TraceDoc AI

Inspiration

Modern businesses process thousands of unstructured documents every month—ranging from invoices and procurement forms to complex legal contracts. However, existing document-processing workflows still have gaps between information extraction and actionable, verifiable auditing, making it difficult to identify and validate critical financial and legal discrepancies.

When organizations miss key auto-renewal deadlines or fail to catch billing discrepancies, the financial and regulatory impact can be significant. We built TraceDoc AI to transform document parsing from simple optical character recognition into an intelligent, end-to-end audit engine that offers visual traceability and automated risk detection.

What it does

TraceDoc AI is an intelligent business document audit platform that converts static PDFs and scanned documents into interactive, actionable insight dashboards.

Key capabilities include:

Visual Source Traceability: Every extracted data point (deadlines, amounts, party details) links directly back to its source location using visual bounding box overlays on the original PDF.
Automated Anomaly & Discrepancy Detection: Cross-checks mathematical values (e.g., line-item totals, tax subtotals vs. final totals) and flags missing compliance fields, unverified clauses, or potential cost discrepancies requiring human review.
Obligation & Deadline Extraction: Automatically parses contract terms, auto-renewal windows, and payment due dates, converting them into actionable reminders and structured calendar events.
Missing Data Audit: Identifies mandatory regulatory or workflow fields that are missing prior to final approval.

How we built it

We built TraceDoc AI using a modular pipeline architecture designed for scale and precision:

Ingestion & Layout Extraction: Documents are parsed using PyPDF2 and PDFPlumber to extract full-text content alongside spatial bounding-box coordinates (x, y, width, height) for every character and structural block.
AI Analysis Engine: Extracted text and layout structure are fed into Vision LLM pipelines configured with strict JSON schemas to capture key fields, deadlines, and potential discrepancies.
Validation & Verification Logic: Programmatic scripts execute math and logic checks on structured outputs (e.g., verifying Subtotal + Tax = Total) to identify financial or structural anomalies.
Interactive Audit Dashboard: A split-screen frontend links extracted metadata cards directly to visual highlights on the rendered document canvas, allowing one-click verification of source evidence.

Challenges we ran into

Coordinate Mapping Across Pages: Accurately mapping LLM-identified entities back to the exact bounding box coordinates across multi-page PDFs was challenging due to varied document layouts, column structures, and font scaling.
Eliminating AI Hallucinations: Large language models can occasionally infer dates or amounts that are not explicitly stated. We addressed this by enforcing structured JSON outputs combined with spatial verification checks.
Handling Low-Quality Scans: Scanned invoices often suffer from noise and skewing, requiring custom preprocessing and OCR cleanup before extraction.

Accomplishments that we're proud of

Visual Source Traceability: Built a seamless split-screen interface where clicking any extracted datapoint or detected finding instantly jumps and highlights its source location on the PDF page.
End-to-End Automated Auditing: Successfully integrated line-item math verification and clause-related checks into a single unified dashboard.
User Experience: Designed an intuitive interface that streamlines document review workflows for legal and financial teams.

What we learned

Structuring unstructured documents requires combining probabilistic LLM capabilities with deterministic programmatic validation loops.
Providing immediate visual proof builds user trust in automated AI auditing tools far faster than standalone text summaries.
Designing multi-modal data pipelines requires careful consideration of layout analysis, schema enforcement, and rendering performance.

What's next for TraceDoc AI

Batch & Bulk Processing: Expanding the platform to allow enterprise teams to upload and cross-audit hundreds of documents concurrently.
ERP & Software Integrations: Adding native webhooks and integrations with platforms such as QuickBooks, SAP, and Salesforce to streamline accounting workflows.
Custom Enterprise Rule Engines: Enabling legal and procurement teams to define custom compliance checks and risk-scoring rules tailored to their operational policies.\

What it does