We founded Pluton Technologies in Chhattisgarh with a single focus: build the document foundation required to digitise India's 60 million SMBs, and to serve the national ambition of Viksit Bharat @2047.
Over 70% of vital business and governance records in India — land revenue titles (खसरा / खतौनी), mandi billing, kirana invoices, court affidavits — exist on paper, in formats no machine can read.
Foreign scanning tools and cloud OCR APIs are trained almost exclusively on Latin typography and clean European business invoices. Applied to Indian multilingual documents, low-contrast carbon copies and complex Devanagari script, they produce severe character truncation and disordered output. Not slightly worse results — unusable ones.
Pluton was founded to close that structural gap with multimodal vision-language models purpose-built for India's linguistic diversity and its digital public infrastructure. The country's information economy cannot rest on systems that only read English.
Give any Indian organisation a complete document-understanding capability, without stitching together three brittle tools.
Capture, read, structure, verify. Without shipping sensitive documents to a third party whose retention policy nobody has read, and without being locked to a single cloud because that is where the OCR happens to live.
These are load-bearing. Each closes off a shortcut that would make the near term easier.
Devanagari and the regional Indic languages modelled as first-class citizens, not secondary afterthoughts bolted onto an English system. The training data, the evaluation sets and the failure cases we optimise for are Indian documents.
High-performance on-device processing and offline-first scanning queues, tuned for Tier 2 and Tier 3 cities where the network is intermittent and the handset is a budget Android device, not a flagship.
Zero permanent image storage and strict ephemeral processing. These are Aadhaar forms, land titles and medical records; the privacy design is architectural, and it is easier to hold from the start than to retrofit.
Every accuracy figure we publish arrives with the document set it was measured on. A number without its conditions is not a claim anyone can check, and this category has too many of those already.
Document AI is not an empty field. It is a field whose incumbents each have a structural reason not to solve the specific problem we are solving.
| Player | Strength | Where it leaves room |
|---|---|---|
| Google Document AI | Broad processor ecosystem, deep model research | Cloud lock-in; setup complexity; Indic performance is not the priority |
| Azure Document Intelligence | Strong on printed text, deep Microsoft integration | At its best only inside the Microsoft stack |
| AWS Textract | Reliable inside AWS-native pipelines | AWS-bound; little deployment flexibility |
| Open vision-language models | Cheap, private, closing the gap quickly | Raw models, not a product — no orchestration, no review, no guarantees |
| Indian LLM labs | Strong language coverage and national alignment | General-purpose models; documents are one use case among many, not the whole focus |
Independent benchmarks place real-world production accuracy across these vendors well below the figures quoted on curated test sets. The useful question is rarely which engine wins on a clean benchmark, but which system is cheapest and most reliable to run on the documents an organisation actually has — in the scripts they are actually written in.
A company honest about its edges moves faster than one claiming all of them.
The faded carbon copy. The handwritten खसरा. The one your current tool quietly returns as empty. That is the one we want to see.
Pluton Technologies (OPC) Private Limited.
Building India's document intelligence layer.