MIKA
MIKA: multi-modal document analysis
In short
MIKA (Multi-modal Intelligent Knowledge Analysis) is the RootNia module that reads and interprets a document's text, tables, images and scanned pages together. Instead of handling each element separately, it combines them into a single structured knowledge layer that classification, extraction and RAG all build on.
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Why multi-modal analysis is needed
Real documents are rarely just text. A policy mixes clauses with tables of sums insured; a technical report mixes paragraphs, figures and specification tables; a scanned delivery note is, at source, an image. Processing each part separately loses context: a figure in a table is no longer tied to the row and column that give it meaning.
What MIKA does
- Reads several content types at once: text, tables, images and scanned pages from the same document.
- Preserves structure: keeps the relationship between headings, cells, figures and the text that describes them.
- Produces a single knowledge layer: a structured representation of the document, ready to classify, extract and query.
Where it fits in the pipeline
- The orchestrator collects the document from SharePoint, OneDrive or a database.
- If it is a scan or an image, OCR extracts the text.
- MIKA interprets the whole document and builds the structured layer.
- The classifier assigns sector and type; VERA extracts values anchored to their region; RAG uses that layer to answer.
Example: a table that keeps its context
Illustrative example of how it works, not a specific customer.
A policy wording includes a three-column table — cover, sum insured and deductible — and a footnote that excludes one scenario. A text-only system returns loose figures. MIKA keeps each figure tied to its row and column, and links the footnote to the cover it affects, so a later question about that cover gets the right figure and the exclusion.
Limits and good practices
- Source quality: a badly degraded scan limits what any system can read; digitize at a reasonable resolution.
- Highly visual documents: charts or drawings with no text carry less information than a table or a paragraph.
- Verification: for critical values, combine MIKA with VERA to review each value at its source.
Examples
- Tables of sums insured and deductibles in insurance policies.
- Specification tables and figures in engineering documentation.
- Scanned reports in healthcare.
Frequently asked questions
What does MIKA stand for?
Multi-modal Intelligent Knowledge Analysis. "Multi-modal" means it interprets several kinds of content — text, tables and images — within the same document.
How is it different from OCR?
OCR turns images into text. MIKA goes a step further — it interprets how that text relates to tables and figures — so the content keeps its structure and meaning.
Which modules use MIKA's output?
The knowledge layer MIKA produces feeds sector classification, VERA's verified extraction and RAG's context retrieval.