AI-Powered<\/h1> Data Extraction<\/h1><\/div>[\/vc_column_text][vc_empty_space height=”20px”][vc_column_text]Automate your data entry by extracting and labeling text from virtually any document. Structured or unstructured, print or handwriting, 1 day old to 100 years old.<\/div>[\/vc_column_text]Tell us about your text<\/a>[\/vc_column_inner][vc_column_inner width=”1\/2″ css=”.vc_custom_1442924310657{padding-top: 20px !important;}”][vc_single_image image=”3560″ img_size=”full” alignment=”right”][\/vc_column_inner][\/vc_row_inner][\/vc_column][\/vc_row][vc_row css=”.vc_custom_1440055062628{margin-bottom: 0px !important;}”][vc_column width=”2\/3″ column_margin_top=”20px”]What Can You Extract?<\/h3><\/div><\/div>[vc_empty_space height=”20px”][vc_column_text]Tolstoy’s Data Extractor extracts and labels text from virtually any document or record.<\/p>\nUnlike other solutions, we customize an Extractor for each client. Because of this, we are able to process highly unstructured documents — from legal transcripts, to email data, to handwritten records.[\/vc_column_text][\/vc_column][vc_column width=”1\/3″][\/vc_column][\/vc_row][vc_row full_width=”stretch_row”][vc_column width=”1\/4″ column_margin_top=”10px”]
<\/i>OCR<\/h3>
Print and handwriting, structured or unstructured text.<\/p>\n<\/div>[\/vc_column][vc_column width=”1\/4″ column_margin_top=”10px”]
<\/i>Custom Tagging<\/h3>
Find and label custom types of text. Names, addresses, document numbers, dates, locations, industry-specific terms, etc.<\/p>\n<\/div>[\/vc_column][vc_column width=”1\/4″ column_margin_top=”10px”]
<\/i>Extract<\/h3>
Extract text from unstructured documents into a structured output: CSV, XLSX, JSON, or other structured format.<\/p>\n<\/div>[\/vc_column][vc_column width=”1\/4″ column_margin_top=”10px”]
<\/i>Classify<\/h3>
Classify documents, emails, and other records with custom categories.<\/p>\n<\/div>[\/vc_column][\/vc_row][vc_row full_width=”stretch_row” css=”.vc_custom_1604442225092{background-color: #f9f9f9 !important;}”][vc_column][vc_empty_space height=”30px”][\/vc_column][\/vc_row][vc_row full_width=”stretch_row” css=”.vc_custom_1604442229751{background-color: #f9f9f9 !important;}”][vc_column column_padding_top=”30px”]
Customer Success<\/h3><\/div><\/div>[\/vc_column][\/vc_row][vc_row full_width=”stretch_row” css=”.vc_custom_1604347055001{background-color: #f9f9f9 !important;}”][vc_column width=”1\/3″ column_margin_top=”50px” column_padding_top=”20px”]![\"grid02\"\/](\"https:\/\/staging.tolstoy-ai.com\/wp-content\/uploads\/2020\/06\/alessio-fiorentino-MiNq1Mjikfw-unsplash-1-scaled.jpg\")
<\/div><\/div>Legal Transcripts<\/h2><\/div><\/div>Extract document references from trial dialogue.<\/p><\/div><\/div>[\/vc_column][vc_column width=”1\/3″ column_margin_top=”50px” column_padding_top=”20px”]
Entity Extraction<\/h4><\/div><\/div>[vc_empty_space height=”20px”][vc_column_text]- Who:<\/span>University in Cambridge, MA<\/a><\/li>
- What:<\/span>Legal transcripts<\/a><\/li>
- How many:<\/span>150,000+ pages<\/a><\/li><\/ul><\/div>[\/vc_column_text][\/vc_column][vc_column width=”1\/3″ column_margin_top=”50px” column_padding_top=”20px”][vc_column_text]The university digitized a collection of one million documents from the Nuremberg War Crimes Trial. The contents of the entire collection was unknown, so the university partnered with Tolstoy to help index the documents. This was done by identifying all document references in their 150,000+ pages of court transcripts.<\/p>\n
Tolstoy tagged the references with 99%+ accuracy<\/strong>. This saved them approx. 7-8 months of work<\/strong>.<\/p>\nRead more<\/a>[\/vc_column_text][\/vc_column][\/vc_row][vc_row full_width=”stretch_row” css=”.vc_custom_1604347055001{background-color: #f9f9f9 !important;}”][vc_column width=”1\/3″ column_margin_top=”50px”]![\"grid02\"\/](\"https:\/\/staging.tolstoy-ai.com\/wp-content\/uploads\/2020\/06\/pages-1237059-1920x1440-1.jpg\")
<\/div><\/div>News clippings<\/h2><\/div><\/div>Extract text from old news articles.<\/p><\/div><\/div>[\/vc_column][vc_column width=”1\/3″ column_margin_top=”50px”]
Custom OCR<\/h4><\/div><\/div>[vc_empty_space height=”20px”][vc_column_text]- Who:<\/span>The Wall Street Journal<\/a><\/li>
- What:<\/span>News Clippings<\/a><\/li>
- How old:<\/span>130 years old<\/a><\/li><\/ul><\/div>[\/vc_column_text][\/vc_column][vc_column width=”1\/3″ column_margin_top=”50px”][vc_column_text]To celebrate their 130th anniversary, The Wall Street Journal<\/em> wanted to digitize and reprint articles from their entire history in a special edition. Since many of the articles were very old, traditional OCR software did not pick up the text well or at all.<\/p>\n
We wrote a custom OCR script that parsed their digitized articles with 95%+ accuracy<\/strong>. This saved them several weeks of manual transcription.<\/p>\n"Read<\/a>[\/vc_column_text][\/vc_column][\/vc_row][vc_row full_width="stretch_row" css=".vc_custom_1604088796002{background-color: #f9f9f9 !important;}"][vc_column width="1\/3" column_margin_top="50px" column_margin_bottom="30px"]"5225"<\/div><\/div>"Maintenance<\/h2><\/div><\/div>"Extract<\/p><\/div><\/div>[\/vc_column][vc_column width="1\/3" column_margin_top="50px" column_margin_bottom="30px"]
<"h4" class=\"jt-main-head\" style=\"font-weight:"";\">"OCR<\/"h4"><\/div><\/div>[vc_empty_space height="20px"][vc_column_text]- "Who":<\/span>"New<\/a><\/li>
- "What":<\/span>"Maintenance<\/a><\/li>
- "Fields:<\/span>"2,000+"<\/a><\/li><\/ul><\/div>[\/vc_column_text][\/vc_column][vc_column width="1\/3" column_margin_top="50px" column_margin_bottom="30px"][vc_column_text]The New York Power Authority (NYPA) has a critical, 660-megawatt power cable that experienced multiple faults, taking it out of service for 1 to 2 months at a time.<\/p>\n
