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One source. Every vendor.

Data Pipelines moves eligibility, claims, and accumulators between your systems and your vendors, in production. A new relay starts from the rules of the ones you already run.

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The difference

Every vendor feed, before and after

Today

  • A separate map for every source and vendor
  • Each new vendor built from scratch
  • Business rules kept inside each feed
  • A failed run found downstream

With Data Pipelines

  • Each source mapped once, into a canonical type
  • A new vendor never touches the source
  • New relays start from the rules you already run
  • Exceptions surface for review, not downstream

01 · Map once

Map each source once.

Each source maps into a canonical data type: eligibility, medical and Rx claim lines, authorizations, accumulators, providers, or plan networks. Each vendor maps out of that layer. A new vendor never touches the source.

One eligibility feedMapped in once · out per vendor

Source · core admin

MBR_IDGRP_NOELIG_EFF_DTELIG_TERM_DTREL_CD

Canonical · Eligibility

member_idgroup_idcoverage_startcoverage_endrelationship

Vendor · Care navigation

Member IDEmployer groupStart dateEnd dateRelationship

The source map never changes when a vendor is added. Demo data.

  • Mapping and transform logic kept with each source and each destination
  • Any protocol and any schedule, on either side
  • One source can feed many vendors

02 · Draft from what runs

Add a vendor from the relays you run.

Pick the vendor and the data, and name the employer. The schedule and the rules come from the relay it copies. The draft runs in TEST before it moves to production.

From Demo Plan · 83 live relays · checked 1h agoDemo data

New integration

Vendor
Data
Employer name
Employer IDsThe relay sends these employers’ members and no others.
Rules from other relays
Relay name
[TEST] Demo Plan → Care navigation · Demo Logistics · Eligibility
File name
DemoLogistics_CareNav_Elig_%Y%m%d.txt
Copies
Demo Logistics
Relay
Named from the employer
File
Named from the employer
Runs
Monthly on the 10th, 13:00 UTC · 8 GB
Add the employer to continue.
  • Drop in a vendor’s spec, and ContextBase works out the map
  • ContextBase already knows where your data lives and how it joins
  • Every draft runs in TEST before production

Impact study

Client
Profile
Large third-party administrator
Scope
Data operations

More data work. No new hires.

A large third-party administrator

Scaling data operations without scaling headcount.

The administrator made its mappings, transformations, and checks reusable. Each new requirement now builds on work already done.

  • Routine processing runs on its own
  • Exceptions surface for review, not downstream
  • An existing workflow is refined from its saved logic
Read the impact study

Integrations

Read it where it sits, or move it

  1. 01

    Read in place

    Connectors let ContextBase read databases, warehouses, code, documents, and BI where they sit. Nothing is copied out.

  2. 02

    Move between systems

    Data Pipelines moves data between systems on any schedule, through the canonical layer.

  3. 03

    One connection for both

    Configure a connection once, and ContextBase and Data Pipelines both use it.

Use cases

Where teams start

  1. 01Eligibility to every vendor

    One eligibility feed, mapped once, then out to care navigation, wellness, and payment integrity vendors.

  2. 02Claims to a stop-loss carrier

    Medical claims and accumulators to a stop-loss carrier, on its schedule and in its layout.

  3. 03Accumulators to your PBM

    Accumulators to your pharmacy benefit manager, from the same canonical layer as every other feed.

  4. 04Claims to payment integrity

    Medical and Rx claims to a payment integrity vendor, from the same canonical claim lines.

  5. 05Prior authorizations

    Authorizations moved between payers, TPAs, and PBMs, in production.

  6. 06A new employer group

    Copy the relay you run for a similar employer. The relay and its file take the new employer’s name.

Questions

What teams ask before they start

Which protocols and schedules does it support?

Any protocol to any protocol, on any schedule. Each source and each destination keeps its own mapping and transform logic.

How is a new relay built?

From the rules of a relay you already run, or from the vendor’s spec. ContextBase works out the map from what it knows about your data. Each draft runs in TEST before production.

What happens when a run fails?

Each run is on the record. When one fails, your team sees the failed check, the records it affected, and the transformation behind it.

Is this the same as your connectors?

No. Connectors let ContextBase read data where it sits. Data Pipelines moves data between systems. Both use one connection, configured once. See the connectors

How long has it run in production?

For years. Data Pipelines is where Flume started, and it moves regulated payer data in production.

Start with one feed.

Bring the vendor feed that takes the most work. We map the source once, draft the relay with your team, and run it in TEST before production.

Request a demoContextBase for data and IT
  1. 01A call to pick the feed and the vendor
  2. 02We map the source into the canonical layer
  3. 03Your team checks the relay in TEST before it goes live

Know. Build. Learn. Repeat.

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