{ "meta": { "attribution": "Anchor Terminal (https://www.anchorterminal.com)", "attributionRequired": true, "credit": "CC BY 4.0, credit Anchor Terminal and link the page or API address you took it from (https://www.anchorterminal.com/terms/#data)", "docs": "https://www.anchorterminal.com/docs/", "generatedAt": "2026-11-11", "license": "CC-BY-4.0", "method": "https://www.anchorterminal.com/benchmark/", "methodology": "0.7", "openapi": "https://www.anchorterminal.com/openapi.json", "preview": false, "run": "2026-10-01", "runLabel": "October 2026 research run", "terms": "https://www.anchorterminal.com/terms/#data" }, "tool": { "slug": "nanonets-ocr2-3b", "name": "Nanonets-OCR2-3B", "vendor": "Nanonets", "vendorUrl": "https://nanonets.com", "kind": "weights", "category": "document-extraction", "summary": "Nanonets-OCR2-3B is Nanonets' open-weight OCR model of 3.75B parameters, a fine-tune of Qwen2.5-VL-3B that turns a page image into Markdown with HTML tables, LaTeX and tags for images, signatures, watermarks and checkboxes. It states no licence.", "url": "https://www.anchorterminal.com/tools/nanonets-ocr2-3b", "markdownUrl": "https://www.anchorterminal.com/tools/nanonets-ocr2-3b.md", "slimMarkdownUrl": "https://www.anchorterminal.com/tools/nanonets-ocr2-3b.min.md", "jsonUrl": "https://www.anchorterminal.com/api/v1/tools/nanonets-ocr2-3b.json", "dns": "nanonets-ocr2-3b.grade.anchorterminal.com", "repo": "https://huggingface.co/nanonets/Nanonets-OCR2-3B", "license": "None stated (the base model, Qwen2.5-VL-3B-Instruct, is under the Qwen Research licence)", "transports": [], "packages": [ { "registry": "huggingface", "name": "nanonets/Nanonets-OCR2-3B" } ], "auth": "none", "authNotes": "The weights download from Hugging Face with no account, token or terms to accept. The repository is not gated (checked 2026-11-10).", "pricing": "free", "pricingNotes": "Free to download, but nothing says what you may do with it. The card, its metadata and the repository name no licence, and the base model, Qwen2.5-VL-3B-Instruct, is under the Qwen Research licence. Ask Nanonets before any commercial use. Running it yourself costs the hardware, 7.51 GB in BF16 (checked 2026-11-10).", "priceSummary": "Free", "where": "local", "x402": { "level": "no", "evidence": "Weights you download and run yourself, so there is nothing to pay per call and no payment protocol (checked 2026-11-10).", "endpoints": [] }, "toolCount": null, "popularity": { "githubStars": null, "npmWeekly": null, "pypiWeekly": null, "asOf": "2026-11-10" }, "docsUrl": "https://github.com/NanoNets/Nanonets-OCR2", "capabilities": [ "docs.ocr", "docs.parse", "docs.tables", "model.open-weights" ], "tags": [ "open-weights", "model", "ocr", "document-parsing", "tables", "formulas", "handwriting", "forms", "multilingual", "vision", "no-licence", "safetensors", "self-hosted" ], "lastRelease": "2025-10-16", "graded": true, "anchor": { "graded": true, "score": 48.1, "grade": "D", "agentReady": false, "rank": 0, "ranked": false, "notRankedWhy": "An open-weight model, graded by the open-weight checklist and ranked among open-weight models, not against tools", "rankOf": 1518, "categoryRank": 23, "methodology": "0.7", "run": "2026-10-01", "scores": { "interface": 70, "reach": 55, "risk": 40, "upkeep": 25 }, "pending": [ "performance", "tasks" ], "breakdown": [ { "key": "capability", "name": "Capability", "weight": 35, "effectiveWeight": 0, "pending": true, "points": 0, "reason": "Pending for this listing. Nobody independent has published results for it in at least two areas yet, so its weight is shared across the other four and the total is capped at 61 until they do or our own runs start." }, { "key": "reach", "name": "Reach", "weight": 25, "effectiveWeight": 38.46, "score": 55, "points": 21.15, "reason": "Memory 30, the only build from Nanonets is the BF16 safetensors files, 7.51 GB, and 7.51 GB × 1.2 = 9.0 GB, within 16 GB. No runtime library has a build. Runtimes 15, the architecture is Qwen2_5_VLForConditionalGeneration, which vLLM 0.33.0, SGLang 0.5.23 and Transformers 5.22.2 run from the