separately.
As an IBM Silver Partner, we have hands-on depth with IBM watsonx and watsonx Health — not just a certification. Combined with Microsoft Azure Health Data Services and AWS HealthLake, we have the clinical AI toolstack most firms can only list on a slide.
Not ready for a full engagement?
Start with a defined pilot.
Every healthcare data transformation starts with understanding where you are. Our fixed-scope starter programs give you a clear picture — and a clear path forward — before any long-term commitment.
View All Starter ServicesWe audit your current data infrastructure, map your HIPAA compliance posture, identify your highest-ROI AI use cases, and deliver a prioritized roadmap — with realistic timelines and cost estimates.
See what's includedA free diagnostic that benchmarks your organization's data and AI literacy. Identify where clinical and operational staff are confident — and where the gaps are creating adoption friction.
Take the free assessmentA scoped proof-of-concept connecting your primary EHR to a governed analytics layer via FHIR. You leave with a working pipeline, validated data model, and a clear understanding of what a full implementation requires.
See what's includedA working prototype of one clinical AI use case — prior auth drafting, ambient documentation, or a clinical knowledge assistant — on IBM watsonx or Azure OpenAI, with HIPAA controls and a deployment plan.
See what's included
Let's talk about what your
clinical data could actually do.
Whether you're trying to integrate EHR data, deploy predictive models, close revenue cycle leakage, or build clinical AI your staff will actually use — we've done it. Let's find out where to start.
Healthcare data & AI consulting —
the questions we hear most.
From health system CIOs to payer analytics leads to pharma data heads — here are the questions that come up in every first conversation.
