The
Overview
A logistics company was drowning in manual invoice processing. They knew AI could help, but had no internal machine learning team to build it. Wishtree stepped in to deliver a secure, production-ready MVP in just 10 weeks.
Problem
Statement
The client’s invoice processing was a bottleneck, threatening to slow their entire operation. With no internal ML team and growing invoice volumes, manual data entry was no longer sustainable.
Highlights
OCR + NLP-powered automation
Secure API architecture
Cloud-native scalability
No internal ML team required
MVP delivered in 10 weeks
Syllable AI voice integration for exception handling
Agentic AI refers to autonomous, goal-driven software agents that act with
limited human input to optimize specific goals like pricing, forecast demand,
and detect fraud in real time.
About Client
A logistics company that processes hundreds of vendor invoices daily across multiple locations. Invoice formats varied wildly, data extraction was manual and error-prone, and the team had no in-house machine learning expertise to build an AI solution.
- The company had no internal machine learning team to build an AI solution.
- Staff spent hours daily manually entering invoice data.
- Vendors submitted invoices in dozens of formats, each requiring different handling.
- As the business grew, manual processing could not keep up without hiring more data entry staff.
- Invoice data contained sensitive financial information requiring strict protection that manual processes could not guarantee.
- Implemented Amazon Textract to extract text from scanned invoices, PDFs, and images, handling even low-quality scans with higher accuracy than open-source alternatives .
- Trained custom Amazon Comprehend classification models to identify and extract key fields, across dozens of formats.
- Built a confidence scoring layer using Amazon SageMaker that flags uncertain predictions for human review.
- Designed a protected API layer with AWS API Gateway, AWS Lambda for serverless processing, AWS KMS for encryption, and AWS WAF for threat protection.
- Integrated Syllable AI’s agentic platform to handle edge cases.
- Architected on AWS with auto-scaling ECS containers, Amazon RDS for metadata, and Amazon S3 for secure document storage.
- The Amazon Textract OCR engine converts scanned and image-based invoices into machine-readable text, even when the quality is poor.
- Amazon Comprehend NLP models understand invoice context to distinguish between vendor names, ship-to addresses, line items, and totals across dozens of formats.
- Amazon SageMaker Confidence scoring automatically routes uncertain predictions to human reviewers.
- The system learns from human corrections over time.
- Syllable AI voice agents handle vendor calls for missing information, reducing AP staff phone time by 80% .
- All interactions are logged in AWS CloudTrail for complete audit trails and compliance.
Core Features
AWS textract OCR engine
AWS comprehend NLP models
Amazon SageMaker confidence scoring
Secure AWS API layer
AWS CloudTrail audit logging
Impact
- 93% precision on real invoice data during MVP testing
- ~70% of manual operations automated in production rollout
- Hundreds of staff hours saved weekly on manual data entry
- Error rates dropped significantly vs. manual processing
- Board-level approval secured immediately after MVP demo
- Scalable AWS foundation ready for future AI initiatives
- AP staff phone time reduced by 80% with Syllable AI voice exception handling
- Complete audit trails for every invoice and interaction via AWS CloudTrail
- No internal ML team required
Why Wishtree
Wishtree bridges the gap between AI ambition and delivery.
For this logistics client, we:
- Delivered a working MVP in 10 weeks despite no internal AI expertise
- Leveraged AWS Textract and Comprehend for best-in-class document AI
- Integrated Syllable AI voice agents to automate vendor exception handling
- Built with security and scalability from day one
- Proved value fast with 93% precision on real data
- Secured board-level buy-in for full production development