Domain-specific AI.
Built for Marathi.
Instead of building generalist models, we fine-tune for high-value domains — starting with 200,000+ Government Resolutions. Purpose-built tools that outperform general-purpose AI on the problems that matter most.
Providing quality Marathi data to
- Sarvam AI
- BharatGen
- Fractal
- Bhashini
- Krutrim
- Qure.ai
- AI4Bharat
- Haptik
- Gnani.ai
- Tech Mahindra
- Persistent
- IndiaAI
The problem
Maharashtra's data is locked in formats AI cannot read.
Millions of documents across government, judiciary, agriculture, and education sit inaccessible — scanned on paper, trapped in legacy font encodings, or digital but unstructured. General-purpose AI tools fail on Marathi. Quality Indic data is the acknowledged global bottleneck.
Scanned archives
Decades of government records, court documents, and manuscripts exist only as scanned images. Full OCR is required. Generic tools produce 8-17% error rates on Devanagari.
Legacy font encodings
Documents look like Devanagari but use proprietary ASCII mappings — Shree Dev, Kruti Dev, Shusha. Text extraction produces garbled output. Each font family has its own mapping.
Digital but unstructured
Even natively digital documents are not searchable, indexed, or cross-referenced. Without entity extraction and metadata, they are invisible to analysis.
Quality data is the bottleneck
At the Delhi AI Summit, OpenAI, Google, and Sarvam identified quality Indic training data as the single biggest barrier to multilingual AI performance.
The approach
Specialists outperform generalists. On every domain that matters.
One model trying to handle Marathi alongside English, Hindi, and Tamil will always compromise. Instead, we build focused models — each one deeply tuned to a single domain's script, layout, and vocabulary.
Generalist model
Adequate on all. Excellent at none.
Specialist — GR model
Purpose-built for one thing. Excellent at it.
Domain-specific AI
Fine-tuned for high-value domains. Not another generalist model.
Instead of building one model for every language, we target specific domains where precision matters most — then fine-tune until we outperform every general-purpose alternative.
What you actually get
From raw scan to queryable graph. Six layers, each unlocks what the last cannot.
Watch a single document transform through every stage of the pipeline — from an image of a page, through layout analysis, quality-verified text, and semantic tagging, to a queryable node in a knowledge graph.
Raw scan
Quality control
Layout analysis
सामान्य प्रशासन
विषय: अनुदान
संदर्भात निर्णय
Text + structure
Entity extraction
Knowledge graph
Start with Government Resolutions. 200,000 decisions, finally connected.
The full pipeline applied end-to-end — from scanned PDFs to a queryable knowledge graph of Maharashtra's governance.