Oria vs Claude: Best AI for PowerPoint Consulting Slides

Oria is an AI add-in for Microsoft PowerPoint that turns rough notes into consulting-grade slides — built for management consultants, investment bankers, private equity, and corporate strategy teams. This deck compares Oria with Claude for professional slide creation: layout quality, speed, data-density, and on-brand formatting. Try Oria for PowerPoint at https://www.oria.one/

Transcript

1. 1 AI for PowerPoint Comparison: Oria One vs Claude https://www.oria.one/ Claude still makes basic, boxy slides. But the ecosystem around it wins: deep connectivity with other plugins and real agentic features built in. Oria is the opposite. No agentic features, but it is the closest thing to a McKinsey designer – Oria is the best AI for complex consulting slides. It takes AI-generated slides or raw layouts and makes it look like a McKinsey- designer built it.
2. 2 Test Create a slide with a beautiful detailed visual in the middle, with a 3D platform showing employees working in an office. Lines point to different parts of that platform and connect to the use cases of office work automation provided below. Lines should point to relevant part of the visual that makes sense within the context of use cases. 1. Executive decision support automation - Board briefing synthesis workflows - Scenario-model sensitivity preparation - Cross-portfolio risk flagging 2. Legal and compliance process automation - Contract clause deviation review - Regulatory filing completeness checks - Policy exception escalation routing 3. Financial analysis and control automation - Management reporting variance analysis - Forecast consolidation across entities - Audit trail reconciliation checks 4. Research and knowledge work automation - Literature scan and summarization - Expert interview note structuring - Competitive intelligence signal tracking 5. Client and stakeholder coordination automation - Proposal assembly from precedents - Meeting brief generation workflows - Follow-up action tracking 6. Human capital and organizational automation - Senior hiring pipeline screening - Performance review evidence compilation - Workforce planning data synthesis Example 1 Input
3. 3 • Senior hiring pipeline screening • Performance review evidence compilation • Workforce planning data synthesis • Literature scan and summarization • Expert interview note structuring • Competitive intelligence signal tracking • Management reporting variance analysis • Forecast consolidation across entities • Audit trail reconciliation checks • Contract clause deviation review • Regulatory filing completeness checks • Policy exception escalation routing • Board briefing synthesis workflows • Scenario-model sensitivity preparation • Cross-portfolio risk flagging • Proposal assembly from precedents • Meeting brief generation workflows • Follow-up action tracking 4. Research and knowledge work automation 6. Human capital and organizational automation 2. Legal and compliance process automation 5. Client and stakeholder coordination automation 3. Financial analysis and control automation 1. Executive decision support automation Office Work Automation Use Cases Oria Mode: Text to slide
4. 4 • Senior hiring pipeline screening • Performance review evidence compilation • Workforce planning data synthesis • Management reporting variance analysis • Forecast consolidation across entities • Audit trail reconciliation checks • Literature scan and summarization • Expert interview note structuring • Competitive intelligence signal tracking • Contract clause deviation review • Regulatory filing completeness checks • Policy exception escalation routing • Board briefing synthesis workflows • Scenario-model sensitivity preparation • Cross-portfolio risk flagging • Proposal assembly from precedents • Meeting brief generation workflows • Follow-up action tracking Human capital and organizational automation Research and knowledge work automation Client and stakeholder coordination automation Legal and compliance process automation Financial analysis and control automation Executive decision support automation Office Work Automation Use Cases Oria Mode: Text to slide
