Automated billing matching, receipt extraction, and real-time ledger reconciliation.
Duration
3 Months
Team Size
3 Developers
Launched
2025 Launch
Status
Reference Solution
An executive deep dive tracing operational objectives, challenge landscapes, and delivery outcomes.
LedgerSync AI is a secure intelligent document processing platform designed for accounting firms to ingest client receipts and invoices, parse line-item details, and automatically reconcile transaction records against banking ledgers.
Bookkeepers spent hours downloading email attachments, manually typing billing values, and resolving ledger disputes, causing month-end reporting backlogs.
Automate receipt parsing and banking reconciliation, targeting a 95% automatic matching rate and reducing month-end bookkeeping turnaround times from 5 days to 1 day.
We engineered a secure document ingestion API utilizing GPT-4o Vision models to translate unstructured receipt files into organized JSON arrays, integrated with ledger-matching logic and a human-in-the-loop validation queue.
We implemented GPT-4o Vision endpoints coupled with a fuzzy matching reconciliation engine. A Bookkeeper Review Portal was introduced, utilizing low-latency webhooks to ensure staff validation takes less than 10 seconds.
The firm automated 95% of incoming document matches, eliminated transcription errors, and reduced average monthly closing cycles from 5 days to 1 day.
Custom engineered software nodes mapping specific operational problems to engineering resolutions.
Uses AI vision models to extract line-item detail and tax structures from low-resolution scans.
Applies confidence metrics to matches, routing anomalies to staff before ledger commit.
Compares transaction dates and totals with bank feed logs to perform automatic syncs.
Data pipeline flow maps showing inputs translating down through vector stores and execution API layers.
Handles secure document uploads and manages extraction queues.
Parses documents semantically to extract receipt values in structured format.
A Next.js dashboard that lets staff review, correct, and manually confirm low-confidence document fields.
Measurable performance metrics and operational throughput scaling indicators registered post-launch.
The firm automated 95% of incoming document matches, eliminated transcription errors, and reduced average monthly closing cycles from 5 days to 1 day.
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