Ledgerly Ops
An operations dashboard that unifies orders, stock, and invoices so a growing distributor can stop reconciling spreadsheets.
- Next.js
- TypeScript
- PostgreSQL
AI & automation · Professional services
An AI intake flow that reads incoming documents, extracts key fields, and routes each case to the right reviewer for confirmation.
Concept project — no real client. Created to show how we would approach this kind of product.
Overview
Triage Assist imagines a firm that receives hundreds of documents a week by email. Staff currently open each one, retype the key details, and forward it to a colleague. The concept automates the reading and routing while keeping a person responsible for every decision.
The problem
Intake takes hours each day, urgent matters wait behind routine ones, and details are occasionally mistyped. The firm needs speed without handing judgement to a machine.
Our solution
Documents are classified and key fields extracted with a confidence score. High-confidence results are pre-filled for a quick check; low-confidence fields are highlighted for review. Every case is routed to a team queue based on type and urgency.
User journey
An email with attachments lands in the intake mailbox.
The system identifies the document type and extracts fields with confidence scores.
The case appears in the right team's queue, urgent items first.
A reviewer confirms or corrects highlighted fields in one screen.
Corrections feed the evaluation set used to measure accuracy over time.
Web
4 screens from the web application.
Screen 1 of 4: Intake queue sorted by urgency, with confidence per document.
Swipe, use the arrow keys, or pick a thumbnail. Screens are concept designs with demo data.
1. Web
Intake queue sorted by urgency, with confidence per document.
Intake queue listing documents with type, urgency, and confidence
2. Web
Reviewers confirm fields side by side with the document.
Review screen with document text on the left and extracted fields on the right
3. Web
Routing rules are readable and editable by the team.
Routing rules page listing conditions and destination teams
4. Web
Quality dashboard tracks accuracy against the evaluation set.
Quality dashboard showing volume and correction rate trends
Key features
Identifies document types agreed with the team during the pilot.
Pulls names, dates, references, and amounts into structured data.
Uncertain values are flagged instead of silently accepted.
Rules send each case to the right queue by type and urgency.
Side-by-side document and fields for fast confirmation.
Accuracy, volume, and correction rates tracked over time.
Approach
The review screen was designed around the reviewer's eye path: document on the left, fields on the right, uncertain values in amber, and keyboard shortcuts to confirm.
Python workers process a queue of documents, call language-model APIs with structured output, and store results with the prompt version used, so every result can be audited.
More work
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We'll start from your users and constraints, not a template. Tell us about your project and we'll reply with questions and next steps.