App Builder
Consumer software shaped by workflow reality.
I build AI-assisted apps for personal records, receipts, and evidence. The work combines product judgment, secure cloud backends, extraction quality, and supportable user workflows.
Featured product
ExpenseJournal
MVP / QA
ExpenseJournal is a personal and household receipt evidence assistant for receipt capture, OCR/AI extraction, uncertain-field review, line-item and semantic search, and confirmed evidence reports.
Camera, upload, or forwarding for receipts and evidence.
OCR and AI-assisted merchant, total, tax, and line-item extraction.
Focus the user on uncertain fields before records become trusted.
Find records by item, vendor, amount, date, category, or meaning.
Create confirmed evidence reports for taxes, warranty, reimbursements, or household review.
Product media
ExpenseJournal product screens
Current product screens showing the receipt capture, review, search, reporting, export, and household-sharing workflow.
Screens are product/marketing previews from the current ExpenseJournal build.
Capture receipts
Upload, scan, or record receipt evidence without building one-off spreadsheets.
Review uncertain fields
Keep AI-assisted extraction accountable with human confirmation where confidence or evidence matters.
Search by item or meaning
Find receipts by item, vendor, brand, date, category, or semantic intent.
Ask AI about receipts
Ask questions over your own confirmed receipt evidence instead of guessing from memory.
Export trusted evidence
Create clear reports from confirmed records for practical household and personal use.
Share with your household
Coordinate receipts, reports, and insights with household members.
Capabilities
What ExpenseJournal is built to handle.
Receipt capture
Mobile-friendly capture and upload paths for everyday purchasing records.
OCR/AI extraction
Structured extraction with human confirmation before evidence becomes trusted.
Line-item search
Search by product, vendor, category, date, amount, and related meaning.
Confirmed reports
Evidence packages for warranties, reimbursements, tax preparation, and household review.
Household sharing
Shared records and practical ownership models for more than one user.
Operational telemetry
Quality signals for extraction failures, review patterns, and supportable production behavior.
Next direction
Personal health organizer
In development
A privacy-focused direction for organizing user-provided routines, symptoms, habits, reminders, documents, and health notes so personal information is easier to find and summarize.
Not intended for diagnosis, treatment, or medical advice.
Reusable foundation
Shared cloud and AI platform
- Identity and privacy-aware access patterns
- Document/image processing and OCR
- Extraction, semantic search, and secure storage
- Telemetry, quality signals, and support workflows
- Usage controls and subscription-aware limits
Applied AI patterns
Applied AI patterns behind the product.
The product work is intentionally grounded in user-visible evidence, review, and support paths instead of vague automation claims.
Extract with confidence
OCR/AI extraction, extraction confidence, uncertain-field review, and human-in-the-loop confirmation before records become trusted.
Retrieve with evidence
Semantic search, retrieval, evidence-grounded summaries, line-item references, and confirmed reports that users can inspect.
Operate with signals
Telemetry and quality signals for extraction failure, review loops, support paths, cost/latency awareness, and fallback behavior.
Respect the boundary
Privacy boundaries, usage controls, secure storage, practical ownership, and supportable workflows for personal records.
What this demonstrates
Product work with architecture discipline.
Product judgment
Choosing workflows where AI helps because the evidence and review path are concrete.
Architecture discipline
Designing storage, identity, telemetry, and ownership before the product surface grows.
AI workflow design
Keeping extraction, confidence, review, and search grounded in user-visible records.
Production readiness
Building for support, auditability, rollback paths, and measurable quality signals.