Throughline
One connector interface, every system in sync.
A multi-connector ETL platform that syncs QuickBooks, Copper, Basecamp, and PandaDoc into PostgreSQL and Airtable behind one shared connector interface.
- language
- Python 3.11, Next.js dashboard
- connectors
- 5 (QuickBooks, Copper, Basecamp, PandaDoc, Airtable)
- interface
- 4-method connector ABC + plugin registry
- ownership
- Sole engineer — design, build, and production operation
- in production
- A PR + podcast company; ~6 months and counting
- history
- built Oct 2025 to Mar 2026; in production since
- repo
- Private production repo; public extract: throughline-connector-kit
The problem
A PR and podcast company ran on four disconnected SaaS tools with no unified view of a client, and every new integration historically cost roughly 2,000 lines of bespoke code. Throughline put all four behind one interface feeding a single source of truth.
Who this serves
Finance and ops staff at a PR and podcast business — client-facing teams answer billing and delivery questions faster with one contact layer instead of contradictory records across systems.
How it is built
-
One interface, every integration for free
Every connector implements the same four methods and registers through a decorator. A new source inherits incremental sync, retry queues, health checks, and dual-destination writes without touching the sync engine. The connector entry point is about 50 lines; the full package (API client, transformer, schema) runs 700 to 2,200.
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Incremental sync with per-connector checkpoints
A sync-metadata table records the last successful timestamp per connector and entity type, so each run fetches only what changed. The architecture docs report a 53 to 97% reduction in API calls in production; the public basis is the documented daily-operations comparison of 3,395 incremental calls against an 11,548-call full-refresh baseline.
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A Postgres MDM layer for contact identity
A master_contacts table uses generated columns to normalize email, phone, and name, and cross-links QuickBooks, Copper, Basecamp, and PandaDoc identifiers into a single row, so a contact is one entity instead of four disconnected records.
By the numbers
self-reported marks figures stated in docs or commit history that the source brief could not reproduce from the repository alone. Everything else is traceable to code.
- 53 to 97%: Basis: Throughline architecture docs compare observed incremental runs with a full-refresh baseline; the public example is 3,395 daily API operations versus 11,548 baseline operations, a 69% reduction. No raw production export is published.
Where this honestly stands
Shipped to production. The connector platform is solid; the efficiency gains are documented from production operations but not exported as a reproducible public log. Coverage is below target, so the strongest proof is interface-level tests plus documented CI and operations evidence rather than a blanket coverage claim.
Production repo stays private. The sanitized public extract shows the four-method connector protocol, in-memory sync engine, and tests — no credentials, vendor schemas, or client data.
Want the parts that are not in a public repo? I will walk you through the architecture and the decisions on a call.