With only disconnected paper documents, NYPA needed one holistic and connected view of the data to properly assess the situation.<\/p>\n
Using a customized AI text extraction model, Tolstoy digitized the documents into an Excel database that allowed NYPA to easily view and evaluate the information to determine critical next steps.<\/p>\n
"Read<\/a>[\/vc_column_text][\/vc_column][\/vc_row][vc_row full_width="stretch_row" css=".vc_custom_1604088796002{background-color: #f9f9f9 !important;}"][vc_column width="1\/3" column_margin_top="50px" column_margin_bottom="30px"]"1238"<\/div><\/div>"Museum<\/h2><\/div><\/div>"Extract<\/p><\/div><\/div>[\/vc_column][vc_column width="1\/3" column_margin_top="50px" column_margin_bottom="30px"]
<"h4" class=\"jt-main-head\" style=\"font-weight:"";\">"OCR<\/"h4"><\/div><\/div>[vc_empty_space height="20px"][vc_column_text]- "Who":<\/span>"Museums<\/a><\/li>
- "What":<\/span>"Specimen<\/a><\/li>
- "How:<\/span>"10,000+<\/a><\/li><\/ul><\/div>[\/vc_column_text][\/vc_column][vc_column width="1\/3" column_margin_top="50px" column_margin_bottom="30px"][vc_column_text]Museums across the world are digitizing their records. This includes extracting text from specimen labels into databases. The labels are often handwritten, with an unknown number of formats. The labels come from various eras, countries, and institutions.<\/p>\n
Tolstoy built an Extractor that processes print labels with 98%+ accuracy<\/strong>, and handwritten labels with ~80% accuracy, regardless of the format.\u00a0<\/strong>This project is ongoing and has the potential to reduce processing times from decades to weeks.[\/vc_column_text][\/vc_column][\/vc_row][vc_row][vc_column][vc_empty_space height=”30px”][\/vc_column][\/vc_row][vc_row][vc_column][vc_empty_space height=”30px”][\/vc_column][\/vc_row][vc_row full_width=”stretch_row”][vc_column column_margin_bottom=”20px”]What types of text can be processed?<\/h3><\/div><\/div>[\/vc_column][\/vc_row][vc_row][vc_column width=”1\/2″ column_padding_top=”30px”][vc_column_text]<\/p>\n<\/h4>\n\n- \n
Reports<\/strong><\/h4>\n<\/li>\n- \n
Transcripts<\/strong><\/h4>\n<\/li>\n- \n
Forms<\/strong><\/h4>\n<\/li>\n- \n
Receipts<\/strong><\/h4>\n<\/li>\n- \n
Handwritten labels<\/strong><\/h4>\n<\/li>\n- \n
News articles<\/strong><\/h4>\n<\/li>\n- \n
and more<\/strong><\/h4>\n<\/li>\n<\/ul>\n[\/vc_column_text][\/vc_column][vc_column width=”1\/2″ column_margin_bottom=”30px” column_padding_bottom=”30px” column_padding_left=”100px” column_padding_right=”100px”]
\n ![\"\"](\"https:\/\/staging.tolstoy-ai.com\/wp-content\/uploads\/2020\/11\/Screen-Shot-2020-11-03-at-3.08.27-PM.png\")
<\/div>\n ![\"\"](\"https:\/\/staging.tolstoy-ai.com\/wp-content\/uploads\/2020\/11\/1664_Reel1_0708.jpg\")
<\/div>\n ![\"\"](\"https:\/\/staging.tolstoy-ai.com\/wp-content\/uploads\/2021\/04\/170px-ReceiptSwiss.jpeg\")
<\/div>\n ![\"\"](\"https:\/\/staging.tolstoy-ai.com\/wp-content\/uploads\/2020\/11\/130standard.may1911-scaled.jpg\")
<\/div>\n ![\"\"](\"https:\/\/staging.tolstoy-ai.com\/wp-content\/uploads\/2020\/11\/1095_Reel10_0277.jpg\")
<\/div>\n ![\"\"](\"https:\/\/staging.tolstoy-ai.com\/wp-content\/uploads\/2018\/08\/LACMIP_100000-2_Exogyra-parasitica_Ostrea-washingtoni.jpg\")
<\/div>\n ![\"\"](\"https:\/\/staging.tolstoy-ai.com\/wp-content\/uploads\/2018\/08\/03509224-scaled.jpg\")
<\/div>\n <\/div>\n [\/vc_column][\/vc_row][vc_row full_width=”stretch_row” css=”.vc_custom_1604447520083{background-color: #f9f9f9 !important;}”][vc_column column_margin_top=”20px” column_padding_top=”50px” column_padding_bottom=”30px” column_padding_left=”60px” column_padding_right=”60px”]Automate to Accelerate Data Mastery<\/h3><\/div>Unlock the data in your internal documents, client responses, maintenance forms, and other records. Have a truly complete view of your information. Ensure a strong foundation to track, analyze, and run predictive analytics on your data.<\/p>\n
It's your data. Make the best use of it.<\/h4><\/div>[\/vc_column][\/vc_row][vc_row][vc_column column_margin_top=”60px”]
What Can You Extract?<\/h3><\/div><\/div>[vc_empty_space height=”20px”][vc_column_text]Tolstoy’s Data Extractor extracts and labels text from virtually any document or record.<\/p>\nUnlike other solutions, we customize an Extractor for each client. Because of this, we are able to process highly unstructured documents — from legal transcripts, to email data, to handwritten records.[\/vc_column_text][\/vc_column][vc_column width=”1\/3″][\/vc_column][\/vc_row][vc_row full_width=”stretch_row”][vc_column width=”1\/4″ column_margin_top=”10px”]
<\/i>OCR<\/h3>
Print and handwriting, structured or unstructured text.<\/p>\n<\/div>[\/vc_column][vc_column width=”1\/4″ column_margin_top=”10px”]
<\/i>Custom Tagging<\/h3>
Find and label custom types of text. Names, addresses, document numbers, dates, locations, industry-specific terms, etc.<\/p>\n<\/div>[\/vc_column][vc_column width=”1\/4″ column_margin_top=”10px”]
<\/i>Extract<\/h3>
Extract text from unstructured documents into a structured output: CSV, XLSX, JSON, or other structured format.<\/p>\n<\/div>[\/vc_column][vc_column width=”1\/4″ column_margin_top=”10px”]
<\/i>Classify<\/h3>
Classify documents, emails, and other records with custom categories.<\/p>\n<\/div>[\/vc_column][\/vc_row][vc_row full_width=”stretch_row” css=”.vc_custom_1604442225092{background-color: #f9f9f9 !important;}”][vc_column][vc_empty_space height=”30px”][\/vc_column][\/vc_row][vc_row full_width=”stretch_row” css=”.vc_custom_1604442229751{background-color: #f9f9f9 !important;}”][vc_column column_padding_top=”30px”]
