Timbertix

The Problem

In the timber industry, every load of wood crossing a scale produces a paper ticket. These tickets — recording driver, tonnage, wood type, landowner, and mill — are the foundation of landowner payout calculations.

Processing them was manual, tedious, and error-prone: tickets arrived crumpled paper, each mill used different formats, naming inconsistencies abounded, payout reports required hours of spreadsheet work per week. Errors compounded silently until payout discrepancies surfaced weeks later.

The Solution

Timbertix digitizes the entire timber ticket workflow. Workers snap a photo of a ticket in the field. An AI extraction pipeline reads it, identifies the mill, and maps the data to a standardized schema. A human reviews every record before it reaches a report. Landowner payout reports generate on demand with per-ticket line items and rate calculations.

Architecture

Key capabilities

• AI-native extraction — GPT-4o vision reads ticket images, understanding mill-specific layouts and field positions
• Review-first workflow — no record reaches a report without human approval. Automation assists; humans decide.
• Self-improving matching — every reviewer correction can become a new alias, teaching the system to handle new abbreviations automatically
• Multi-format ingestion — phone photos, PDFs, and spreadsheets all flow through the same pipeline
• Mobile PWA — installable to the home screen, works on any smartphone, no app store required
• Complete audit trail — every step from raw image to final payout is traceable
• Analytics from day one — with every ticket now structured in a database, data that was previously trapped on paper becomes queryable. Tonnage trends by landowner, mill throughput over time, seasonal patterns, rate comparisons — insights that were impossible when the data lived in shoeboxes and spreadsheets

The Outcome

A production SaaS platform serving a real forestry operation. Hours of weekly data entry replaced with a snap-and-review workflow. Payout reports that took a day now generate in seconds. And for the first time, the operation has a structured, queryable history of every load — turning years of paper records into actionable business intelligence.

The Systems Engineering Challenge

The hard part wasn't building a web app or calling an AI API. It was understanding how timber tickets flow through a forestry operation — how mills name things differently, how landowners appear under multiple aliases, how rates vary by wood type and tract — and designing an extraction pipeline that handles all of it reliably.

The domain knowledge shaped everything: the database schema, the matching logic, the review workflow, and the prompts that tell the AI what to look for.