PinSight is an AI-driven audit execution platform designed for SME audit teams, addressing the inefficiencies of traditional labor-intensive auditing.
As enterprise data complexity grows, manual vouching and rule-based sampling become unreliable and error-prone. Meanwhile, most SME audit teams lack access to expensive enterprise systems, creating a critical “digital gap”.
PinSight bridges this gap by automating the full audit workflow — from risk-based sampling and document understanding to logic validation and working paper generation — through a modular, explainable AI system.
The system emphasizes:
- Precision over random sampling
- Structured understanding of unstructured audit evidence
- Human-in-the-loop control for reliability and compliance
This project was delivered as a working product prototype with a functional UI and end-to-end workflow.
Key Innovations
Risk-oriented smart sampling using hybrid ML models (XGBoost + Isolation Forest) to detect anomalies beyond traditional methods.
OCR-free document understanding via Donut (Document Transformer) for direct image-to-JSON extraction.
Triple-check validation engine covering consistency, logic, and cross-document correlation.
Automated audit narrative generation using LLMs with traceable reasoning.
Import: Upload ledgers or datasetsSample: AI identifies high-risk entriesRecognize: Extract structured data from vouchersValidate: Run business logic checksGenerate: Produce audit workpapers
What I Did
Led product development as the sole technologist, translating audit workflows into an AI-driven system.
Designed the full architecture: ingestion, sampling, document understanding, validation, and reporting.
Built a working web prototype with interactive UI for anomaly inspection and report generation.
Developed prompt workflows for AI-assisted audit reasoning and narrative generation.
Prioritised explainability and auditor control in system design.
Delivered MVP under time constraints focusing on usability and reliability.
Reflection
This project reinforced a key insight: in audit systems, reliability and trust matter more than raw model performance.
Instead of building a black-box AI, I designed a system where auditors can verify, control, and override every step.
The modular architecture ensures flexibility and real-world deployability, especially for SME audit environments.
Being the sole technical contributor also strengthened my ability to translate domain knowledge into executable product systems.