Customer Success<\/h3><\/div><\/div>[\/vc_column][\/vc_row][vc_row full_width=”stretch_row” css=”.vc_custom_1604347055001{background-color: #f9f9f9 !important;}”][vc_column width=”1\/3″ column_margin_top=”50px” column_padding_top=”20px”]<\/div><\/div>Legal Transcripts<\/h2><\/div><\/div>Extract document references from trial dialogue.<\/p><\/div><\/div>[\/vc_column][vc_column width=”1\/3″ column_margin_top=”50px” column_padding_top=”20px”]
Entity Extraction<\/h4><\/div><\/div>[vc_empty_space height=”20px”][vc_column_text]- Who:<\/span>University in Cambridge, MA<\/a><\/li>
- What:<\/span>Legal transcripts<\/a><\/li>
- How many:<\/span>150,000+ pages<\/a><\/li><\/ul><\/div>[\/vc_column_text][\/vc_column][vc_column width=”1\/3″ column_margin_top=”50px” column_padding_top=”20px”][vc_column_text]The university digitized a collection of one million documents from the Nuremberg War Crimes Trial. The contents of the entire collection was unknown, so the university partnered with Tolstoy to help index the documents. This was done by identifying all document references in their 150,000+ pages of court transcripts.<\/p>\n
Tolstoy tagged the references with 99%+ accuracy<\/strong>. This saved them approx. 7-8 months of work<\/strong>.<\/p>\nRead more<\/a>[\/vc_column_text][\/vc_column][\/vc_row][vc_row full_width=”stretch_row” css=”.vc_custom_1604347055001{background-color: #f9f9f9 !important;}”][vc_column width=”1\/3″ column_margin_top=”50px”]<\/div><\/div>News clippings<\/h2><\/div><\/div>Extract text from old news articles.<\/p><\/div><\/div>[\/vc_column][vc_column width=”1\/3″ column_margin_top=”50px”]
Custom OCR<\/h4><\/div><\/div>[vc_empty_space height=”20px”][vc_column_text]- Who:<\/span>The Wall Street Journal<\/a><\/li>
- What:<\/span>News Clippings<\/a><\/li>
- How old:<\/span>130 years old<\/a><\/li><\/ul><\/div>[\/vc_column_text][\/vc_column][vc_column width=”1\/3″ column_margin_top=”50px”][vc_column_text]To celebrate their 130th anniversary, The Wall Street Journal<\/em> wanted to digitize and reprint articles from their entire history in a special edition. Since many of the articles were very old, traditional OCR software did not pick up the text well or at all.<\/p>\n
We wrote a custom OCR script that parsed their digitized articles with 95%+ accuracy<\/strong>. This saved them several weeks of manual transcription.<\/p>\n"Read<\/a>[\/vc_column_text][\/vc_column][\/vc_row][vc_row full_width="stretch_row" css=".vc_custom_1604088796002{background-color: #f9f9f9 !important;}"][vc_column width="1\/3" column_margin_top="50px" column_margin_bottom="30px"]"5225"<\/div><\/div>"Maintenance<\/h2><\/div><\/div>"Extract<\/p><\/div><\/div>[\/vc_column][vc_column width="1\/3" column_margin_top="50px" column_margin_bottom="30px"]
<"h4" class=\"jt-main-head\" style=\"font-weight:"";\">"OCR<\/"h4"><\/div><\/div>[vc_empty_space height="20px"][vc_column_text]- "Who":<\/span>"New<\/a><\/li>
- "What":<\/span>"Maintenance<\/a><\/li>
- "Fields:<\/span>"2,000+"<\/a><\/li><\/ul><\/div>[\/vc_column_text][\/vc_column][vc_column width="1\/3" column_margin_top="50px" column_margin_bottom="30px"][vc_column_text]The New York Power Authority (NYPA) has a critical, 660-megawatt power cable that experienced multiple faults, taking it out of service for 1 to 2 months at a time.<\/p>\n
With only disconnected paper documents, NYPA needed one holistic and connected view of the data to properly assess the situation.<\/p>\n
Using a customized AI text extraction model, Tolstoy digitized the documents into an Excel database that allowed NYPA to easily view and evaluate the information to determine critical next steps.<\/p>\n
"Read<\/a>[\/vc_column_text][\/vc_column][\/vc_row][vc_row full_width="stretch_row" css=".vc_custom_1604088796002{background-color: #f9f9f9 !important;}"][vc_column width="1\/3" column_margin_top="50px" column_margin_bottom="30px"]"1238"<\/div><\/div>"Museum<\/h2><\/div><\/div>"Extract<\/p><\/div><\/div>[\/vc_column][vc_column width="1\/3" column_margin_top="50px" column_margin_bottom="30px"]
<"h4" class=\"jt-main-head\" style=\"font-weight:"";\">"OCR<\/"h4"><\/div><\/div>[vc_empty_space height="20px"][vc_column_text]- "Who":<\/span>"Museums<\/a><\/li>
- "What":<\/span>"Specimen<\/a><\/li>
- "How:<\/span>"10,000+<\/a><\/li><\/ul><\/div>[\/vc_column_text][\/vc_column][vc_column width="1\/3" column_margin_top="50px" column_margin_bottom="30px"][vc_column_text]Museums across the world are digitizing their records. This includes extracting text from specimen labels into databases. The labels are often handwritten, with an unknown number of formats. The labels come from various eras, countries, and institutions.<\/p>\n
Tolstoy built an Extractor that processes print labels with 98%+ accuracy<\/strong>, and handwritten labels with ~80% accuracy, regardless of the format.\u00a0<\/strong>This project is ongoing and has the potential to reduce processing times from decades to weeks.[\/vc_column_text][\/vc_column][\/vc_row][vc_row][vc_column][vc_empty_space height=”30px”][\/vc_column][\/vc_row][vc_row][vc_column][vc_empty_space height=”30px”][\/vc_column][\/vc_row][vc_row full_width=”stretch_row”][vc_column column_margin_bottom=”20px”]What types of text can be processed?<\/h3><\/div><\/div>[\/vc_column][\/vc_row][vc_row][vc_column width=”1\/2″ column_padding_top=”30px”][vc_column_text]<\/p>\n<\/h4>\n\n- \n
Reports<\/strong><\/h4>\n<\/li>\n- \n
Transcripts<\/strong><\/h4>\n<\/li>\n- \n
Forms<\/strong><\/h4>\n<\/li>\n- \n
