How AI Automated ACH Transaction Creation for a Fintech Payments Platform
After engineering and launching a production-grade ACH payments platform from the ground up, Step 7 Consulting later extended the platform with AI-driven automation to eliminate one of its most time-consuming workflows: manually creating ACH transactions from PDF documents.
This work builds directly on the production-grade ACH payments platform Step 7 Consulting previously architected and delivered, including secure bank connectivity, payment orchestration, approval workflows, and full auditability.
When the platform was originally designed, technologies such as large language models and OCR services were still in their early stages and not yet capable of supporting reliable, production-grade automation. In addition, the real-estate fintech industry relies heavily on PDFs as the standard artifact for documenting and tracking transactions. As these technologies matured, Step 7 implemented an AI-powered workflow directly into the SaaS platform it had originally built.
Outcome: ACH transaction creation time was reduced from approximately 5 minutes per transaction with frequent errors to 15 seconds with zero errors, dramatically improving operational efficiency and reliability.
Client Background
The client is a fintech startup operating a real-estate-focused ACH payments SaaS platform engineered and built by Step 7 Consulting from initial concept through production deployment.
At launch, the platform delivered secure bank connectivity, payment orchestration, approval workflows, and full auditability across complex real-estate transactions. However, certain workflows—particularly the creation of ACH transactions from PDF documents—were intentionally implemented as human-driven processes. At the time, generative AI and OCR technologies had not yet matured enough for reliable, production-grade automation, and the industry itself relies on PDFs as the standard format for documenting and tracking transactions.
The platform was therefore designed to support these industry-standard workflows while maintaining accuracy, control, and auditability. As AI and document-processing capabilities matured, the client again partnered with Step 7 Consulting to enhance the existing platform with intelligent automation—building on a foundation Step 7 had already architected and delivered.
The decision to pursue AI automation emerged from strategic discussions between Step 7 and the startup’s leadership team, who recognized that the manual labor and associated bottlenecks were a major limitation that could be eliminated with the emergence of AI. Step 7’s deep understanding of both the existing platform architecture and the business context enabled a rapid, confident path to AI implementation.
Challenges
Although the core ACH platform was robust, transaction creation relied heavily on manual effort:
- Users manually reviewed PDF documents to identify payees, amounts, and payment details
- Data-entry errors required rework and manual corrections
- Transaction creation averaged approximately 5 minutes per payment
- The workflow did not scale efficiently with transaction volume
- Any automation had to preserve security, auditability, and human oversight
The challenge was not simply extracting text from documents, but doing so accurately, deterministically, and in a way that fit regulated fintech workflows.
Solution
Step 7 Consulting designed and implemented a production-grade AI Assistant embedded directly into the existing fintech platform, eliminating manual transaction creation while preserving enterprise-level control and traceability.
Rather than treating AI as a standalone feature, the solution was architected as a deterministic workflow augmented by AI, with clearly defined inputs, validation layers, and auditable outputs.
Throughout the initiative, Step 7 maintained close alignment with the startup’s leadership, providing regular progress updates and conducting joint reviews of AI output quality to ensure the automation met both technical and business requirements before production deployment.
Engineering Leadership & Delivery
Step 7 Consulting served as the technical leadership and delivery organization for the entire AI initiative.
Beyond architecture and implementation, Step 7 led and managed the full, cross-functional development team, spanning:
- AI solutions and systems architecture
- Project and delivery management
- Frontend and backend engineering
- Data and prompt engineering
- Infrastructure and cloud engineering
- Quality assurance and release management
The development team was composed of a mix of Step 7 engineers and additional specialists recruited and onboarded by Step 7 to meet the needs of the platform. Regardless of sourcing, all technical resources operated under Step 7’s leadership, processes, and delivery standards.