5. 5 Office work automation: six use case categories 1 Executive decision support • Board briefing synthesis • Scenario-model sensitivity prep • Cross-portfolio risk flagging 2 Legal & compliance • Contract clause deviation review • Regulatory filing checks • Policy exception escalation 3 Financial analysis & control • Management variance analysis • Forecast consolidation • Audit trail reconciliation 4 Research & knowledge work • Literature scan & summarization • Expert interview structuring • Competitive intelligence 5 Client & stakeholder coord. • Proposal assembly from precedents • Meeting brief generation • Follow-up action tracking 6 Human capital & org. • Senior hiring pipeline screening • Performance review evidence • Workforce planning synthesis The connected office Boardroom Legal files Finance desk Library Client meeting HR & interviews Claude Prompt: Convert text as instruction to slide Broken, HTML-based layout and style
6. 6 Ambition to become top 3 player in 2030 and 2035 with 7 core initiatives Transition to modern IT platform architecture with microservices and implementation of an agile working model across teams Building a “selling” website and an effective digital engagement strategy across multiple channels and touchpoints, enabling enhanced conversion Radical development of the mobile application in terms of convenience and functionality, adding 10+ new customer journeys and Improving customer experience and building convenient customer journeys in digital channels across mobile and web Launching anchor products based on an understanding of customer pain points and deep dive to customer interviews Key goals of developments Assets Clients ROE bn bn 300+ 100 Thousands 500+ thousands 100 % % 20 8 Top 3 30 place 2028 2025 Opportunity to accelerate business growth 1 2 3 4 5 NPS -11% 25%+ 6 Deploying Agile squads to accelerate feature development to production and happieness of product teams 7 Building end-to-end GenAI layer enabling all activities of front-office and streamlining backlog of back-office Targets are pending final board confirmation Example 2 Input
7. 7 Building a "selling" website and an effective digital engagement strategy across multiple channels and touchpoints, enabling enhanced conversion Radical development of the mobile application in terms of convenience and functionality, adding 10+ new customer journeys and Opportunity to accelerate business growth Transition to modern IT platform architecture with microservices and implementation of an agile working model across teams Building end-to-end GenAI layer enabling all activities of front- office and streamlining backlog of back-office Improving customer experience and building convenient customer journeys in digital channels across mobile and web Deploying Agile squads to accelerate feature development to production and happieness of product teams Launching anchor products based on an understanding of customer pain points and deep dive to customer interviews Key goals of developments 30 place Top 3 500+ Thousands Targets are pending final board confirmation 100 thousands ROE 8 % 2025 2028 300+ bn 20 % Clients 100 bn Assets 25%+ -11% NPS 4 2 3 6 7 5 1 Ambition to become top 3 player in 2030 and 2035 with 7 core initiatives Oria Mode: Improve Design
8. 8 • Key initiatives aim to significantly boost market share and customer engagement, driving substantial growth across all metrics by 2028. • Focus on digital transformation, product innovation, and operational efficiency. Building a “selling” website and an effective digital engagement strategy across multiple channels and touchpoints, enabling enhanced conversion Transition to modern IT platform architecture with microservices and implementation of an agile working model across teams Radical development of the mobile application in terms of convenience and functionality, adding 10+ new customer journeys and Improving customer experience and building convenient customer journeys in digital channels across mobile and web Launching anchor products based on an understanding of customer pain points and deep dive to customer interviews Opportunity to accelerate business growth Key goals of developments Targets are pending final board confirmatin Clients 500+ Thousands Clients 100 thousands Summary Assets 300+ bn Assets 100 bn NPS 25%+ NPS -11% 30 place ROE 20% ROE 8% 2025: 2028: Top 3 1. 2. 3. 4. 5. Ambition to become top 3 player in 2030 and 2035 with 7 core initiatives Oria Mode: Improve Design