Receipts<\/strong><\/h4>\n<\/li>\n- \n
Handwritten labels<\/strong><\/h4>\n<\/li>\n- \n
News articles<\/strong><\/h4>\n<\/li>\n- \n
and more<\/strong><\/h4>\n<\/li>\n<\/ul>\n[\/vc_column_text][\/vc_column][vc_column width=”1\/2″ column_margin_bottom=”30px” column_padding_bottom=”30px” column_padding_left=”100px” column_padding_right=”100px”]
\n <\/div>\n <\/div>\n <\/div>\n <\/div>\n <\/div>\n <\/div>\n <\/div>\n <\/div>\n [\/vc_column][\/vc_row][vc_row full_width=”stretch_row” css=”.vc_custom_1604447520083{background-color: #f9f9f9 !important;}”][vc_column column_margin_top=”20px” column_padding_top=”50px” column_padding_bottom=”30px” column_padding_left=”60px” column_padding_right=”60px”]Automate to Accelerate Data Mastery<\/h3><\/div>Unlock the data in your internal documents, client responses, maintenance forms, and other records. Have a truly complete view of your information. Ensure a strong foundation to track, analyze, and run predictive analytics on your data.<\/p>\n
It's your data. Make the best use of it.<\/h4><\/div>[\/vc_column][\/vc_row][vc_row][vc_column column_margin_top=”60px”]
Unlike other solutions, we customize an Extractor for each client. Because of this, we are able to process highly unstructured documents — from legal transcripts, to email data, to handwritten records.[\/vc_column_text][\/vc_column][vc_column width=”1\/3″][\/vc_column][\/vc_row][vc_row full_width=”stretch_row”][vc_column width=”1\/4″ column_margin_top=”10px”]
OCR<\/h3>
Print and handwriting, structured or unstructured text.<\/p>\n<\/div>[\/vc_column][vc_column width=”1\/4″ column_margin_top=”10px”]
Custom Tagging<\/h3>
Find and label custom types of text. Names, addresses, document numbers, dates, locations, industry-specific terms, etc.<\/p>\n<\/div>[\/vc_column][vc_column width=”1\/4″ column_margin_top=”10px”]
Extract<\/h3>
Extract text from unstructured documents into a structured output: CSV, XLSX, JSON, or other structured format.<\/p>\n<\/div>[\/vc_column][vc_column width=”1\/4″ column_margin_top=”10px”]
Classify<\/h3>
Classify documents, emails, and other records with custom categories.<\/p>\n<\/div>[\/vc_column][\/vc_row][vc_row full_width=”stretch_row” css=”.vc_custom_1604442225092{background-color: #f9f9f9 !important;}”][vc_column][vc_empty_space height=”30px”][\/vc_column][\/vc_row][vc_row full_width=”stretch_row” css=”.vc_custom_1604442229751{background-color: #f9f9f9 !important;}”][vc_column column_padding_top=”30px”]
Customer Success<\/h3><\/div><\/div>[\/vc_column][\/vc_row][vc_row full_width=”stretch_row” css=”.vc_custom_1604347055001{background-color: #f9f9f9 !important;}”][vc_column width=”1\/3″ column_margin_top=”50px” column_padding_top=”20px”]<\/div><\/div>Legal Transcripts<\/h2><\/div><\/div>Extract document references from trial dialogue.<\/p><\/div><\/div>[\/vc_column][vc_column width=”1\/3″ column_margin_top=”50px” column_padding_top=”20px”]
Entity Extraction<\/h4><\/div><\/div>[vc_empty_space height=”20px”][vc_column_text]- Who:<\/span>University in Cambridge, MA<\/a><\/li>
- What:<\/span>Legal transcripts<\/a><\/li>
- How many:<\/span>150,000+ pages<\/a><\/li><\/ul><\/div>[\/vc_column_text][\/vc_column][vc_column width=”1\/3″ column_margin_top=”50px” column_padding_top=”20px”][vc_column_text]The university digitized a collection of one million documents from the Nuremberg War Crimes Trial. The contents of the entire collection was unknown, so the university partnered with Tolstoy to help index the documents. This was done by identifying all document references in their 150,000+ pages of court transcripts.<\/p>\n
Tolstoy tagged the references with 99%+ accuracy<\/strong>. This saved them approx. 7-8 months of work<\/strong>.<\/p>\nRead more<\/a>[\/vc_column_text][\/vc_column][\/vc_row][vc_row full_width=”stretch_row” css=”.vc_custom_1604347055001{background-color: #f9f9f9 !important;}”][vc_column width=”1\/3″ column_margin_top=”50px”]<\/div><\/div>News clippings<\/h2><\/div><\/div>Extract text from old news articles.<\/p><\/div><\/div>[\/vc_column][vc_column width=”1\/3″ column_margin_top=”50px”]
Custom OCR<\/h4><\/div><\/div>[vc_empty_space height=”20px”][vc_column_text]- Who:<\/span>The Wall Street Journal<\/a><\/li>
- What:<\/span>News Clippings<\/a><\/li>
- How old:<\/span>130 years old<\/a><\/li><\/ul><\/div>[\/vc_column_text][\/vc_column][vc_column width=”1\/3″ column_margin_top=”50px”][vc_column_text]To celebrate their 130th anniversary, The Wall Street Journal<\/em> wanted to digitize and reprint articles from their entire history in a special edition. Since many of the articles were very old, traditional OCR software did not pick up the text well or at all.<\/p>\n
We wrote a custom OCR script that parsed their digitized articles with 95%+ accuracy<\/strong>. This saved them several weeks of manual transcription.<\/p>\n"Read<\/a>[\/vc_column_text][\/vc_column][\/vc_row][vc_row full_width="stretch_row" css=".vc_custom_1604088796002{background-color: #f9f9f9 !important;}"][vc_column width="1\/3" column_margin_top="50px" column_margin_bottom="30px"]"5225"<\/div><\/div>"Maintenance<\/h2><\/div><\/div>"Extract<\/p><\/div><\/div>[\/vc_column][vc_column width="1\/3" column_margin_top="50px" column_margin_bottom="30px"]
<"h4" class=\"jt-main-head\" style=\"font-weight:"";\">"OCR<\/"h4"><\/div><\/div>[vc_empty_space height="20px"][vc_column_text]- "Who":<\/span>"New<\/a><\/li>
- "What":<\/span>"Maintenance<\/a><\/li>
- "Fields:<\/span>"2,000+"<\/a><\/li><\/ul><\/div>[\/vc_column_text][\/vc_column][vc_column width="1\/3" column_margin_top="50px" column_margin_bottom="30px"][vc_column_text]The New York Power Authority (NYPA) has a critical, 660-megawatt power cable that experienced multiple faults, taking it out of service for 1 to 2 months at a time.<\/p>\n