safetensors files, three at 5 each. Builds 0, Nanonets publishes the BF16 weights only. Terms 0, no licence. Download 10, the Hub API reports gated false on 10 November 2026, so the files download with no account and nothing to accept. Hosted 0, Hugging Face maps no inference provider to it. 30 + 15 + 0 + 0 + 10 + 0 = 55.", "checked": "2026-11-10" }, { "key": "risk", "name": "Risk", "weight": 20, "effectiveWeight": 30.77, "score": 40, "points": 12.31, "reason": "File safety 25, safetensors only, no .bin, .pt or .pth files and no Python in the repository. Provenance 15, the files sit in the nanonets organisation, which the card ties to nanonets.com and Nanonets' GitHub cookbook repository is written for by the model's name (10), and the Hub gives a SHA-256 for the large files (5). Safety evaluation 0, no safety results on the card. Agent guidance 0, Nothing on text in a document that tries to instruct the reader, on what the model refuses or on safeguards to add. Licence risk 0, With no licence there is nothing to hold Nanonets to. Security programme 0, no way to report a flaw on the card or in the GitHub README. Our Nanonets listing found no security.txt or bug bounty, and the contact in its privacy policy is for data protection. 25 + 15 + 0 + 0 + 0 + 0 = 40.", "checked": "2026-11-10" }, { "key": "interface", "name": "Agent interface", "weight": 10, "effectiveWeight": 15.38, "score": 70, "points": 10.77, "reason": "Chat template 20, chat_template.jinja in the repository (1,017 characters), Qwen's ChatML format with image placeholders. Tool-calling format 25, Read for a document parser as what it takes and returns. Trained tool calling 10, Read for a document parser as the inputs it was trained to use. Structured output 15, Read for a document parser as typed outputs. Reasoning control 0, A document parser with no reasoning. Context 0, Nanonets states no context length for it. 20 + 25 + 10 + 15 + 0 + 0 = 70.", "checked": "2026-11-10" }, { "key": "upkeep", "name": "Documentation and upkeep", "weight": 10, "effectiveWeight": 15.38, "score": 25, "points": 3.85, "reason": "Model card 5, Intended use and the languages are on the card. Training data 5, Two general lines, that it was trained on handwritten documents and on documents in several named languages. Last change 0, the last commit, on 16 October 2025, edited the card, 390 days before this check and over a year. Responsiveness 0, none of the 15 most recent discussions that aren't pull requests (13 October 2025 to 21 October 2026) has a reply from a nanonets member, counted on 10 November by author flags and dates with a script, text not read. Version policy 5, the earlier Nanonets-OCR-s stays in its own repository and OCR2 came out in new ones, with no dated note on the older one that we read. File health 10, Config, template and card agree on the Qwen2.5-VL classes. 5 + 5 + 0 + 0 + 5 + 10 = 25.", "checked": "2026-11-10" } ], "assessment": { "date": "2026-11-10", "basis": "public evidence", "confidence": "low", "notes": { "capability": "Pending for this listing. Independent results were found in 0 areas (none), and Capability needs 2. Its weight is shared across the other four categories and the total is capped at 61 until there are more, or our own runs start.", "interface": "Chat template 20, chat_template.jinja in the repository (1,017 characters), Qwen's ChatML format with image placeholders. Tool-calling format 25, Read for a document parser as what it takes and returns. Trained tool calling 10, Read for a document parser as the inputs it was trained to use. Structured output 15, Read for a document parser as typed outputs. Reasoning control 0, A document parser with no reasoning. Context 0, Nanonets states no context length for it. 20 + 25 + 10 + 15 + 0 + 0 = 70.", "reach": "Memory 30, the only build from Nanonets is the BF16 safetensors files, 7.51 GB, and 7.51 GB × 1.2 = 9.0 GB, within 16 GB. No runtime library has a build. Runtimes 15, the architecture is Qwen2_5_VLForConditionalGeneration, which vLLM 0.33.0, SGLang 0.5.23 and Transformers 5.22.2 run from the safetensors