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Getting source data into a form the product can trust.

We are building the ingestion and recovery paths that turn external data into dependable product input.

In development
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The need

Know what arrived from each source and what still needs processing.

Our work

Build controlled intake, queued workers, source records, checkpoints, and replay.

Current stage

Core ingestion and recovery paths are being developed.

The problem behind the work.

AI and analytics features depend on data arriving from external systems. Those sources have different schedules and failure modes. We are building a data foundation that records what arrived, what was processed, and where attention is needed.

A successful connection is only the first step. If a provider is delayed, a job fails, or a time window is missed, the product needs a reliable way to identify and recover the gap.

The workflow at a glance.

01External source
02Ingress or schedule
03Queued worker
04Archive and checkpoint

Simplified view of the workflow.

What we are building.

Our work

We are building the integration layer: source setup, intake, background processing, raw archives, run state, and recovery paths.

Approach

Incoming requests create bounded work for queue-backed workers. The workers retain the original input and record progress through manifests and checkpoints. Replay and schedule reconciliation give the team a defined route back to missing data.

Why we took this approach.

01

Separate intake from processing

Fast entrypoints accept work; workers handle provider calls and persistence without holding an incoming request open.

02

Keep the original input

Raw archives help us trace the source of a record and revisit downstream processing when needed.

03

Make gaps recoverable

Checkpoints and replay identify a particular missing window and provide a path to process it again.

Where the work stands.

The data foundation is in development. Its design is aimed at reliable intake and recovery across external sources.

What are you building?