With only disconnected paper documents, NYPA needed one holistic and connected view of the data to properly assess the situation.<\/p>\n
Using a customized AI text extraction model, Tolstoy digitized the documents into an Excel database that allowed NYPA to easily view and evaluate the information to determine critical next steps.<\/p>\n
"Read<\/a>[\/vc_column_text][\/vc_column][\/vc_row][vc_row full_width="stretch_row" css=".vc_custom_1604088796002{background-color: #f9f9f9 !important;}"][vc_column width="1\/3" column_margin_top="50px" column_margin_bottom="30px"]"1238"<\/div><\/div>"Museum<\/h2><\/div><\/div>"Extract<\/p><\/div><\/div>[\/vc_column][vc_column width="1\/3" column_margin_top="50px" column_margin_bottom="30px"]
<"h4" class=\"jt-main-head\" style=\"font-weight:"";\">"OCR<\/"h4"><\/div><\/div>[vc_empty_space height="20px"][vc_column_text]- "Who":<\/span>"Museums<\/a><\/li>
- "What":<\/span>"Specimen<\/a><\/li>
- "How:<\/span>"10,000+<\/a><\/li><\/ul><\/div>[\/vc_column_text][\/vc_column][vc_column width="1\/3" column_margin_top="50px" column_margin_bottom="30px"][vc_column_text]Museums across the world are digitizing their records. This includes extracting text from specimen labels into databases. The labels are often handwritten, with an unknown number of formats. The labels come from various eras, countries, and institutions.<\/p>\n
Tolstoy built an Extractor that processes print labels with 98%+ accuracy<\/strong>, and handwritten labels with ~80% accuracy, regardless of the format.\u00a0<\/strong>This project is ongoing and has the potential to reduce processing times from decades to weeks.[\/vc_column_text][\/vc_column][\/vc_row][vc_row][vc_column][vc_empty_space height=”30px”][\/vc_column][\/vc_row][vc_row][vc_column][vc_empty_space height=”30px”][\/vc_column][\/vc_row][vc_row full_width=”stretch_row”][vc_column column_margin_bottom=”20px”]What types of text can be processed?<\/h3><\/div><\/div>[\/vc_column][\/vc_row][vc_row][vc_column width=”1\/2″ column_padding_top=”30px”][vc_column_text]<\/p>\n<\/h4>\n\n- \n
Reports<\/strong><\/h4>\n<\/li>\n- \n
Transcripts<\/strong><\/h4>\n<\/li>\n- \n
Forms<\/strong><\/h4>\n<\/li>\n- \n
Receipts<\/strong><\/h4>\n<\/li>\n- \n
Handwritten labels<\/strong><\/h4>\n<\/li>\n- \n
News articles<\/strong><\/h4>\n<\/li>\n- \n
and more<\/strong><\/h4>\n<\/li>\n<\/ul>\n[\/vc_column_text][\/vc_column][vc_column width=”1\/2″ column_margin_bottom=”30px” column_padding_bottom=”30px” column_padding_left=”100px” column_padding_right=”100px”]
\n <\/div>\n <\/div>\n <\/div>\n <\/div>\n <\/div>\n <\/div>\n <\/div>\n <\/div>\n [\/vc_column][\/vc_row][vc_row full_width=”stretch_row” css=”.vc_custom_1604447520083{background-color: #f9f9f9 !important;}”][vc_column column_margin_top=”20px” column_padding_top=”50px” column_padding_bottom=”30px” column_padding_left=”60px” column_padding_right=”60px”]Automate to Accelerate Data Mastery<\/h3><\/div>Unlock the data in your internal documents, client responses, maintenance forms, and other records. Have a truly complete view of your information. Ensure a strong foundation to track, analyze, and run predictive analytics on your data.<\/p>\n
It's your data. Make the best use of it.<\/h4><\/div>[\/vc_column][\/vc_row][vc_row][vc_column column_margin_top=”60px”]
<\/div><\/div>Legal Transcripts<\/h2><\/div><\/div>Extract document references from trial dialogue.<\/p><\/div><\/div>[\/vc_column][vc_column width=”1\/3″ column_margin_top=”50px” column_padding_top=”20px”]
Entity Extraction<\/h4><\/div><\/div>[vc_empty_space height=”20px”][vc_column_text]- Who:<\/span>University in Cambridge, MA<\/a><\/li>
- What:<\/span>Legal transcripts<\/a><\/li>
- How many:<\/span>150,000+ pages<\/a><\/li><\/ul><\/div>[\/vc_column_text][\/vc_column][vc_column width=”1\/3″ column_margin_top=”50px” column_padding_top=”20px”][vc_column_text]The university digitized a collection of one million documents from the Nuremberg War Crimes Trial. The contents of the entire collection was unknown, so the university partnered with Tolstoy to help index the documents. This was done by identifying all document references in their 150,000+ pages of court transcripts.<\/p>\n
Tolstoy tagged the references with 99%+ accuracy<\/strong>. This saved them approx. 7-8 months of work<\/strong>.<\/p>\nRead more<\/a>[\/vc_column_text][\/vc_column][\/vc_row][vc_row full_width=”stretch_row” css=”.vc_custom_1604347055001{background-color: #f9f9f9 !important;}”][vc_column width=”1\/3″ column_margin_top=”50px”]<\/div><\/div>News clippings<\/h2><\/div><\/div>Extract text from old news articles.<\/p><\/div><\/div>[\/vc_column][vc_column width=”1\/3″ column_margin_top=”50px”]
Custom OCR<\/h4><\/div><\/div>[vc_empty_space height=”20px”][vc_column_text]- Who:<\/span>The Wall Street Journal<\/a><\/li>
- What:<\/span>News Clippings<\/a><\/li>
- How old:<\/span>130 years old<\/a><\/li><\/ul><\/div>[\/vc_column_text][\/vc_column][vc_column width=”1\/3″ column_margin_top=”50px”][vc_column_text]To celebrate their 130th anniversary, The Wall Street Journal<\/em> wanted to digitize and reprint articles from their entire history in a special edition. Since many of the articles were very old, traditional OCR software did not pick up the text well or at all.<\/p>\n