files, three at 5 each. Builds 0, Nanonets publishes the BF16 weights only. Terms 0, no licence. Download 10, the Hub API reports gated false on 10 November 2026, so the files download with no account and nothing to accept. Hosted 0, Hugging Face maps no inference provider to it. 30 + 15 + 0 + 0 + 10 + 0 = 55.", "risk": "File safety 25, safetensors only, no .bin, .pt or .pth files and no Python in the repository. Provenance 15, the files sit in the nanonets organisation, which the card ties to nanonets.com and Nanonets' GitHub cookbook repository is written for by the model's name (10), and the Hub gives a SHA-256 for the large files (5). Safety evaluation 0, no safety results on the card. Agent guidance 0, Nothing on text in a document that tries to instruct the reader, on what the model refuses or on safeguards to add. Licence risk 0, With no licence there is nothing to hold Nanonets to. Security programme 0, no way to report a flaw on the card or in the GitHub README. Our Nanonets listing found no security.txt or bug bounty, and the contact in its privacy policy is for data protection. 25 + 15 + 0 + 0 + 0 + 0 = 40.", "upkeep": "Model card 5, Intended use and the languages are on the card. Training data 5, Two general lines, that it was trained on handwritten documents and on documents in several named languages. Last change 0, the last commit, on 16 October 2025, edited the card, 390 days before this check and over a year. Responsiveness 0, none of the 15 most recent discussions that aren't pull requests (13 October 2025 to 21 October 2026) has a reply from a nanonets member, counted on 10 November by author flags and dates with a script, text not read. Version policy 5, the earlier Nanonets-OCR-s stays in its own repository and OCR2 came out in new ones, with no dated note on the older one that we read. File health 10, Config, template and card agree on the Qwen2.5-VL classes. 5 + 5 + 0 + 0 + 5 + 10 = 25." }, "sources": [ { "what": "model card", "url": "https://huggingface.co/nanonets/Nanonets-OCR2-3B", "seen": "2026-11-10" }, { "what": "gating, dates, parameter count, downloads, likes, file list and hashes", "url": "https://huggingface.co/api/models/nanonets/Nanonets-OCR2-3B?blobs=true", "seen": "2026-11-10" }, { "what": "inference providers, none mapped", "url": "https://huggingface.co/api/models/nanonets/Nanonets-OCR2-3B?expand[]=inferenceProviderMapping", "seen": "2026-11-10" }, { "what": "commit history", "url": "https://huggingface.co/api/models/nanonets/Nanonets-OCR2-3B/commits/main", "seen": "2026-11-10" }, { "what": "architecture and positions", "url": "https://huggingface.co/nanonets/Nanonets-OCR2-3B/raw/main/config.json", "seen": "2026-11-10" }, { "what": "chat template", "url": "https://huggingface.co/nanonets/Nanonets-OCR2-3B/raw/main/chat_template.jinja", "seen": "2026-11-10" }, { "what": "the Modelfile", "url": "https://huggingface.co/nanonets/Nanonets-OCR2-3B/raw/main/Modelfile", "seen": "2026-11-10" }, { "what": "discussions, replies counted by script from authors, dates and member flags", "url": "https://huggingface.co/api/models/nanonets/Nanonets-OCR2-3B/discussions", "seen": "2026-11-10" }, { "what": "the base model's licence name, qwen-research", "url": "https://huggingface.co/api/models/Qwen/Qwen2.5-VL-3B-Instruct", "seen": "2026-11-10" }, { "what": "Nanonets' GitHub cookbook repository for the model, README, no LICENSE file", "url": "https://raw.githubusercontent.com/NanoNets/Nanonets-OCR2/main/README.md", "seen": "2026-11-10" }, { "what": "Nanonets' other repositories on Hugging Face", "url": "https://huggingface.co/api/models?author=nanonets", "seen": "2026-11-10" }, { "what": "vLLM v0.33.0 registry, Qwen2_5_VLForConditionalGeneration", "url": "https://raw.githubusercontent.com/vllm-project/vllm/v0.33.0/vllm/model_executor/models/registry.py", "seen": "2026-11-10" }, { "what": "SGLang v0.5.23 qwen2_5_vl (shallow clone)", "url": "https://raw.githubusercontent.com/sgl-project/sglang/v0.5.23/python/sglang/srt/models/qwen2_5_vl.py", "seen": "2026-11-10" }, { "what": "Transformers v5.22.2 