Understand Meaning.Not Just Layout.Automate with Intent.
Most document automation platforms focus on extracting what they see: tables, fields, checkboxes, and text blocks detected by OCR and machine-learning models. MiruIQ was built for a different problem.
Comparison at a Glance
See how MiruIQ compares to typical IDP platforms.
| Capability | MiruIQ | Typical IDP Platforms |
|---|---|---|
| Meaning-based extraction | ||
| Semantic table selection | ||
| Context interpretation outside tables | ||
| Cross-document validation | ||
| Pipeline orchestration | ||
| Local AI in managed service | ||
| Per-pipeline isolation |
Meaning First. Normalize Everything.
With MiruIQ, you define what data means, not how it looks.
Whether a document uses "Bruttolohn", "Lohn", "Gross salary", or explains values indirectly in surrounding text, MiruIQ extracts and normalizes the information into a single, consistent target structure.
Adapt to Change
Handles changing layouts and document variants gracefully; remains stable even as formats evolve.
Normalize Once
Define meaning once. MiruIQ maps diverse representations to a single, consistent target structure.
Format Resilient
Works across languages, layouts, and document versions without retraining or reconfiguration.
Fixed Extraction vs Semantic Understanding
In any IDP software comparison, the first question is what actually gets extracted. Most document automation platforms (including Klippa, Nanonets, Rossum, and ABBYY) extract fixed elements: tables based on layout, rows and columns as they appear, checkboxes and form fields structurally.
This works well when layouts are consistent, there is one obvious table or field, and interpretation happens later. MiruIQ goes further. A document AI comparison should therefore start one level deeper: not which fields a tool detects, but whether it understands what those fields mean.
Typical Extraction
MiruIQ Semantic Extraction
Semantic Extraction in Practice
See how MiruIQ understands context that typical tools miss entirely.
Value Normalization
MiruIQ interprets contextual notes and units to automatically normalize raw extracted values into their true numeric form.
A document table shows: Month / Earnings / Deductions, with values 4.5 and 0.8. Above the table it states: "All amounts are shown in thousands CHF for readability."
Typical tools extract Earnings = 4.5 and Deductions = 0.8, forwarding values as-is.
MiruIQ understands that Earnings represents monetary income, interprets the contextual note outside the table, and automatically normalizes values to 4500 and 800 CHF.
{
"monthly_earnings_chf": 4500,
"monthly_deductions_chf": 800
}Context Interpretation
MiruIQ reads footnotes, disclaimers, and contextual notes surrounding tables to semantically enrich extracted values.
Footnotes state: "Gross salary includes base pay and bonuses. Overtime compensation is excluded." A table below shows Employee / Salary with value 7'200.
Typical tools extract Salary = 7200 without semantic context.
MiruIQ understands "Salary" refers to gross salary, overtime is explicitly excluded, and extracted values are semantically aligned for downstream checks.
{
"gross_salary": 7200,
"includes_base_pay": true,
"includes_bonuses": true,
"includes_overtime": false
}This prevents silent mismatches when data is compared, validated, or aggregated across systems.
Built for Real Business Processes, Not Single Documents
Cross-document logic is a first-class capability.
Many workflows require multiple documents to be evaluated together: loan applications, insurance claims, compliance cases, and incident reports. MiruIQ supports this natively.
Document Grouping
Group related documents into a single business case and wait until the group is complete.
Cross-Document Validation
Perform semantic cross-document validation and forward structured validation results.
Explicit Lifecycle
Immediately delete all intermediate state after validation: no implicit document retention.
This capability is not advertised as a native feature by most document automation platforms.
Pipelines, Not Point Solutions
Orchestration instead of one-off extraction.
MiruIQ is built around pipelines that connect sources, classifiers, extractors, validations, and sinks into cohesive automation flows.
Sources
Processing
Routing & Output
MiruIQ acts as the orchestration layer between documents and decisions, not just an extraction endpoint.
Extract What Matters, Not Everything
Focus on relevant pages, even in very large files.
Instead of processing a document as a single block, MiruIQ can operate at page and section level using semantic relevance detection.
Relevance Detection
Identify which pages matter and ignore boilerplate and noise.
Targeted Extraction
Extract only relevant sections or tables, improving accuracy and performance.
Ideal For
Local AI: In Every Deployment Model
No public cloud AI. No third-party inference.
MiruIQ runs local AI and LLMs in all deployment modes, including the managed as-a-service offering.
No Public Cloud AI
Documents are never sent to public cloud AI providers. No reliance on hyperscaler inference APIs.
Local Infrastructure
AI models run within MiruIQ-operated infrastructure or fully on-premise.
Isolation by Design
No shared document pools, no cross-pipeline visibility. Scoped temporary state and isolated processing contexts.
Guarantees
We do not trust third-party cloud AI providers with customer documents, and neither should you.
Comparison Questions
What to check in any IDP software comparison, and where MiruIQ stands.
Three things: whether extraction handles your hardest documents (complex tables, long documents, mixed bundles) without per-layout templates, whether human review is built into the flow rather than bolted on, and whether the platform can deploy where your data must live. MiruIQ was built on exactly these three.
Template-based tools learn a layout and break when the layout changes. MiruIQ is schema-driven: you define the data you need and the extraction understands the document semantically. The same schema works across every supplier's invoice format and every bank's statement layout.
Run your five hardest documents through each candidate before reading any feature list. Then ask the deployment question hyperscaler-based vendors avoid: can it run on-premise or in Swiss/EU hosting with local models? Finally, insist on pricing you can compute per document.
Document Automation That Understands Meaning
Move beyond layout-based extraction. Build automation that understands what documents mean.