We wrote a custom OCR script that parsed their digitized articles with 95%+ accuracy<\/strong>. This saved them several weeks of manual transcription.<\/p>\n"Read<\/a>[\/vc_column_text][\/vc_column][\/vc_row][vc_row full_width="stretch_row" css=".vc_custom_1604088796002{background-color: #f9f9f9 !important;}"][vc_column width="1\/3" column_margin_top="50px" column_margin_bottom="30px"]"5225"<\/div><\/div>"Maintenance<\/h2><\/div><\/div>"Extract<\/p><\/div><\/div>[\/vc_column][vc_column width="1\/3" column_margin_top="50px" column_margin_bottom="30px"]
<"h4" class=\"jt-main-head\" style=\"font-weight:"";\">"OCR<\/"h4"><\/div><\/div>[vc_empty_space height="20px"][vc_column_text]- "Who":<\/span>"New<\/a><\/li>
- "What":<\/span>"Maintenance<\/a><\/li>
- "Fields:<\/span>"2,000+"<\/a><\/li><\/ul><\/div>[\/vc_column_text][\/vc_column][vc_column width="1\/3" column_margin_top="50px" column_margin_bottom="30px"][vc_column_text]The New York Power Authority (NYPA) has a critical, 660-megawatt power cable that experienced multiple faults, taking it out of service for 1 to 2 months at a time.<\/p>\n
With only disconnected paper documents, NYPA needed one holistic and connected view of the data to properly assess the situation.<\/p>\n
Using a customized AI text extraction model, Tolstoy digitized the documents into an Excel database that allowed NYPA to easily view and evaluate the information to determine critical next steps.<\/p>\n
"Read<\/a>[\/vc_column_text][\/vc_column][\/vc_row][vc_row full_width="stretch_row" css=".vc_custom_1604088796002{background-color: #f9f9f9 !important;}"][vc_column width="1\/3" column_margin_top="50px" column_margin_bottom="30px"]"1238"<\/div><\/div>"Museum<\/h2><\/div><\/div>"Extract<\/p><\/div><\/div>[\/vc_column][vc_column width="1\/3" column_margin_top="50px" column_margin_bottom="30px"]
<"h4" class=\"jt-main-head\" style=\"font-weight:"";\">"OCR<\/"h4"><\/div><\/div>[vc_empty_space height="20px"][vc_column_text]- "Who":<\/span>"Museums<\/a><\/li>
- "What":<\/span>"Specimen<\/a><\/li>
- "How:<\/span>"10,000+<\/a><\/li><\/ul><\/div>[\/vc_column_text][\/vc_column][vc_column width="1\/3" column_margin_top="50px" column_margin_bottom="30px"][vc_column_text]Museums across the world are digitizing their records. This includes extracting text from specimen labels into databases. The labels are often handwritten, with an unknown number of formats. The labels come from various eras, countries, and institutions.<\/p>\n
Tolstoy built an Extractor that processes print labels with 98%+ accuracy<\/strong>, and handwritten labels with ~80% accuracy, regardless of the format.\u00a0<\/strong>This project is ongoing and has the potential to reduce processing times from decades to weeks.[\/vc_column_text][\/vc_column][\/vc_row][vc_row][vc_column][vc_empty_space height=”30px”][\/vc_column][\/vc_row][vc_row][vc_column][vc_empty_space height=”30px”][\/vc_column][\/vc_row][vc_row full_width=”stretch_row”][vc_column column_margin_bottom=”20px”]What types of text can be processed?<\/h3><\/div><\/div>[\/vc_column][\/vc_row][vc_row][vc_column width=”1\/2″ column_padding_top=”30px”][vc_column_text]<\/p>\n<\/h4>\n\n- \n
Reports<\/strong><\/h4>\n<\/li>\n- \n
Transcripts<\/strong><\/h4>\n<\/li>\n- \n
Forms<\/strong><\/h4>\n<\/li>\n- \n
Receipts<\/strong><\/h4>\n<\/li>\n- \n
Handwritten labels<\/strong><\/h4>\n<\/li>\n- \n
News articles<\/strong><\/h4>\n<\/li>\n- \n
and more<\/strong><\/h4>\n<\/li>\n<\/ul>\n[\/vc_column_text][\/vc_column][vc_column width=”1\/2″ column_margin_bottom=”30px” column_padding_bottom=”30px” column_padding_left=”100px” column_padding_right=”100px”]
\n <\/div>\n <\/div>\n <\/div>\n <\/div>\n <\/div>\n <\/div>\n <\/div>\n <\/div>\n [\/vc_column][\/vc_row][vc_row full_width=”stretch_row” css=”.vc_custom_1604447520083{background-color: #f9f9f9 !important;}”][vc_column column_margin_top=”20px” column_padding_top=”50px” column_padding_bottom=”30px” column_padding_left=”60px” column_padding_right=”60px”]Automate to Accelerate Data Mastery<\/h3><\/div>Unlock the data in your internal documents, client responses, maintenance forms, and other records. Have a truly complete view of your information. Ensure a strong foundation to track, analyze, and run predictive analytics on your data.<\/p>\n
It's your data. Make the best use of it.<\/h4><\/div>[\/vc_column][\/vc_row][vc_row][vc_column column_margin_top=”60px”]
Extract document references from trial dialogue.<\/p><\/div><\/div>[\/vc_column][vc_column width=”1\/3″ column_margin_top=”50px” column_padding_top=”20px”]
Entity Extraction<\/h4><\/div><\/div>[vc_empty_space height=”20px”][vc_column_text]- Who:<\/span>University in Cambridge, MA<\/a><\/li>
- What:<\/span>Legal transcripts<\/a><\/li>
- How many:<\/span>150,000+ pages<\/a><\/li><\/ul><\/div>[\/vc_column_text][\/vc_column][vc_column width=”1\/3″ column_margin_top=”50px” column_padding_top=”20px”][vc_column_text]The university digitized a collection of one million documents from the Nuremberg War Crimes Trial. The contents of the entire collection was unknown, so the university partnered with Tolstoy to help index the documents. This was done by identifying all document references in their 150,000+ pages of court transcripts.<\/p>\n
Tolstoy tagged the references with 99%+ accuracy<\/strong>. This saved them approx. 7-8 months of work<\/strong>.<\/p>\nRead more<\/a>[\/vc_column_text][\/vc_column][\/vc_row][vc_row full_width=”stretch_row” css=”.vc_custom_1604347055001{background-color: #f9f9f9 !important;}”][vc_column width=”1\/3″ column_margin_top=”50px”]<\/div><\/div>News clippings<\/h2><\/div><\/div>Extract text from old news articles.<\/p><\/div><\/div>[\/vc_column][vc_column width=”1\/3″ column_margin_top=”50px”]
Custom OCR<\/h4><\/div><\/div>[vc_empty_space height=”20px”][vc_column_text]- Who:<\/span>The Wall Street Journal<\/a><\/li>