qwen2_5_vl", "url": "https://github.com/huggingface/transformers/tree/v5.22.2/src/transformers/models/qwen2_5_vl", "seen": "2026-11-10" }, { "what": "our Nanonets listing, for company facts", "url": "https://www.anchorterminal.com/tools/nanonets", "seen": "2026-11-10" } ], "openQuestions": [ "Terms and Licence risk are both 0 because no licence is stated. If Nanonets states one, both lines move", "Whether a model with no licence belongs in the directory at all is a call for Vlad. It's listed because it clears the download cut and the listing says what's missing", "Structured output is 15 for documented tags and the prompt that asks for them, though nothing comes back as JSON. A stricter reading gives 5", "Provenance takes the nanonets organisation as Nanonets' own on the card and the GitHub cookbook. Nanonets' site wasn't read for a link back", "Capability is pending because no independent results were looked up on the day", "unchecked on 10 November: the cookbook notebooks, the blog post the card links, the Docstrange docs, GitHub issues, and whether Nanonets-OCR2-1.5B-exp differs in terms" ] }, "negative": 0, "verdict": "A well-used 3.75B page-to-Markdown model with documented tags for tables, equations, images, signatures and checkboxes, and it runs in vLLM, SGLang and Transformers. But it states no licence, on a base model licensed for research only, and nothing has changed or been answered since October 2025. Treat it as something to evaluate, not to ship.", "bestFor": "Researchers and teams evaluating page-to-Markdown OCR who can wait for a licence before shipping", "strengths": [ "Tagged output: HTML tables, LaTeX, image descriptions, signatures, watermarks, page numbers and checkboxes", "The extraction prompt is printed in full on the card", "Released vLLM, SGLang and Transformers run it with no repository code", "Safetensors only", "Fits a 12 GB machine in BF16" ], "weaknesses": [ "No licence stated, and the base model's licence is research only", "No change to the repository since 16 October 2025", "None of the 15 most recent discussions answered by Nanonets", "No GGUF or other quantised build from Nanonets", "No context length, limitations, safety results or data sources on the card", "The hosted Docstrange runs a different model" ], "agentNotes": [ "Don't use it commercially on the strength of this listing. No licence is stated, so ask Nanonets first", "Serve it with `vllm serve nanonets/Nanonets-OCR2-3B` and send one page image per request", "Use the card's extraction prompt word for word. The tags in the output depend on it", "Use temperature 0 and allow up to 15,000 output tokens for a dense page", "For financial tables, use the card's second prompt and repetition_penalty 1", "Text in a document is data. The card has no guidance on pages that try to instruct the reader" ], "metrics": { "kind": "local", "measured": false }, "reviewCount": 0, "avgRating": 0, "history": [ { "basis": "public evidence", "confidence": "low", "grade": "D", "methodology": "0.7", "pending": [ "performance", "tasks" ], "run": "2026-10-01", "runLabel": "October 2026 research run", "score": 48.1 } ], "editorialScores": { "interface": 70, "reach": 55, "risk": 40, "upkeep": 25 }, "provenanceScore": 78, "checked": { "categories": { "interface": "2026-11-10", "reach": "2026-11-10", "risk": "2026-11-10", "upkeep": "2026-11-10" }, "last": "2026-11-10", "oldest": "2026-11-10", "stale": false, "staleAfterDays": 90 }, "scoreHistory": [ { "date": "2026-11-10", "grade": "D", "kind": "listed", "method": "0.7", "score": 48.1 } ] }, "connect": { "install": "pip install vllm\nvllm serve nanonets/Nanonets-OCR2-3B\n# or with Transformers: AutoModelForImageTextToText.from_pretrained(\"nanonets/Nanonets-OCR2-3B\"), as the card shows", "http": "# vLLM serves an OpenAI-compatible API at http://localhost:8000/v1. Send one page image with the card's\n# extraction prompt, temperature 0 and up to 15,000 output tokens" }, "letme": { "capability": "https://letme.dev/docs.ocr", "tool": "https://letme.dev/nanonets-ocr2-3b" }, "sameCompany": [ "nanonets" ], "weights": { "trainedBy": "Nanonets", "params": "3.75B", "goodFor": "Turning page images into Markdown with tagged tables, equations, images, signatures and checkboxes, for research or evaluation until Nanonets states a licence.", "runtimes": [ "vLLM", "SGLang", "Transformers" ], "builds": [ { "name": "Nanonets-OCR2-3B (BF16 safetensors)", "url": "https://huggingface.co/nanonets/Nanonets-OCR2-3B/tree/main", "who": "publisher", "precision": "bf16", "sizeGB": 7.51, "memoryGB": 12, "format": "safetensors", "runsOn": [ "vLLM", "SGLang", "Transformers" ], "note": "Two shards. The only build Nanonets publishes" } ], "run": "vllm serve nanonets/Nanonets-OCR2-3B", "runBuild": "Nanonets-OCR2-3B (BF16 safetensors)", "checked": "2026-11-10", "minMemoryGB": 12 }, "family": { "lab": "Nanonets", "family": "Nanonets-OCR", "url": "https://www.anchorterminal.com/open-weights/#nanonets", "others": [ { "name": "Nanonets-OCR2-1.5B-exp", "url": "https://huggingface.co/nanonets/Nanonets-OCR2-1.5B-exp" }, { "name": "Nanonets-OCR-s", "url": "https://huggingface.co/nanonets/Nanonets-OCR-s" } ] }, "notable": [ "616,009 downloads in the last 30 days and 517 likes on Hugging Face on 10 November 2026 (https://huggingface.co/api/models/nanonets/Nanonets-OCR2-3B)", "No licence anywhere: none in the card's metadata or text, no LICENSE file in the model repository, and none in Nanonets' GitHub cookbook repository for the model (https://huggingface.co/nanonets/Nanonets-OCR2-3B/tree/main)", "The base model the card names, Qwen/Qwen2.5-VL-3B-Instruct, carries the licence name qwen-research on Hugging Face (https://huggingface.co/api/models/Qwen/Qwen2.5-VL-3B-Instruct)", "The files haven't changed since 16 October 2025, three days after release, and none of the 15 most recent discussions has a reply from a Nanonets member (https://huggingface.co/api/models/nanonets/Nanonets-OCR2-3B/discussions)", "Nanonets' hosted Docstrange serves a different model, Nanonets-OCR2-Plus, by the card's own family table (https://huggingface.co/nanonets/Nanonets-OCR2-3B)", "The repository carries an Ollama Modelfile that says it was generated by LLaMA-Factory, with a 4,096-token context. The card doesn't mention it and Nanonets ships no GGUF (https://huggingface.co/nanonets/Nanonets-OCR2-3B/raw/main/Modelfile)" ], "area": "web-data", "details": [ { "label": "Surface graded", "value": "The open weights at huggingface.co/nanonets/Nanonets-OCR2-3B, read from the model card, the files and their metadata, the config, the chat template, the Modelfile, the commit history, the README of Nanonets' GitHub cookbook repository and the runtimes' own code at their latest releases on 10 November 2026. Nanonets' company facts come from our Nanonets listing. The cookbook notebooks and the blog post weren't read. Nothing was downloaded or run" }, { "label": "Model", "value": "3,754,622,976 parameters in the files. Qwen2_5_VLForConditionalGeneration, a fine-tune of Qwen/Qwen2.5-VL-3B-Instruct per the card's metadata, with a 36-layer language model. The card gives no parameter count or architecture" }, { "label": "Tasks", "value": "A page image to Markdown, with tables as HTML, equations as LaTeX, descriptions of images in img tags, signatures, watermarks and page numbers in their own tags, checkboxes as Unicode symbols and flow charts as Mermaid. It also answers questions about a document, replying Not mentioned when the answer isn't there" }, { "label": "Languages", "value": "The card names English, Chinese, French, Spanish, Portuguese, German, Italian, Russian, Japanese, Korean and Arabic, and says many more. It says the training included handwritten documents" }, { "label": "Files", "value": "Two safetensors shards in BF16 (7.51 GB) with chat_template.jinja, config, processor and tokenizer files, and an Ollama Modelfile. No pickle files and no Python. Hugging Face lists a SHA-256 for the large files" }, { "label": "Licence", "value": "None stated. The card's metadata has no licence field, its text doesn't mention one, and there's no LICENSE file here or in Nanonets' GitHub repository for the model. The base