TraceLodger

Objective

Create a forensic review application that can ingest heterogeneous artifacts, normalize evidence, and reconstruct trustworthy activity timelines with evidence integrity as the primary requirement.

Tech Stack

• Rust + Tauri
• React
• Python sidecar
• Next.js control plane
• WorkOS
• Stripe
• Neon Postgres

Tooling Used

• Desktop security boundary: Tauri + Rust for low-overhead local runtime and stronger native control surface.
• Parsing/ingestion specialization: Python sidecar for flexible artifact normalization and parser iteration speed.
• Control plane services: WorkOS for identity boundaries, Stripe for entitlement gating, Neon Postgres for managed persistence.
• Why this stack: Separates forensic-safe local handling from account/billing concerns while preserving evidence-integrity behavior during degraded auth/network states.

Architecture

TraceLodger splits responsibilities across local and remote layers.
The desktop app (Tauri/Rust + React) handles local evidence handling and operator workflow.
A Python sidecar performs parsing and ingestion transforms for structured analysis pipelines.

The web control plane (Next.js) manages identity, billing, and entitlement boundaries through WorkOS and Stripe-backed gating.
Critical behavior: when authorization or network state degrades, the system falls back to read-only handling so evidence cannot be modified.

Execution

I prioritized integrity guarantees before UI polish.
That meant explicitly designing failure behavior so operators could continue reviewing data safely without write-side side effects.

I then implemented ingestion and parsing boundaries to separate raw evidence handling from transformed analysis views.
This made the pipeline easier to audit and reduced the chance of silent state corruption across parsing failures.

Results

TraceLodger demonstrates a security-first forensic architecture where availability issues do not compromise chain-of-custody style guarantees.
The project sharpened my approach to graceful degradation, entitlement-aware workflows, and forensic-safe data handling.

Next improvements: richer timeline visualization, stronger parser plugin model, and expanded artifact type coverage.