Throughout the engagement, Step 7:
- Collaborated with leadership to define success criteria and validate that AI automation addressed the highest-priority operational pain points
- Defined the end-to-end system and data architecture
- Authored detailed technical specifications, workflows, and acceptance criteria
- Planned and coordinated work across frontend, backend, data, and infrastructure teams
- Led sprint execution, QA, and production deployments
- Provided leadership with transparent visibility into AI performance metrics, error rates, and ROI projections throughout development
- Owned delivery accountability from concept through production release
- Delivered documentation to support long-term platform evolution
This model ensured architectural consistency, predictable delivery, and a single point of technical ownership across a complex, multi-system platform. The continued partnership with the startup’s leadership enabled rapid decision-making on AI investment priorities and risk tolerance, accelerating time to value.
AI Assistant Architecture & User Experience
Step 7 designed the full AI Assistant experience, including UI workflows, database models, and API architecture, ensuring seamless integration with the existing SaaS platform.
The AI Assistant enables users to:
- Select and upload PDF documents directly from their desktop
- Securely submit documents for automated processing
- Review and approve system-generated transaction data
Key technical components included:
- Secure uploads using AWS S3 Transfer Acceleration to optimize performance
- AWS Presigned URLs to enforce time-bound, secure file access
- Backend services coordinating document intake, processing states, and failure handling
Document Processing & Structured Data Extraction
To reliably convert industry-standard PDF documents into structured data:
- AWS Textract was used to extract text and document structure from uploaded PDFs
- Extracted content was normalized into a consistent, intermediate JSON-based schema to support validation, replay, and downstream processing
- Extraction results and metadata were persisted to enable auditability, troubleshooting, and reprocessing
This ensured downstream AI processing operated on known, structured inputs, not raw documents.
AI-Driven Interpretation with Guardrails
Step 7 integrated OpenAI GPT as an interpretation layer—not a decision engine.
Key design considerations included:
- Prompt engineering that explicitly defined required fields, data types, and output structure
- Instructions for handling missing or ambiguous data
- Constraints requiring strict, schema-compliant JSON responses
AI output was treated as suggested data, never authoritative truth.
Validation, Idempotency & Transaction Creation
To ensure enterprise-grade reliability:
- AI output was validated against business rules and data schemas
- New payees were created automatically only if they did not already exist
- ACH transactions were created programmatically using validated data
- Idempotency controls prevented duplicate transaction creation
- All steps were designed to be safely retried without side effects
This ensured the workflow remained deterministic even when AI behavior varied.
Observability, Logging & Operational Control
Every step of the AI-assisted workflow was logged in real time, including:
- Document upload events
- Textract extraction results
- OpenAI prompt requests and responses
- Validation outcomes and transaction creation steps
This provided:
- Full auditability suitable for regulated environments
- Operational visibility and troubleshooting capability
- Confidence deploying AI into production systems
Results
- Delivered measurable ROI that enabled leadership to confidently invest in additional AI-driven enhancements across the platform
- Reduced ACH transaction creation time from ~5 minutes to ~15 seconds per transaction
- Eliminated manual data-entry errors, achieving zero-error transaction creation
- Increased throughput without adding operational headcount
- Preserved security, auditability, and human oversight in a regulated fintech system
- Established a replicable AI implementation model that leadership could apply to future automation opportunities
Before & After Snapshot
Before
- Manual review of PDF documents
- ~5 minutes per transaction
- Frequent data-entry errors
- Limited scalability
After
- AI-driven automated transaction creation
- ~15 seconds per transaction
- Zero errors
- Scalable, repeatable workflow
- Clear ROI framework demonstrating business value
Why This Matters for Mid-Market Teams
Many fintech and mid-market teams want to adopt AI but struggle to operationalize it safely within production systems.
This project demonstrates how Step 7 Consulting helps teams:
- Architect platforms with future extensibility in mind
- Align AI adoption with industry standards, not hype
- Partner with leadership to prioritize AI investments based on business impact
- Insert AI into deterministic workflows with validation and guardrails
- Preserve auditability, control, and human oversight
- Deliver measurable business outcomes from AI initiatives
The result isn’t experimental AI — it’s enterprise-grade automation built on a solid technical foundation and trusted partnership with leadership.
Project Artifacts
AI Assistant
Transaction Detail
Source PDF