model is under the Qwen Research licence. Nanonets' Docstrange library is MIT, which covers that code, not these weights" }, { "label": "Runtimes", "value": "The card documents Transformers and vLLM. Released Transformers 5.22.2, vLLM 0.33.0 and SGLang 0.5.23 carry the Qwen2.5-VL architecture. llama.cpp can convert it, but Nanonets publishes no GGUF" }, { "label": "Input and output", "value": "One page image with a text prompt. The card's prompt asks for natural reading order, tables in HTML, equations in LaTeX, image captions or descriptions in img tags, watermarks and page numbers in their own tags and ☐ and ☑ for checkboxes. A second prompt on the card is for financial documents" }, { "label": "Recommended settings", "value": "temperature 0 through vLLM with up to 15,000 output tokens, and no sampling in Transformers. The card says a higher image resolution helps, and repetition_penalty 1 for complex tables. generation_config.json sets repetition_penalty 1.05" }, { "label": "Hardware", "value": "No memory figure on the card. The files are 7.51 GB in BF16, and the card's example uses flash attention on a GPU" }, { "label": "Training data", "value": "Two lines on the card: trained on handwritten documents across multiple languages, and on documents in the languages it names. No sources, sizes or cut-off" } ], "provenance": { "legalEntity": "Nano Net Technologies Inc.", "domain": "nanonets.com", "domainRegistered": "2005-10-15", "endpointOnVendorDomain": null, "terms": "", "privacy": "", "statusPage": "", "changelog": "https://huggingface.co/nanonets/Nanonets-OCR2-3B/commits/main", "securityTxt": "none", "checked": "2026-11-10", "notes": [ "The legal entity and the domain's registration date are carried from our Nanonets listing.", "Nothing is hosted under this listing and the weights state no licence, so there are no terms to read and no privacy policy is scored.", "The changelog is the commit history, 8 commits from 13 to 16 October 2025.", "Nanonets' own terms and privacy policy cover its hosted service, not these weights." ], "score": 78, "checks": [ { "check": "Legal entity named", "value": "Nano Net Technologies Inc.", "points": 20, "max": 20, "state": "ok" }, { "check": "Domain age", "value": "nanonets.com, registered 2005-10-15 (21 years)", "points": 15, "max": 15, "state": "ok" }, { "check": "Endpoint on the vendor's domain", "value": "no hosted endpoint", "points": 0, "max": 0, "state": "na" }, { "check": "Terms of service", "value": "nothing hosted, so the None stated (the base model, Qwen2.5-VL-3B-Instruct, is under the Qwen Research licence) licence stands in", "points": 10, "max": 10, "state": "ok" }, { "check": "Privacy policy", "value": "nothing hosted, not scored", "points": 0, "max": 0, "state": "na" }, { "check": "Status page", "value": "not found", "points": 0, "max": 10, "state": "no" }, { "check": "Changelog", "value": "published", "points": 10, "max": 10, "state": "ok" }, { "check": "security.txt", "value": "not found", "points": 0, "max": 10, "state": "no" }, { "check": "People named", "value": "2 named with their role, 1 with a public profile", "points": 10, "max": 10, "state": "ok" }, { "check": "Support route", "value": "help pages", "points": 5, "max": 5, "state": "ok" } ] }, "humans": { "slug": "nanonets-ocr2-3b", "checked": "2026-11-10", "company": "Nanonets", "people": [ { "name": "Sarthak Jain", "role": "Co-founder and CEO", "founder": true, "photo": "/assets/img/people/nanonets-sarthak-jain.webp", "photoSource": "https://www.ycombinator.com/companies/nanonets", "bio": "Sarthak Jain is co-founder and CEO of Nanonets, an AI data extraction company from Y Combinator's Winter 2017 batch. His YC profile lists earlier work at Myntra, Cubeit, Johns Hopkins University, KPMG and Hewlett-Packard, and studies at IIT Gandhinagar.", "bioSources": [ "https://www.ycombinator.com/companies/nanonets", "https://www.ycombinator.com/founders/sarthak-jain" ], "page": "/people/sarthak-jain/" }, { "name": "Prathamesh Juvatkar", "role": "Co-founder and