- What:<\/span>News Clippings<\/a><\/li>
- How old:<\/span>130 years old<\/a><\/li><\/ul><\/div>[\/vc_column_text][\/vc_column][vc_column width=”1\/3″ column_margin_top=”50px”][vc_column_text]To celebrate their 130th anniversary, The Wall Street Journal<\/em> wanted to digitize and reprint articles from their entire history in a special edition. Since many of the articles were very old, traditional OCR software did not pick up the text well or at all.<\/p>\n
We wrote a custom OCR script that parsed their digitized articles with 95%+ accuracy<\/strong>. This saved them several weeks of manual transcription.<\/p>\n"Read<\/a>[\/vc_column_text][\/vc_column][\/vc_row][vc_row full_width="stretch_row" css=".vc_custom_1604088796002{background-color: #f9f9f9 !important;}"][vc_column width="1\/3" column_margin_top="50px" column_margin_bottom="30px"]"5225"<\/div><\/div>"Maintenance<\/h2><\/div><\/div>"Extract<\/p><\/div><\/div>[\/vc_column][vc_column width="1\/3" column_margin_top="50px" column_margin_bottom="30px"]
<"h4" class=\"jt-main-head\" style=\"font-weight:"";\">"OCR<\/"h4"><\/div><\/div>[vc_empty_space height="20px"][vc_column_text]- "Who":<\/span>"New<\/a><\/li>
- "What":<\/span>"Maintenance<\/a><\/li>
- "Fields:<\/span>"2,000+"<\/a><\/li><\/ul><\/div>[\/vc_column_text][\/vc_column][vc_column width="1\/3" column_margin_top="50px" column_margin_bottom="30px"][vc_column_text]The New York Power Authority (NYPA) has a critical, 660-megawatt power cable that experienced multiple faults, taking it out of service for 1 to 2 months at a time.<\/p>\n
With only disconnected paper documents, NYPA needed one holistic and connected view of the data to properly assess the situation.<\/p>\n
Using a customized AI text extraction model, Tolstoy digitized the documents into an Excel database that allowed NYPA to easily view and evaluate the information to determine critical next steps.<\/p>\n
"Read<\/a>[\/vc_column_text][\/vc_column][\/vc_row][vc_row full_width="stretch_row" css=".vc_custom_1604088796002{background-color: #f9f9f9 !important;}"][vc_column width="1\/3" column_margin_top="50px" column_margin_bottom="30px"]"1238"<\/div><\/div>"Museum<\/h2><\/div><\/div>"Extract<\/p><\/div><\/div>[\/vc_column][vc_column width="1\/3" column_margin_top="50px" column_margin_bottom="30px"]
<"h4" class=\"jt-main-head\" style=\"font-weight:"";\">"OCR<\/"h4"><\/div><\/div>[vc_empty_space height="20px"][vc_column_text]- "Who":<\/span>"Museums<\/a><\/li>
- "What":<\/span>"Specimen<\/a><\/li>
- "How:<\/span>"10,000+<\/a><\/li><\/ul><\/div>[\/vc_column_text][\/vc_column][vc_column width="1\/3" column_margin_top="50px" column_margin_bottom="30px"][vc_column_text]Museums across the world are digitizing their records. This includes extracting text from specimen labels into databases. The labels are often handwritten, with an unknown number of formats. The labels come from various eras, countries, and institutions.<\/p>\n
Tolstoy built an Extractor that processes print labels with 98%+ accuracy<\/strong>, and handwritten labels with ~80% accuracy, regardless of the format.\u00a0<\/strong>This project is ongoing and has the potential to reduce processing times from decades to weeks.[\/vc_column_text][\/vc_column][\/vc_row][vc_row][vc_column][vc_empty_space height=”30px”][\/vc_column][\/vc_row][vc_row][vc_column][vc_empty_space height=”30px”][\/vc_column][\/vc_row][vc_row full_width=”stretch_row”][vc_column column_margin_bottom=”20px”]What types of text can be processed?<\/h3><\/div><\/div>[\/vc_column][\/vc_row][vc_row][vc_column width=”1\/2″ column_padding_top=”30px”][vc_column_text]<\/p>\n<\/h4>\n\n- \n
Reports<\/strong><\/h4>\n<\/li>\n- \n
Transcripts<\/strong><\/h4>\n<\/li>\n- \n
Forms<\/strong><\/h4>\n<\/li>\n- \n
Receipts<\/strong><\/h4>\n<\/li>\n- \n
Handwritten labels<\/strong><\/h4>\n<\/li>\n- \n
News articles<\/strong><\/h4>\n<\/li>\n- \n
and more<\/strong><\/h4>\n<\/li>\n<\/ul>\n[\/vc_column_text][\/vc_column][vc_column width=”1\/2″ column_margin_bottom=”30px” column_padding_bottom=”30px” column_padding_left=”100px” column_padding_right=”100px”]
\n <\/div>\n <\/div>\n <\/div>\n <\/div>\n <\/div>\n <\/div>\n <\/div>\n <\/div>\n [\/vc_column][\/vc_row][vc_row full_width=”stretch_row” css=”.vc_custom_1604447520083{background-color: #f9f9f9 !important;}”][vc_column column_margin_top=”20px” column_padding_top=”50px” column_padding_bottom=”30px” column_padding_left=”60px” column_padding_right=”60px”]Automate to Accelerate Data Mastery<\/h3><\/div>Unlock the data in your internal documents, client responses, maintenance forms, and other records. Have a truly complete view of your information. Ensure a strong foundation to track, analyze, and run predictive analytics on your data.<\/p>\n
It's your data. Make the best use of it.<\/h4><\/div>[\/vc_column][\/vc_row][vc_row][vc_column column_margin_top=”60px”]
- Who:<\/span>University in Cambridge, MA<\/a><\/li>
- What:<\/span>Legal transcripts<\/a><\/li>
- How many:<\/span>150,000+ pages<\/a><\/li><\/ul><\/div>[\/vc_column_text][\/vc_column][vc_column width=”1\/3″ column_margin_top=”50px” column_padding_top=”20px”][vc_column_text]The university digitized a collection of one million documents from the Nuremberg War Crimes Trial. The contents of the entire collection was unknown, so the university partnered with Tolstoy to help index the documents. This was done by identifying all document references in their 150,000+ pages of court transcripts.<\/p>\n
Tolstoy tagged the references with 99%+ accuracy<\/strong>. This saved them approx. 7-8 months of work<\/strong>.<\/p>\n