9. 9 Ambition: Top 3 player by 2030–2035 via 7 core initiatives OPPORTUNITY TO ACCELERATE GROWTH 2025 2028 TOP 3 TARGET ROE 8% 20% Clients 100k 500k+ Assets 100 bn 300 bn+ NPS -11% 25%+ Targets pending final board confirmation KEY GOALS OF DEVELOPMENT 1 Anchor products from customer insight Launch products grounded in deep customer pain-point research and interviews 2 Convenient omnichannel customer journeys Improve CX across mobile and web with seamless digital journeys 3 Radical mobile app evolution Add 10+ new customer journeys with major functionality and convenience upgrades 4 “Selling” website and digital engagement Effective engagement strategy across channels and touchpoints to lift conversion 5 Modern IT platform on microservices Transition architecture and adopt an agile working model across teams 6 Agile squads to accelerate delivery Deploy squads to speed feature delivery and improve product-team happiness 7 End-to-end GenAI layer Enable all front-office activities and streamline back-office backlog Claude Prompt: Make this slide look beautiful, improve design Simple output, broken roundness of corners
10. 10 Marketplace economics: a small take rate compounds through a self-reinforcing flywheel Create a slide with a clear business model diagram centered and a unit economics box to the right. The center visual should be a simple revenue flow chart: “Buyer pays” → “Transaction GMV” → “App takes X% fee” → “Farmer receives payout,” with a small note underneath stating in a full sentence: “The primary revenue stream is a small percentage take rate on each completed transaction, with optional fees for promoted listings once liquidity is proven.” On the right, add a boxed section titled “Illustrative unit economics (per transaction)” laid out as a mini waterfall or bullet list that includes full-sentence lines such as: “Average order value is £Y, of which the app captures X% as revenue,” “Payment processing and support costs reduce contribution by £A,” and “Contribution margin improves as order value and repeat frequency increase.” On the left, add a “Marketplace flywheel” visual in a circular loop with 4 nodes: “More farmers list” → “More local variety” → “More buyers return” → “Higher farmer sell-through” → back to “More farmers list.” Each node should have one full sentence beneath it, explaining how it drives the next step (e.g., “When sell-through is high, farmers list more frequently and recommend the app to other growers”). Finish with a bottom bar of two full-sentence risk mitigations: “We will initially focus on dense neighbourhoods to ensure pickup convenience and high match rates,” and “We will implement lightweight trust features to reduce no-shows and improve repeat usage.” Example 1 Input
11. 11 We will implement lightweight trust features to reduce no-shows and improve repeat usage. Illustrative unit economics (per transaction) The primary revenue stream is a small percentage take rate on each completed transaction, with optional fees for promoted listings once liquidity is proven We will initially focus on dense neighbourhoods to ensure pickup convenience and high match rates. Contribution margin improves as order value and repeat frequency increase. Average order value is £Y, of which the app captures X% as revenue. Payment processing and support costs reduce contribution by £A. When sell- through is high, farmers list more frequently and recommend the app to other growers. Higher demand leads to more sales for farmers, improving their earnings. Satisfied buyers return for convenience and quality, building a loyal customer base. Increased variety attracts diverse buyers looking for unique local produce. Marketplace Flywheel Higher farmer sell-through Platform commission captured Farmer receives payout More buyers return Buyer initiates transaction Transaction GMV More farmers list More local variety Revenue Flow App takes X% fee Net earnings after fee Total value of goods Buyer pays Marketplace Business Model & Growth Engine Oria Mode: Text to slide
12. 12 Marketplace economics: a small take rate compounds through a self-reinforcing flywheel Marketplace flywheel 1 More farmers list Greater supply density attracts more buyers. 2 More local variety Buyers find what they want, return more often. 3 More buyers return Higher demand lifts farmer sell-through rates. 4 Higher sell-through Farmers list more frequently and refer peers. ↺ loops back to (1) Revenue flow Buyer pays Checkout at full order price Transaction GMV Captured on platform App takes X% fee Take rate on completed order Farmer receives payout Net of fees The primary revenue stream is a small percentage take rate on each completed transaction, with optional fees for promoted listings once liquidity is proven. Illustrative unit economics (per transaction) Average order value £Y Buyer pays full order price at checkout. Platform revenue (X% take) +X% The app captures X% of order value as gross revenue. Payment & support costs −£A Processing fees and CX reduce contribution by £A. Contribution margin Net Improves as AOV and repeat frequency rise. Contribution margin scales with order value and repeat frequency — each loyal buyer compounds the take rate. Risk mitigations • We will initially focus on dense neighbourhoods to ensure pickup convenience and high match rates. • We will implement lightweight trust features to reduce no-shows and improve repeat usage. Claude Prompt: Convert text as instruction to slide Broken, HTML-based layout and style