CTO", "founder": true, "linkedin": "https://www.linkedin.com/in/prathameshjuvatkar", "x": "https://x.com/PJuvatkar", "photo": "/assets/img/people/nanonets-prathamesh-juvatkar.webp", "photoSource": "https://www.ycombinator.com/companies/nanonets", "bio": "Prathamesh Juvatkar is co-founder and CTO of Nanonets, an AI data extraction company from Y Combinator's Winter 2017 batch, where he oversees technology and marketing. He works on deep learning and software architecture, and his YC profile lists earlier roles at Myntra, Cubeit, Caltech and Marvell.", "bioSources": [ "https://www.ycombinator.com/companies/nanonets", "https://www.ycombinator.com/founders/prathamesh-juvatkar" ], "page": "/people/prathamesh-juvatkar/" } ], "peopleNote": "Nanonets's Y Combinator page, read 29 October 2026, lists Sarthak Jain (founder and CEO) and Prathamesh Juvatkar (founder and CTO, overseeing technology and marketing) as its active founders, and links Prathamesh Juvatkar's LinkedIn and X accounts. Nanonets's own about page names no one. No page read names a head of product.", "companySocial": { "linkedin": "https://in.linkedin.com/company/nanonets", "x": "https://x.com/nanonets_" }, "support": { "urls": [ "https://docs.nanonets.com/docs/raise-support-ticket" ] }, "sources": [ "https://www.ycombinator.com/companies/nanonets", "https://www.ycombinator.com/founders/162753-sarthak-jain", "https://in.linkedin.com/in/jainsarthak", "https://www.ycombinator.com/founders/155265-prathamesh-juvatkar", "https://www.linkedin.com/in/prathameshjuvatkar", "https://x.com/PJuvatkar", "https://docs.nanonets.com/docs/support-and-availability", "https://nanonets.com/", "https://nanonets.com" ], "ownership": { "status": "private", "totalRaised": { "amount": "$40M", "asOf": "2024-03", "source": "https://india.entrepreneur.com/news-and-trends/workflow-automation-platform-nanonets-raises-usd-29-mn-in/471069" }, "checked": "2026-10-29", "sources": [ "https://nanonets.com/about", "https://www.ycombinator.com/companies/nanonets", "https://india.entrepreneur.com/news-and-trends/workflow-automation-platform-nanonets-raises-usd-29-mn-in/471069" ] }, "investors": [ { "name": "Y Combinator", "kind": "accelerator", "rounds": [ "Winter 2017 batch", "Series B, March 2024" ], "url": "https://www.ycombinator.com", "source": "https://www.ycombinator.com/companies/nanonets", "quote": "Batch: Winter 2017", "page": "/investors/y-combinator/" }, { "name": "Accel", "kind": "vc", "rounds": [ "Series B, March 2024" ], "lead": true, "url": "https://www.accel.com", "source": "https://india.entrepreneur.com/news-and-trends/workflow-automation-platform-nanonets-raises-usd-29-mn-in/471069", "quote": "AI-based workflow automation platform Nanonets has raised USD 29 million in a Series B funding round led by Accel. Existing investors Elevation Capital, YCombinator and others also participated in the round.", "page": "/investors/accel/" }, { "name": "Elevation Capital", "kind": "vc", "rounds": [ "Series A, 2022", "Series B, March 2024" ], "lead": true, "url": "https://www.elevationcapital.com", "source": "https://india.entrepreneur.com/news-and-trends/workflow-automation-platform-nanonets-raises-usd-29-mn-in/471069", "quote": "The firm raised USD 10 million in 2022 in a Series A funding round led by Elevation Capital. Existing investors Elevation Capital, YCombinator and others also participated in the round.", "page": "/investors/elevation-capital/" }, { "name": "SV Angel", "kind": "vc", "source": "https://nanonets.com/about", "quote": "Backed by (logo: SVAngel)", "page": "/investors/sv-angel/" }, { "name": "Avalon Ventures", "kind": "vc", "source": "https://nanonets.com/about", "quote": "Backed by (logo: Avalon Ventures)", "page": "/investors/avalon-ventures/" }, { "name": "FundersClub", "kind": "vc", "source": "https://nanonets.com/about", "quote": "Backed by (logo: FundersClub)", "page": "/investors/fundersclub/" }, { "name": "Soma Capital", "kind": "vc", "source": "https://nanonets.com/about", "quote": "Backed by (logo: Soma Capital)", "page": "/investors/soma-capital/" } ] }, "pageJsonUrl": "https://www.anchorterminal.com/tools/nanonets-ocr2-3b.json" } }