Document Pipeline Orchestrator

A browser-based platform for running document-processing pipelines that replaced two legacy desktop applications with one interface, showing live progress instead of blocking dialogs.

34,275lines of code
91files
55commits
17HTTP endpoints
12data models

What the measurements mean

Only what is unusual for this project. Every sentence names the field it came from; nothing here is asserted that cannot be checked against the data.

How it works

Two desktop tools doing neighbouring jobs meant two installations, two update paths and two sets of habits. Bringing them together in one web interface removed all of that. The larger gain is operational. Each pipeline starts as a tracked run with live progress, so a long document job can be watched, cancelled and inspected instead of waiting behind a frozen window. Statistics are recorded for each stage (CSV, PDF and cross-check counts), so a bad run can be diagnosed from its record rather than by running it again. The compression and clustering stages are exposed as steps that can be configured.

What kind of engineering this is

Six signals counted from the source: files that run models locally, that act without being asked each time, that check their own output, that survive failure, that touch parsers and protocols, and that were measured then improved. Every axis carries its file count.

Depth profileDepth signals: Local inference 0 files, Autonomy 0 files, Verification 14 files, Resilience 90 files, Low level 24 files, Iteration 0 filesLocal inference0 filesAutonomy0 filesVerification14 filesResilience90 filesLow level24 filesIteration0 files

What it is written in

Share of code bytes. Vendored bundles and generated output are set aside before this is computed, which is why it differs from a simple file count.

Language mixPython 90.3%, JavaScript 5.9%, CSS 2.7%, Batchfile 0.6%, HTML 0.4%, Shell 0.1%Python 90.3%

What stands out

Capabilities

Grade

Every check names the field it measured. Checks that cannot apply are excluded, never counted as failures.

B
82.4% over 18 of 18 checks100% coverage

Not met

Architecture

Directories carrying real modules, largest first.

HTTP surface

17 endpoints in total.

Data model

Typed structures and tables found in the source.

Vocabulary

Parsed 74 of 74 Python file(s). Counts are exact; the names below are a sample.

478functions
592methods
126classes
59documented modules

Classes

Functions

Command line

Depth

Signals counted from the code, not asserted.

55 commits across 7 separate working days over 1.1 months, 27 of them revising existing behaviour

Built from scratch

Proves itself

Survives failure

Systems

Distributed systems

Working within limits

Environment engineering

GPU & memory management

Languages

Built with

Recurring themes in the commit history

Evidence

Taken from the code itself: a module docstring or the opening of the README.

FastAPI application entry point. Run with: uvicorn unified_orchestrator.server:app --reload --port 8000 # or python -m unified_orchestrator.server

At a glance

Status
paused
Primary language
Python
First commit
2026-03-09
Last activity
2026-04-10
Domain
Desktop applications
Agent docs
CLAUDE.md, AGENTS.md