106. Construction of a Technical Data Search AI Portal
Having technical documents "available" is not enough to sell. We will make internal and external technical documents readily accessible and usable through AI search.
Technical documentation in the manufacturing industry is the same as "non-existent" when it is scattered. Data sheets, specifications, CAD files, inspection reports, manuals, FAQs, past response emails. When users cannot find the necessary materials, they disengage, sales teams waste time searching, and engineers repeatedly address the same questions. This service will build an AI portal that allows cross-searching of technical documents. Instead of keyword searches, it will be designed to search in the context of applications, conditions, products, standards, etc., providing a series of results including "relevant documents," "summaries of relevant sections," "related pages," and "next actions (quotes/consultations)." It will cater to both external public versions (for customers) and internal versions (for sales/technical teams), aiming to reduce inquiries and facilitate business negotiations. ■Provided Content (3 Points) 1. Inventory of documents and information design (classification, tags, permissions, update flow) 2. Construction of AI search portal (searching/summarizing/citing sources/linking pathways) 3. Operation and guardrail design (preventing incorrect answers, improving logs, managing permissions) *Please let us know the types of documents (PDF/CAD/Excel, etc.), storage locations (Drive/SharePoint, etc.), and the scope of publication (external/internal). We will design it as quickly as possible.
basic information
■Concerns like these - There are too many documents, making it difficult for both customers and internal staff to search. - Sales cannot find technical documents, leading to delayed responses. - Engineers are repeatedly addressing the same questions. - There are downloadable documents, but they are hard to find and not used. - It is difficult to delineate between public documents and internal documents (permissions). ■Provided Content (Details) - 1) Document inventory (the preliminary step to make documents "searchable") - 2) Information design (which affects search accuracy) - 3) AI search portal functionality ("find → understand → proceed") - 4) Guardrails (to prevent misunderstandings and leaks) - 5) Design for external and internal versions (different purposes) ■Deliverables - Document inventory sheet (type, version, publication category, storage location) - Information design document (classification/tags/metadata/synonym dictionary) - Portal screen design (search UI, result display, filters, navigation) - Permission design (external/members/internal, control at the document level) - AI search specifications (summaries, evidence presentation, citation rules, behavior when unanswered) - Operational design (updates, reviews, log improvements, KPIs)
Price information
■6 million to 30 million yen (varies based on data volume, permissions, and integration) - Light (small scale, single storage location, either external or internal): 6 to 10 million yen - Standard (classification/permissions, summarization, filtering, operational design included): 10 to 18 million yen - Extended (large scale, multiple storage locations, membership-based, CRM/DB integration, management screen included): 18 to 30 million yen *Estimate required
Delivery Time
Applications/Examples of results
■Purpose - Reduction of exploration time for technical documents (internal and external) - Reduction of inquiries (improvement of self-resolution rate) - Improvement of response speed (support for immediate answers in sales) - Increase in conversion rate to business negotiations (connecting documents to estimates/consultations) - Assetization of knowledge (standardization of version management and operations) ■Examples of Achievements - Technical documents scattered → Immediate presentation through AI search, reducing response workload - Overseas support → Cross-searching English documents to accelerate initial responses - Member-specific documents → Mitigating leakage risks through permission design ■Approach 1. Current situation analysis: Types, quantity, storage locations, and publication scope of documents 2. Inventory: Organizing publication categories and version management 3. Information design: Finalizing classification/tags/synonyms 4. Implementation: Search UI, summarization, permissions, measurement 5. Testing: Validating accuracy with representative questions (correcting zero hits/misunderstandings) 6. Operation: Monthly improvements (logs → dictionary/tags → accuracy enhancement)
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