Data - centric Document Intelligence SaaS Platform To empower business users with self - service document automation Proud to have global tech majors back us as our value - added resellers and our journey has just begun... Data is core to digital transformation. About 60% of enterprise data lies in documents. Document processing is key to successful transformation. IDP market is poised to grow 5 times in the next 5 years and 10 times by 2030 Huge headroom for growth. Most industries are in early stages of adoption Intelligent Document Processing Market Worth $11.6 Billion By 2030: Grand View Research, Inc. (prnewswire.com) Digital transformation has largely focused on the process automation. Business process transaction data comes from documents and extraction is manual. Documents automation forms 60% of digital transformation. Document automation is in nascent stage with limited adoption in a few industries. 2 What is the space? There are many Intelligent Document Processing (IDP) solutions in the market. Yet there is a huge opportunity, due to the limited capabilities of incumbent products Mature Whitespace Cogniquest* 3 Reference: Everest Group Peak Matrix 2022 Who are the incumbents? OCR Preprocessing Classification Complementary Tech Multi - lingual Unstructured Text Seals and Signatures Time to market NLP based solution Pre - trained models Confidence Flags Cross training Composite view Contextual Understanding Template independent Complex Table extraction User based learning Business User Self service Whitespace Analysis Current IDP market capability Unmet needs and unsolved challenges can only be addressed by a disruptive innovative solution with technology leapfrog! Lack of fully built solutions. They either do one thing very well or do most things partially Document processing needs are shifting from structured documents to unstructured text Need for large training set of documents and time/effort to train makes it prohibitive Varied levels of quality and templates of input documents reduces the success rate Customization is key to effective solution and most products have limited flexibility Business users are dependent on the technology team to handle new documents and data sets What are we solving? AI/ML experts, Serial Entrepreneurs, Linguists and Corporate leader with collective experience of 100+ years in the space have come together, to build a cutting - edge "data - centric AI” document intelligence platform Satish Grampurohit Chief Evangelist (Strategy and growth) Ex - Infosys Global Delivery Head (Insurance). Digital Transformation leader. Angel investor. Startup board advisor. NIT Thejaswi S CPO (NLP & data expert) Linguist turned NLP expert. An expert in developing data models for AI/ML and NLP solutions across industry domains with 15+ years of experience. Nathaniel N Head – Engineering (IDP, AI/ML) Full - stack developer with 15 years of experience in building end - to - end automation solutions using AI/ML Girish Kerodi CEO (NLP expert) Journalist turned Serial Entrepreneur. Hands - on experience in developing industry solutions using AI and ML technologies Harsha CTO (IDP, AI/ML) Chief architect with experience in leading teams for developing AI/ML solutions for Intelligent Document Processing. 5 Who are we? Our “patent applied” SaaS platform ( InfoRefinery ) is powered by proprietary cognitive engines, offering human like reading and understanding capabilities 6 What have we developed? InfoRefinery NLP Document layout analysis Numerical analysis Aspect - based Sentiment analysis Image processing Document classification Information extraction Topic - based data classification Table Extraction Document TOC Unstructured documents (e.g., Email, press release) Structured documents (e.g., Acord) Semi - structured documents (e.g., Invoice) Financial spreading Data analytics OUTCOMES PLATFORM CAPABILITIES Intelligent search & discovery Document tagging Cognitive engines powering InfoRefinery* InfoRefinery platform Data compositive (Structural) Mathematical Linguistic Data - centric Context - aware *Patent pending technology Text / language Image Grid Structure Domain Cognitive engines InfoRefinery , built on data - centric AI technology, can process complex unstructured documents with just about 1/10 th the industry average training (data and duration) Human - like reading* Understands • Structure • Context • Language • Domain Secret sauce: Cognitive engines built with native NLP/ ML/ AI Data - centric