13. 13 Agent built on top of Claude Proposition Differentiation Distribution User Experience Generative Design Engine Single Slide API Cost ”We are better than Claude because …” ”We make your Claude stronger. We complement Claude to build MBB-grade slides … ” No moat. You run on the same LLM as everyone else - racing the company that owns it and ships incredibly fast Defensible by design. Patent-pending visual AI Claude can't replicate - Anthropic doesn't ship image models Head-to-head against Claude, Copilot, and soon OpenAI: all bundling PPTX plugin into their base subscription No direct competition. Two clean paths to market: (1) professional design layer on top of Claude or (2) standalone plugin for the non-Claude users Fully parallel: 10 slides take 5-6 minutes XX$ due to long system instruction, no room for margin XX$ Isolated plugin - no connectivity across eco- system of Claude / Copilot Connectivity 4-6 minutes for 1 design option Multiple slide Sequential work: 10 slides take 40-60 minutes 30-40 seconds to get 2-5 design options 2-3 minutes to final editable slide Full connectivity - if deployed on top of Claude / Copilot Two options Hard to differentiate & distribute with negative unit economics Defensible proprietary product, up to 10X cheaper and faster Example 6 Input
14. 14 No direct competition. Two clean paths to market: (1) professional design layer on top of Claude or (2) standalone plugin for the non-Claude users Hard to differentiate & distribute with negative unit economics Defensible proprietary product, up to 10X cheaper and faster Defensible by design. Patent-pending visual AI Claude can’t replicate - Anthropic doesn’t ship image models “We make your Claude stronger. We complement Claude to build MBB-grade slides …” XXX seconds to get 2-5 design options XXX minutes to final editable slide XXX$ Head-to-head against Claude, Copilot, and soon OpenAI: all bundling PPTX plugin into their base subscription No moat. You run on the same LLM as everyone else - racing the company that owns it and ships incredibly fast Isolated plugin - no connectivity across eco- system of Claude / Copilot XX $ due to long system instruction, no room for margin Full connectivity - if deployed on top of Claude / Copilot Sequential work: 10 slides take 40-60 minutes Agent built on top of Claude Generative Design Engine Fully parallel: 10 slides take 5-6 minutes “We are better than Claude because …” 4-6 minutes for 1 design option User Experience Multiple slide Connectivity Proposition Single Slide Differentiation Distribution API Cost Two options Oria Mode: Improve Design
15. 15 Two options Agent built on top of Claude Generative Design Engine Proposition “We are better than Claude because …” “We make your Claude stronger. We complement Claude to build MBB-grade slides … ” Differentiation No moat. You run on the same LLM as everyone else — racing the company that owns it and ships incredibly fast Defensible by design. Patent-pending visual AI Claude can’t replicate — Anthropic doesn’t ship image models Distribution Head-to-head against Claude, Copilot, and soon OpenAI: all bundling PPTX plugin into their base subscription No direct competition. Two clean paths to market: (1) professional design layer on top of Claude or (2) standalone plugin for non-Claude users User Experience Single Slide: 4–6 min for 1 design option Multiple Slides: 10 slides take 40–60 minutes (sequential) Single Slide: XXX sec for 2–5 design options; 2–3 min to final Multiple Slides: XXX slides inXXXminutes (fully parallel) API Cost XXX per slide due to long system instruction — no room for margin XXX per slide, Connectivity Isolated plugin — no connectivity across eco-system of Claude / Copilot Full connectivity — if deployed on top of Claude / Copilot Hard to differentiate & distribute with negative unit economics Defensible proprietary product, up to 10X cheaper and faster Claude Prompt: Redesign the slide Simple output, looks AI-generated with empty gaps
16. 16 AI for PowerPoint Comparison: Oria One vs Claude https://www.oria.one/