Software for healthcare
that holds up in audit.
EHR-adjacent platforms, prior-authorization automation, clinical and operational data unification, and HIPAA-aware AI for providers, payers, and life-sciences operators. PHI-aware by default, audit-recorded where it matters.
What healthcare software looks like in practice.
Healthcare runs on a handful of large clinical and claims systems, decades of accumulated integrations between them, and a long tail of point tools that filled gaps the big systems never closed. The data is rich but fragmented across patient, encounter, claim, and authorization records, and the documents that drive day-to-day work still arrive as faxes, PDFs, and portal uploads.
Most operators don't need a replacement core system. They need the work that surrounds the core to get faster, more accurate, and more auditable: the prior-auth queue that takes too long, the referral intake that drops cases, the clinical data that sits trapped in flowsheets, the operational reporting that gets stitched together by hand every Monday morning. AI helps with a real portion of this, but only when it's built into a system that handles PHI correctly and records what it does.
Apollo builds the platforms, data work, and AI applications that sit alongside the core systems. HIPAA-aware by default, audit-recorded where it matters, and integrated with the EHR, payer systems, and clinical data sources you already run.
HIPAA defines the floor, not the ceiling. HITRUST CSF or SOC 2 is often the contractual requirement. For software classified as a medical device, FDA 21 CFR Part 11 and quality-system regulation apply. Audit-readiness has to be designed in, not bolted on later.
The integration between the EHR and the payer portal that someone wrote in 2014, that breaks every time either side ships an update, and that nobody on either team is comfortable touching. The faxed-document intake handled by a temp queue. The clinical data export that lives in a spreadsheet.
Document extraction from clinical notes, faxes, and referral packets. Prior-auth triage and supporting-documentation assembly. Clinical-summarization assistants for back-office work. Patient-message triage with explicit escalation. AI is useful where the work is high-volume, unstructured, and reviewable.
Four engagement patterns in healthcare.
Most healthcare and life-sciences engagements land in one of these shapes. Each gets built PHI-aware, audit-recorded, and integrated with the EHR, payer, or clinical-data systems you already run.
Prior-Auth & Referral Automation
Document intake from faxes, portals, and EHR-generated referral packets. AI-assisted extraction of clinical evidence and policy-matching support. Reviewer UIs that surface the supporting documentation alongside the decision, with audit trail on every approval and denial path.
EHR-Adjacent Platforms
The applications that sit beside Epic, Cerner, Meditech, or Athena and do the work the EHR doesn't: care-coordination tooling, patient outreach platforms, specialty-specific workflows, and operational dashboards. FHIR-based where the integration supports it, custom where it doesn't.
Clinical & Operational Data Unification
Bringing clinical, claims, scheduling, and operational data into a query-able layer that powers reporting, analytics, and downstream AI. Patient identity resolution, vocabulary mapping (SNOMED, LOINC, RxNorm), and the audit fields needed for compliance and quality-program reporting.
Faxed-Document Intake at Scale
Faxes, scans, and emailed PDFs landing in queue, classified, extracted, and routed into the right downstream system. Confidence scoring drives which records flow through and which land in a reviewer queue. The reviewer queue is part of the design, not a workaround.
A healthcare platform, end to end.
Simplified, but representative of how we lay out work alongside an existing healthcare stack. The Apollo platform layer is the centerpiece. Source systems feed into it through FHIR, HL7, and document pipelines; applications and AI workflows downstream all read from the same governed foundation.
Compliance, designed in.
The frameworks that shape what we build, and what we ship.
Healthcare software has a long list of compliance regimes that overlap and sometimes conflict. The right ones for any given engagement depend on what the software does, who pays for it, who uses it, and what data it touches.
Apollo treats these as design inputs, not as a checklist applied at the end. Access controls, audit trails, encryption boundaries, and consent handling get specified before the first line of code and reviewed against the relevant frameworks at every iteration.
The panel on the right is the working set we encounter most often. Any given project picks a subset, and the proposal will be explicit about which ones apply and what we will and won't certify directly.
Federal & statutory
Industry frameworks & certifications
Interoperability standards
Four phases. Built around the regulatory reality.
Apollo's standard methodology, applied to healthcare. Compliance posture, PHI handling, and integration design get specified before the build starts, so audit-readiness is a property of the system rather than a phase at the end.
Map the work. Map the regulations.
The current process, the systems involved, the documents and events that drive it, and the volumes. The compliance frameworks in scope and the access-control posture they imply. Where AI helps, where rules suffice, and where a clinical reviewer still has to look.
Architecture, PHI plan, audit plan.
System architecture and integration design against the EHR and payer landscape. PHI handling, encryption boundaries, and consent. Audit trail design that satisfies HITRUST or SOC 2 evidence requests. Reviewer UI wireframes where humans stay in the loop.
Platform. Integrations. Review UIs.
Platform deployed inside the security boundary, FHIR and HL7 integrations connected, AI extraction wired up with confidence thresholds, reviewer UIs functional. Two-week iterations. Each shipped capability arrives with its tests, its monitoring, and its audit fields.
Measure. Tune. Expand.
Throughput, error rates, and AI accuracy in production dashboards. Threshold tuning based on what the data shows. Gradual rollout to adjacent specialties or service lines once the first workflow is steady. Knowledge transfer to your team along the way.
Tell us what you're trying to build.
Send a paragraph about the project: the systems involved, the volumes if you have them, the compliance frameworks in scope, and the parts that aren't working today. We'll reply within one business day, either with a 30-minute call or with an honest "this is not the right fit; here's who you should call instead."