CodeixLab built an internal business automation dashboard that ingests documents, extracts structured data, routes work and keeps people in control wherever judgment is required. AI does the repetitive reading and drafting; the application decides what is valid and what needs a human.
Project overview
- Industry: Business operations
- Services: AI automation and integrations, custom software development, web application development
- Platforms: Internal web dashboard with background processing services
- Tech stack: Python, Ruby on Rails, Next.js, PostgreSQL, Claude and OpenAI models
The challenge
Many teams still move information through inboxes, spreadsheets, PDFs and repeated copy-paste work. The cost is not only time: manual re-keying introduces errors, and nobody can say later where a number in the system came from.
AI can help, but only if its output becomes structured, reviewable and connected to the system of record. A model that confidently fills in the wrong invoice total is worse than no automation at all.
Our approach
Separate extraction from approval
LLMs summarise, classify and propose field values. They never write directly to operational records. The application validates formats, cross-checks values against existing data and highlights low-confidence fields for review.
Human-in-the-loop where it counts
High-risk or ambiguous fields go to a reviewer with the source document side by side. The reviewer’s decision is recorded before anything updates the business system, so accountability is clear.
Routing based on validated state
Work is routed by the validated record, not by raw model output. Exceptions go to the right queue; clean records move straight through.
Model choice as a configuration
The pipeline supports both Claude and OpenAI models behind one interface, so the team can choose the best model per task and switch without rewriting the workflow.
Key features
- Document intake from uploads and email
- Structured extraction with confidence highlighting
- Side-by-side review screens for exceptions
- Rules-based validation and routing
- Audit trails showing where each value came from and who approved it
- Integration with existing business systems through APIs
Engineering decisions that mattered
- AI proposes, code disposes: business rules live in application code, not prompts.
- Honest about errors: the system is designed on the assumption that AI will sometimes be wrong.
- Traceability: every automated value can be traced to a source document and a decision.
What this means for your project
If your team spends hours a week moving data out of documents and inboxes, this pattern is usually the fastest return on an AI project. Read where AI automation actually saves time, explore our AI automation services, or book a free consultation.
Outcome
Document processing with structured extraction, human-in-the-loop review and audit trails, connected safely to existing business systems.