MuleSoft IDP in Production – Lessons Learnt, Common Mistakes, and What We’d Do Differently.
Most Intelligent Document Processing (IDP) initiatives look impressive in demos. Clean documents. High accuracy. Instant automation. But when it goes to Production, everything changes as Production is different.
Once MuleSoft IDP moves beyond a POC and into real business operations, new challenges surface, They can be categorized into technical, operational, and organizational. This blog shares real-world lessons learned from implementing MuleSoft IDP in production environments, so you can avoid costly missteps.
1. The Biggest Myth: “IDP Will Be 100% Accurate“
This is the most common and dangerous assumption. In our first blog in IDP series https://conscendo.io/intelligent-document-processing-with-mulesoft-what-customers-should-really-know/ we discussed what customers should know before selecting the IDP.
What Happens in Reality
- Scanned documents vary wildly in quality, while doing POCs we pick only the best documents
- Templates evolve without notice, while in POC stage, we might receive only a few standard or old templates
- Handwritten or stamped content appears
- OCR accuracy fluctuates based on layout and scan resolution
Lesson Learned
IDP is probabilistic, not deterministic.
What Works
- Define confidence thresholds per field
- Accept partial automation
- Design workflows assuming human review will exist
2. IDP Does Not Eliminate Human Review, It Optimizes It
Many teams attempt to remove humans completely. That usually fails.
Where Human-in-the-Loop Is Essential
- Low-confidence fields
- Exception documents
- Regulatory or financial approvals
- First-time document templates
Best Practice
Design human review as a first class capability, not an afterthought:
- Role-based access
- Clear correction UX
- Audit trails for changes
Automation improves when humans are part of the loop.
3. OCR Is Not the Same as Extraction (And Treating Them the Same Hurts Scale)
A common architectural mistake is tightly coupling OCR and extraction.
Why This Breaks in Production
- OCR is compute-heavy
- Extraction models evolve faster
- Re-running OCR is expensive and slow
Lesson Learned
Separate:
- OCR processing
- Data extraction
- Post-processing logic
This enables:
- Independent scaling
- Faster model improvements
- Easier reprocessing
4. Document Volume Spikes Are Inevitable
POCs often use steady, predictable volumes.
Production never does.
Real-World Triggers
- Month-end closings
- Seasonal demand
- Partner batch uploads
- Backlogs after outages
What Breaks First
- Synchronous flows
- Hard-coded rate limits
- Tight API dependencies
What Works
- Queue-based async processing
- Back-pressure instead of failures
- Bulk-friendly ingestion patterns
Design IDP for bursts, not averages.
5. Model Accuracy Improves Slowly – Manage Expectations
AI models do improve, but not overnight.
What Teams Expect
- Continuous rapid accuracy gains
What Actually Happens
- Incremental improvements
- Diminishing returns after a point
- Some documents never reach high confidence
Lesson Learned
- Measure accuracy realistically
- Track false positives vs false negatives
- Focus on business impact, not model scores
6. Monitoring Is More Than “Is the Flow Up?“
In production, uptime alone is meaningless.
What You Actually Need to Monitor
- OCR success rate
- Extraction confidence trends
- Human review volume
- Processing time per document
- Failure and retry patterns
Key Insight
A “green” Mule app can still deliver bad data.
Observability must include data quality, not just system health.
7. Reprocessing Is Not Optional – It’s Mandatory
Every production IDP system will need reprocessing.
Common Scenarios
- OCR engine upgrades
- Improved extraction models
- Downstream system failures
- Business rule changes
What Fails
- Forcing users to re-upload documents
- Re-running full pipelines unnecessarily
What Works
- Store raw documents centrally
- Store extracted results and metadata
- Enable replay from object storage
- Keep extraction idempotent
If you can’t replay, you don’t have enterprise IDP.
8. Cost Control Becomes a Real Conversation
AI and OCR are not free.
Hidden Cost Drivers
- Repeated OCR calls
- Over-processing low-value documents
- Poor confidence thresholds
- Unnecessary re-runs
Lessons Learned
- Classify documents early
- Skip extraction when automation value is low
- Tune confidence thresholds pragmatically
IDP success includes financial sustainability.
9. Business Adoption Matters More Than Technical Elegance
Some technically perfect IDP solutions still fail. Why?
Common Reasons
- Review screens are confusing
- Exception handling is slow
- Business teams don’t trust extracted data
What Works
- Involve business users early
- Start with semi-automation
- Build trust through transparency
- Improve UX before improving models
Adoption beats accuracy.
10. What We’d Do Differently Next Time
If we were starting again:
- Design for reprocessing on day one
- Separate OCR, extraction, and orchestration
- Treat human review as core, not optional
- Build async-first architectures
- Measure business outcomes, not AI hype
- Start with document classification
- Standardize documents early
- Design reprocessing from day one
Final Thoughts: Production Is Where IDP Becomes Real
MuleSoft IDP is a powerful capability, but only when designed for reality.
Production environments expose:
- Variability
- Scale challenges
- Human dependencies
- Cost constraints
Teams that succeed don’t aim for perfect automation.
They aim for reliable, governed, and continuously improving automation.
