Building an AI‑First Document Digitization Engine for Mifos X: Architecture, Async Patterns, and Debugging LLM Pipelines in Production

Mifos Initiative is a global open source core banking platform serving underserved communities. But onboarding new financial institutions means migrating decades of paper records, handwritten forms, and legacy reports a task that breaks traditional ETL tools.

We built an AI‑powered document digitization and intelligent report migration engine as a GSoC 2026 project. This talk is not about ML theory it’s about the production engineering behind it: how we architected a scalable, debuggable, and maintainable system inside the Mifos ecosystem.

We will cover three hard‑earned engineering lessons:

1. Technology architecture for hybrid AI workloads

• Microservices: Document service, OCR ensemble (GLM ollama), LLM extractor, report migrator, verification dashboard.

• Async choreography: RabbitMQ/Kafka between upload, processing, and Mifos API ingestion.

• Polyglot storage: PostgreSQL for structured data, MinIO/S3 for documents, MongoDB for OCR metadata.

• Idempotency & rollback: How we ensure financial data integrity across retries and partial failures.

2. Engineering practice – debugging AI pipelines in production

• Logging confidence scores per field to detect silent extraction degradation.

• “Human‑in‑the‑loop as a debugging tool” – routing low‑confidence extractions to a review dashboard that becomes a labelled dataset for fine‑tuning.

• Simulating legacy document degradation (noise, skew, low DPI) in staging to stress‑test OCR before deployment.

• Tracing: Correlating document ID → preprocessing parameters → OCR output → LLM prompt → entity JSON → Mifos API call.

3. Developer productivity patterns for open source AI projects

• Reusable Docker Compose stack for local development (OCR models via CPU, mock LLM for integration tests).

• GitHub Actions CI: Unit tests + golden‑dataset regression tests for entity extraction.

• Structured prompt versioning (Git‑tracked YAML prompts) to rollback bad LLM behaviours.

• Live demo: One command to run the full digitization pipeline on a sample handwritten loan application.

Attendees will learn:

1. Reference architecture for integrating OCR, LLMs, and legacy banking APIs in an open source project.

2. How to design async job queues for ML workloads where inference latency is unpredictable.

3. Concrete debugging strategies for AI pipelines: confidence logging, HITL as debugger, synthetic degradation testing.

4. DevOps patterns for shipping open source AI features without dedicated ML engineers on call.

5. A checklist for making LLM‑based systems auditable and rollback‑ready.