Enterprise document digitisation — archives that finally search back.
Complete digitisation of hard-copy documents, images and video archives for enterprises across any segment and language. On-premise deployments mean data never leaves your infrastructure.
What this line actually covers
Turning enterprise archives — decades of scanned paper, handwritten registers, mixed-script forms — into something searchable in natural language, deployed on the client's own infrastructure rather than a cloud API. Coverage spans Hindi, Marathi, Tamil, Telugu, Bengali, Gujarati, and Urdu, with natural-language search across millions of pages and an agentic layer that classifies, tags, and routes documents rather than just extracting text and stopping there.
Who this is for
Financial institutions, legal firms, healthcare providers, and government departments sitting on archives that are too large, too old, or too sensitive to digitise using a generic cloud OCR tool — especially where the documents themselves were never written in Latin script to begin with.
The market, honestly
The global platforms — ABBYY, Google Document AI, AWS Textract, Azure AI Document Intelligence, Hyperscience, and a wave of mid-market tools like Nanonets and Docsumo — all claim broad language coverage, sometimes into the hundreds of languages. That claim is mostly true and mostly beside the point. Indic scripts carry over a hundred unique glyphs against roughly fifty for Latin script, plus conjuncts — a single character in Devanagari or Tamil can be built from two or three components fused together — that most models trained primarily on Latin-script data simply haven't seen enough of. Handwriting is worse: there isn't nearly enough annotated training data for Indian-language handwriting the way there is for English, and even recent published benchmarks show OCR-vision models degrading sharply on Devanagari and other low-resource scripts once the scan quality drops from lab-clean to "actually pulled out of a filing cabinet." None of the big cloud platforms publish credible numbers on handwritten Indic-script accuracy, and most of them are cloud-only anyway, which rules them out for a regulated archive that can't leave the building.
The most direct competitor is Sarvam AI's Akshar platform, built specifically for Indic document digitisation across scanned archives and complex scripts, alongside a cluster of academic and government infrastructure — AI4Bharat, the NLTM-OCR consortium spanning several IITs, and Bhashini as the shared national language layer everyone increasingly builds on top of. There's also a heritage-archive niche (organisations like MIDF and the National Archives' own digitisation vendor) doing related work at the preservation end of the spectrum.
The industry-wide direction of travel is away from "OCR plus keyword search" and toward agentic document intelligence — extraction feeding directly into retrieval and automated workflows rather than a static searchable index. Most enterprise IDP buyers are now evaluating that agentic layer as standard, not optional.
Where FRAM3 sits
Genuine on-prem, sovereign deployment for archives that legally cannot touch a hyperscaler API; real depth across seven scripts including the harder cases like Urdu nastaliq and mixed-script pages, instead of shallow breadth across two hundred languages that mostly means "we tried"; and an agentic classify-tag-route layer built in from the start, not bolted onto a search box. Add boutique-level willingness to fine-tune against a specific client's actual handwriting style and archive quirks — something none of the global platforms will do at this scale of engagement.
What we deliver
- OCR and digitisation across Hindi, Marathi, Tamil, Telugu, Bengali, Gujarati, and Urdu
- Natural-language search across millions of pages
- Agentic classification, tagging, and routing — not just extraction
One engine, sharpened from every direction.
Have a project that fits this line?
Or one that fits none of them. Those are usually the interesting ones.