Read more<\/a>[\/vc_column_text][\/vc_column][\/vc_row][vc_row full_width=”stretch_row” css=”.vc_custom_1604347055001{background-color: #f9f9f9 !important;}”][vc_column width=”1\/3″ column_margin_top=”50px”]
<\/div><\/div>News clippings<\/h2>
<\/div><\/div>Extract text from old news articles.<\/p><\/div><\/div>[\/vc_column][vc_column width=”1\/3″ column_margin_top=”50px”]
Custom OCR<\/h4>
<\/div><\/div>[vc_empty_space height=”20px”][vc_column_text]- Who:<\/span>The Wall Street Journal<\/a><\/li>
- What:<\/span>News Clippings<\/a><\/li>
- How old:<\/span>130 years old<\/a><\/li><\/ul><\/div>[\/vc_column_text][\/vc_column][vc_column width=”1\/3″ column_margin_top=”50px”][vc_column_text]To celebrate their 130th anniversary, The Wall Street Journal<\/em> wanted to digitize and reprint articles from their entire history in a special edition. Since many of the articles were very old, traditional OCR software did not pick up the text well or at all.<\/p>\n
We wrote a custom OCR script that parsed their digitized articles with 95%+ accuracy<\/strong>. This saved them several weeks of manual transcription.<\/p>\n
"Read<\/a>[\/vc_column_text][\/vc_column][\/vc_row][vc_row full_width="stretch_row" css=".vc_custom_1604088796002{background-color: #f9f9f9 !important;}"][vc_column width="1\/3" column_margin_top="50px" column_margin_bottom="30px"]
"5225"<\/div><\/div>"Maintenance<\/h2>
<\/div><\/div>"Extract<\/p><\/div><\/div>[\/vc_column][vc_column width="1\/3" column_margin_top="50px" column_margin_bottom="30px"]
<"h4" class=\"jt-main-head\" style=\"font-weight:"";\">"OCR<\/"h4"><\/div><\/div>[vc_empty_space height="20px"][vc_column_text]- "Who":<\/span>"New<\/a><\/li>
- "What":<\/span>"Maintenance<\/a><\/li>
- "Fields:<\/span>"2,000+"<\/a><\/li><\/ul><\/div>[\/vc_column_text][\/vc_column][vc_column width="1\/3" column_margin_top="50px" column_margin_bottom="30px"][vc_column_text]The New York Power Authority (NYPA) has a critical, 660-megawatt power cable that experienced multiple faults, taking it out of service for 1 to 2 months at a time.<\/p>\n
With only disconnected paper documents, NYPA needed one holistic and connected view of the data to properly assess the situation.<\/p>\n
Using a customized AI text extraction model, Tolstoy digitized the documents into an Excel database that allowed NYPA to easily view and evaluate the information to determine critical next steps.<\/p>\n
"Read<\/a>[\/vc_column_text][\/vc_column][\/vc_row][vc_row full_width="stretch_row" css=".vc_custom_1604088796002{background-color: #f9f9f9 !important;}"][vc_column width="1\/3" column_margin_top="50px" column_margin_bottom="30px"]
"1238"<\/div><\/div>"Museum<\/h2>
<\/div><\/div>"Extract<\/p><\/div><\/div>[\/vc_column][vc_column width="1\/3" column_margin_top="50px" column_margin_bottom="30px"]
<"h4" class=\"jt-main-head\" style=\"font-weight:"";\">"OCR<\/"h4"><\/div><\/div>[vc_empty_space height="20px"][vc_column_text]- "Who":<\/span>"Museums<\/a><\/li>
- "What":<\/span>"Specimen<\/a><\/li>
- "How:<\/span>"10,000+<\/a><\/li><\/ul><\/div>[\/vc_column_text][\/vc_column][vc_column width="1\/3" column_margin_top="50px" column_margin_bottom="30px"][vc_column_text]Museums across the world are digitizing their records. This includes extracting text from specimen labels into databases. The labels are often handwritten, with an unknown number of formats. The labels come from various eras, countries, and institutions.<\/p>\n
Tolstoy built an Extractor that processes print labels with 98%+ accuracy<\/strong>, and handwritten labels with ~80% accuracy, regardless of the format.\u00a0<\/strong>This project is ongoing and has the potential to reduce processing times from decades to weeks.[\/vc_column_text][\/vc_column][\/vc_row][vc_row][vc_column][vc_empty_space height=”30px”][\/vc_column][\/vc_row][vc_row][vc_column][vc_empty_space height=”30px”][\/vc_column][\/vc_row][vc_row full_width=”stretch_row”][vc_column column_margin_bottom=”20px”]
What types of text can be processed?<\/h3>
<\/div><\/div>[\/vc_column][\/vc_row][vc_row][vc_column width=”1\/2″ column_padding_top=”30px”][vc_column_text]<\/p>\n<\/h4>\n
- \n
- \n
Reports<\/strong><\/h4>\n<\/li>\n
- \n
Transcripts<\/strong><\/h4>\n<\/li>\n
- \n
Forms<\/strong><\/h4>\n<\/li>\n
- \n
Receipts<\/strong><\/h4>\n<\/li>\n
- \n
Handwritten labels<\/strong><\/h4>\n<\/li>\n
- \n
News articles<\/strong><\/h4>\n<\/li>\n
- \n
and more<\/strong><\/h4>\n<\/li>\n<\/ul>\n
[\/vc_column_text][\/vc_column][vc_column width=”1\/2″ column_margin_bottom=”30px” column_padding_bottom=”30px” column_padding_left=”100px” column_padding_right=”100px”]
\n <\/div>\n <\/div>\n <\/div>\n <\/div>\n <\/div>\n <\/div>\n <\/div>\n <\/div>\n [\/vc_column][\/vc_row][vc_row full_width=”stretch_row” css=”.vc_custom_1604447520083{background-color: #f9f9f9 !important;}”][vc_column column_margin_top=”20px” column_padding_top=”50px” column_padding_bottom=”30px” column_padding_left=”60px” column_padding_right=”60px”]Automate to Accelerate Data Mastery<\/h3>
<\/div>Unlock the data in your internal documents, client responses, maintenance forms, and other records. Have a truly complete view of your information. Ensure a strong foundation to track, analyze, and run predictive analytics on your data.<\/p>\n
It's your data. Make the best use of it.<\/h4><\/div>[\/vc_column][\/vc_row][vc_row][vc_column column_margin_top=”60px”]
- \n
- "What":<\/span>"Specimen<\/a><\/li>
- "What":<\/span>"Maintenance<\/a><\/li>
- What:<\/span>News Clippings<\/a><\/li>
- What:<\/span>Legal transcripts<\/a><\/li>
![\"black\"](\"https:\/\/staging.tolstoy-ai.com\/wp-content\/themes\/juster\/images\/arrows\/box-arrow-right.png\"){kind=icon}
<\/span><\/div>