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One platform vs. a stitched-together stack.

Most labs run a LIS here, a reporting tool there, and billing somewhere else. Here's how an all-in-one AI platform compares to that disconnected stack — and to a legacy LIS — across what matters to a genetic or molecular lab.

CapabilityDisconnected stackLegacy LISLabrynix
LIS / LIMS + reporting + billingSeparate tools, re-keyed dataCore LIS; reporting & billing bolted onOne connected platform — captured once, flows everywhere
Custom specialty reportsManual assembly or fixed templatesRigid, one-size templatesAI-built per case (PGx, oncology, neurology…), lab-signed-out
Built-in AI for the labNoneBolt-on chatbot, if anyOur own lab-trained AI — SOPs, workflows, draft reports
Billing / RCM on the same recordSeparate biller → more denialsSeparate billing moduleEligibility, coding & claims off the order + report record
IntegrationsCustom build per toolVendor services projectHL7 v2 / FHIR R4, instruments, REST & FHIR API
Audit trail across the workflowFragmented across toolsPartialEnd-to-end, timestamped, inspection-ready
ImplementationMultiple vendors to coordinateLong, rigid roll-outScoped to your lab, parallel workstreams, migration plan
ExperienceVaries tool to toolOften datedModern, branded, built for genetic & molecular labs

Comparison reflects common patterns in the category, not any specific vendor. The most useful test is your own: ask any vendor to run one of your real cases — order to report to claim — end to end.

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