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  6. 110. Proposal Draft Generation AI Design

110. Proposal Draft Generation AI Design

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last updated:Feb 12, 2026

アンドワン 本社、東京支社、川崎営業所
アンドワン 本社、東京支社、川崎営業所
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The proposal is not about "writing" but about "structuring." It automatically generates the main points of the proposal from the hearing content, increasing the speed and success rate of proposals.

Companies that are slow to make proposals will lose, even if they have a winning strategy. In B2B, the speed of proposals after the initial meeting directly correlates with trust. However, the reality is that information is scattered, not organized in the minds of the responsible parties, and proposals are created from "scratch" each time. As a result, submissions are delayed, and competitors move forward with their evaluations. This service is designed to generate a draft proposal (structure + key points + rationale + estimation assumptions) using AI based on hearing notes, form inputs, inquiry content, and existing materials. It is not just about generating text; it fixes the winning strategy of the proposal (issues → actions → results → approach → conditions) and makes it reproducible by anyone. ■ Service Offerings (3 points) 1. Proposal template design (structure of the winning strategy, chapter organization, essential elements) 2. Draft generation design (input items, output format, reference rationale) 3. Operation and review design (quality assurance, prevention of incorrect generation, improvements) *First, please provide the "template of the proposal (the chapter organization you are currently using)" and "common types of projects." We will create the generation framework.

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110. Proposal Draft Generation AI Design

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  • Related Link - https://andoneweb.co.jp/
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■Concerns like these - It takes too long to create proposals, leading to late submissions - Proposal quality varies by person in charge - Reusing existing materials has become personalized and hard to find - Prerequisites for estimates are overlooked, causing disputes later - Proposals turn into "function descriptions," failing to convey value ■Provided Content (Details) - Proposal template design (confirming the "format" before generation) - Input design (breaking it down to a level that the field can input) - Draft output design (fixing it into a "usable" form) - Reference basis (stronger when connected to RAG/internal knowledge) - Guardrails (preventing accidents in proposals) ■Deliverables - Proposal template (chapters, essential elements, writing rules) - Input form/item definitions (items needed from hearing to generation) - Generative AI specifications (output format, prohibitions, basis presentation, confirmation item extraction) - "Items to Confirm" checklist (to prevent omissions) - Standard expression collection for prices and conditions (ranges, variable factors, disclaimers) - Operational rules (review, version management, logs, improvements) - KPI design (creation time, submission speed, win rate, number of revisions)

Price information

■4 million to 20 million yen (varies based on template number and integration) - Light (single template + basic generation specifications + operational design): 4 to 7 million yen - Standard (multiple project types, reference basis, enhanced checklist): 7 to 13 million yen - Extended (internal knowledge RAG integration, CRM integration, version management/audit included): 13 to 20 million yen *Estimate required

Delivery Time

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※Minimum 1.5 months / Standard 2 to 4 months

Applications/Examples of results

■Purpose - Reduction of proposal creation time (improvement in speed) - Standardization of proposal quality (elimination of individual dependency) - Clarification of estimation prerequisites (prevention of future troubles) - Improvement of reproducibility of winning strategies (enhancement of win rate) - Acceleration of training for newcomers (template operation) ■Examples of Achievements - Late proposal submission → Improved submission speed through draft generation - Omissions in prerequisites → Reduced disputes with checklists - Quality variability → Ensured consistency of proposals with fixed chapter structure ■Approach 1. Current situation analysis: Inventory existing proposals, winning patterns, and project types 2. Template design: Confirm chapter structure, essential elements, and prerequisite formats 3. Generation design: Define the flow of input → output → review 4. Testing: Generate drafts using past projects and reflect on corrections 5. Deployment: Integrate into standard operations for sales/consulting 6. Improvement: Strengthen templates and examples monthly (accumulate winning strategies)

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1. Introduction of FAQ × AI Chat

1. Introduction of FAQ × AI Chat

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2. Introduction of AI chat linked to the product database

2. Introduction of AI chat linked to the product database

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3. Introduction of AI for Pre-Estimate Hearing

3. Introduction of AI for Pre-Estimate Hearing

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4. Introduction of multilingual AI chat.

4. Introduction of multilingual AI chat.

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5. Construction of a Technical Data Search AI Portal

5. Construction of a Technical Data Search AI Portal

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6. Veteran Inheritance System

6. Veteran Inheritance System

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7. Building an In-house Knowledge AI (RAG)

7. Building an In-house Knowledge AI (RAG)

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8. Inquiry Automatic Classification AI Design

8. Inquiry Automatic Classification AI Design

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9. Lead Evaluation AI (Temperature Estimation) Design

9. Lead Evaluation AI (Temperature Estimation) Design

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10. Sales Talk Generation AI Design

10. Sales Talk Generation AI Design

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11. Proposal Draft Generation AI Design

11. Proposal Draft Generation AI Design

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12. Specification Summary AI (Internal) Design

12. Specification Summary AI (Internal) Design

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13. Drawing/Image Description AI (Assistance) Design

13. Drawing/Image Description AI (Assistance) Design

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14. AI-Compatible FAQ Structured Design

14. AI-Compatible FAQ Structured Design

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15. AI Response Guardrail Design

15. AI Response Guardrail Design

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16. AI Chat Log Analysis Design

16. AI Chat Log Analysis Design

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17. Establishing rules for AI content creation

17. Establishing rules for AI content creation

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18. AI Implementation PoC Design (Web)

18. AI Implementation PoC Design (Web)

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19. AI × Inquiry Flow Design

19. AI × Inquiry Flow Design

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21. Formulation of AI Utilization Policy

21. Formulation of AI Utilization Policy

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Company information

アンドワン

アンドワン 本社、東京支社、川崎営業所

IT/Telecommunications

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A site that is just cheap ultimately increases costs and risks." We propose web development that maximizes business results while thoroughly addressing essential requirements. Are you creating a website like this? - It seems that the websites of competitors look better, but you don't know why. - Every update incurs additional costs, and before you know it, expenses have ballooned beyond expectations. - While the appearance is nice, it ignores laws and industry-specific rules, leading to complaint risks... - You want to attract customers and inquiries, but the production company only talks about design. - As a result of choosing a production that is simply cheap, you are overwhelmed with trouble handling and can't focus on your core business. Point 1. Avoid troubles with a design that has no "gaps or omissions." 2. Minimize operational costs with a design that assumes in-house updates. 3. Planning power that pursues business results. "Is the initial cost a bit high?" But in the long run, it's safe and cost-effective. We have prepared a plan to truly deliver results "correctly.

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