Your next launch shouldn't wait six weeks for an answer. Axiomera harmonizes your claims, distributor, specialty pharmacy, CRM, and rebate data into one governed layer inside your own environment. So commercial analytics ships in days, and your data science team signs off on how.
Claims, distributor 867/852 EDI, specialty pharmacy status feeds, CRM, payer/rebate data. Same NDC, five identities.
867: NDC 00XX-XXXX-XX? SP: status "PA pend"
Every feed restructured to one canonical shape, whatever dialect it arrives in. X12 EDI, NCPDP, flat files.
X12 · NCPDP · FHIR
Product, prescriber, account, and plan identities resolved to one master. NDC, NPI, plan hierarchy. Each mapping confidence-scored.
NDC ✓ NPI ✓ conf 0.94
One longitudinal launch record: this brand, by account and payer, from ship to script to rebate. Lineage on every number.
one brand, one truth
True demand vs. channel inventory. Coverage vs. pull-through. GtN your CFO can defend. Refreshed continuously, in days not quarters.
dashboard, not data project
These are the numbers your CFO's staff will check. So we cite them exactly.
Read the left edge and the right edge of the curve above: silos in, launch picture out, in days. The middle is your data team's problem. And we make them sign-off partners, not blockers.
Container in your environment, nothing leaves your walls. Every field scored, lineaged, and human-approved. Delivery into Snowflake or Databricks. It slots into your stack, it doesn't replace it.
Real-time transparency and automatic matching of manufacturer-payer rebates. An accrual position you can defend line-by-line, traceable to contract clauses and claims.
Identify fraud, waste, and abuse patterns across pharmacy networks. On data where one patient, one pharmacy, and one claim mean one thing.
Trustworthy brand, sales, and market-access analytics with predictive capability. Analysis cycles in days instead of six-week rebuilds.
Convert support-program exhaust into structured, provenance-carrying evidence your medical affairs team can actually use.
Train algorithms on harmonized historical pathology data to identify cancer mutations. With lineage back to every source slide and report.
Resolve inconclusive classifier results in the methylation gray zone with consistent, terminology-bound training data.
AI overlay foundations for camera-based and laparoscopic devices, built on harmonized procedure and outcomes data.
Harmonized, provenance-carrying datasets for label expansion and safety monitoring. See the Life Sciences page →
Bring one brand and one question. We'll walk your commercial and data teams through the architecture on your stack.