technology Can handle • Unstructured • Semi structured • Structured data Without templates and needs fewer training samples Product Capabilities Customer benefits Minimal training with less samples Confidence flags Faster deployment (< 4 weeks) Business user - friendly *Patent Applied Template - free processing Secure & scalable solutions 7 How are we different? Our GTM strategy is to co - create solutions with the clients, build strong relationship with partners, with the end goal being to become API provider to even the competitors Develop packaged solutions with SIs / ISVs Enhance packaged solutions into hyper apps Cloud managed apps Get close to customer problems working with channel partners Ready - to - use hyper apps for channel partners, end customers APIs / managed marketplace apps GTM strategy Customer led Product led GTM Motion 8 Channel led How do we plan to grow? Annual License Fee Consumption based (Price per page) Professional Services Revenue Model Shared Services/BPO/KPO End customers RPA solution providers Global System Integrators Co - opetition as a strategy by extending our hyper intelligent capabilities as APIs is echoing well with diverse segments of clients including competing RPA/IDP leaders Independent software vendors Cogniquest Pre Trained Models APIs Industry Solutions Corporate actions tracker W9 forms Acord forms Waiver forms ISDA IRA forms Financial spreading Form processing Document classification Smart mailbox automation Invoice processing Stamp & signature identification Table extraction Aspect - based sentiment NLP: NER, relationship extraction 3 - way/multiway data reconciliation Document quality enhancer Contract management solution Intelligent pharmacovigilance De - identification - Contracts Who are the buyers and what do they buy? Customer Profile GTM We are encouraged by the positive response from 50+ leads, with adoption across industry segments, geographies and customer types Co - creation with Top tier IT player European Medical Device Maker Global SIs as value added resellers Emerging tech - based insurer Logistics Startup US based Insurance TPA New age RPA platform player An Indian public sector power utility Legal KPO provider Mortgage and HOA companies Pharma Utility InsureTech Legal Supply chain Manufacturing Real Estate Healthcare Multiple Insurance 10 • Document Types : Structured, Semi structured and unstructured documents • Engagement type : Custom package, pretrained hyper app and marketplace API • Customer types : System Integrators, Channel partners (ISVs) and End customers • Engagement value : ARR of $25K to $500K • Pricing model : Annual license + Subscription (per page) + Professional Services • Deployment : Cloud based SaaS, Marketplace API and On - premise How is the traction ? We see a huge opportunity to be a dominant player in document automation space, offering industry hyper apps and advanced APIs in the marketplace 11 • We booked $125k and billed $100k in 2022 - 23 FY (the first year of operation!) • Operational break even by FY 2025 • FY24 & FY25 numbers are bottom up and figures beyond FY26 are top down “Most likely” Financial projection snapshot Revenue ($ Mn) FY24 FY25 FY26 FY27 FY28 License & subscription fee 0.38 2.25 6.75 16.88 42.19 Implementation & maintenance charges 0.24 0.95 1.43 2.14 3.21 Gross Revenue 0.61 3.20 8.18 19.01 45.39 Total Cost 1.37 2.37 4.60 9.97 23.53 Net margin - 0.76 0.83 3.57 9.04 21.86 Revenue MUSD FY24 FY25 FY26 FY27 FY28 Conservative 0.38 1.50 4.49 11.90 28.34 Most Likely 0.61 3.20 8.18 19.01 45.39 Optimistic 0.84 4.41 12.99 36.23 96.17 Revenue projection scenarios How do the numbers add up? Cost heads ($ - Mn) FY24 Staffing (Development, Support and R&D) 0.50 Rent 0.04 Infrastructure (cloud, marketplace) 0.13 Office Expenses 0.06 Legal (Certifications, Patent, Fees) 0.13 Sales / partnerships 0.22 Marketing (Digital, analysts, events) 0.16 Miscellaneous 0.14 Total Cost 1.37 Discretionary spend : 0.52 MUSD 20% 46% 20% 14% Sales Tech team Other expenses Marketing Funding Ask... $ 500K 12 • Social media marketing • Industry Forums, Conferences • Analyst coverage 14% • Certifications – ISO27001, SOC1, GDPR • Global Patent filing • Scalable Marketplace solution 20% • Investment in PoC • Sales team • Channel partners 20% • Talent and capacity building • Research and Development • Global leadership team 46% What support do we need now? We need your strategic advisory and business connects Thank you! Satish.g@cogniquest.ai www.cogniquest.ai