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# YARNS & COLORS | OPEN AI MISSION 002 ## Meeting Intelligence Center ### Turning Enterprise Conversations into Living Knowledge **Company:** YARNS & COLORS / 锦祥纺织科技(苏州)有限公司 **Location:** Suzhou, China **Engagement:** Paid Project / Milestone-Based Delivery **Open To:** Independent AI Builders, AI Workflow Architects, Knowledge Engineers, AI-Native Studios, and Small Technical Teams --- ## 1. What We Want to Build We are not building a simple **Meeting Recorder** or **AI Meeting Summarizer**. YARNS & COLORS wants to create an important real-time knowledge entry point for the enterprise. Every day, valuable first-hand information is created through internal meetings, customer visits, supplier discussions, video conferences, trade shows, product development, and technical discussions. These conversations contain information that traditional ERP systems rarely capture: * Why was a decision made? * What does the customer actually want? * What market changes are emerging? * Which products are customers becoming interested in? * Which problems keep recurring? * Who committed to what? * Why did a project change direction? Our long-term objective is: **To transform important enterprise conversations into continuously accumulating, reusable enterprise knowledge.** --- ## 2. Long-Term Roadmap ### Phase 1 | Capture & Structure **Meeting → Transcript → Summary → Knowledge Extraction → Human Confirmation → Knowledge Storage** **This is the scope of the current Mission.** ### Phase 2 | Connect, Follow & Track Future capabilities may include: * Connecting related meetings * Customer and project continuity * Decision-change tracking * Action-item and commitment follow-up * Recurring issue detection * Customer requirement evolution ### Phase 3 | Enterprise Intelligence As knowledge accumulates, the system should help identify: * Cross-customer requirements * Emerging market signals * Long-term unresolved issues * Changes in customer requirements * Repeated technical problems * Emerging commercial opportunities * Changes to previous decisions The goal is to move from: **Meeting Records → Enterprise Intelligence** ### Phase 4 | Digital Workforce Knowledge Infrastructure Confirmed meeting knowledge should eventually become usable, under appropriate permissions, by other Digital Employees such as Executive AI, Email, Sales, Product Development, Procurement, and future AI roles. --- ## 3. Scope of This Mission **This engagement covers Phase 1 only.** Phases 2–4 explain the long-term direction and are **not included in the current quotation or acceptance scope**. However, the Phase 1 data model, knowledge structure, and architecture must support future expansion **without requiring a complete rebuild**. We do not want an oversized system on Day One, but we also do not want a disposable demo. --- ## 4. Phase 1 Core Loop Phase 1 should establish: **Real Meeting → Trusted Record → Knowledge Extraction → Human Confirmation → Enterprise Knowledge Storage → Retrieval** The system should prove that information from a real enterprise meeting can become **structured, traceable, searchable, confirmable, and reusable knowledge**. --- ## 5. Capability 01 | Meeting Efficiency & Trusted Record The system should support: * Meeting audio capture and/or approved audio upload * Speech-to-Text * Speaker identification / diarization where practical * Timestamped transcript * Meeting-duration management and reminders * Executive Summary * Meeting Minutes * Action Items Time management should consider: * Planned meeting duration * Midpoint reminder * Approximately 80% time reminder * End-of-meeting reminder * Important items still lacking a conclusion, owner, or deadline We do not expect the Builder to train a proprietary ASR model. Mature technologies should be used where appropriate. --- ## 6. Capability 02 | Meeting Knowledge Extraction The system must go beyond summarization and extract independent **Knowledge Units**, including: 1. Customer Requirements 2. Market Trends / Market Signals 3. Product Suggestions 4. Technical Issues 5. Decisions and Decision Rationale 6. Risks 7. Items Requiring Verification 8. New Opportunities 9. Competitor Information 10. Commitments and Actions Each Knowledge Unit should: * Be understandable independently * Retain its source * Be traceable to the original meeting * Retain relevant speaker and timestamp information where practical * Preserve supporting evidence --- ## 7. Capability 03 | Knowledge Governance AI-generated content should not automatically become an official company fact. The system should support a lifecycle such as: **Draft → Pending Confirmation → Confirmed → Updated / Superseded → Archived** Important numerical information, customer commitments, formal decisions, and decision rationale should support human confirmation. AI interpretations must never silently overwrite original source material. --- ## 8. Capability 04 | Enterprise Knowledge Storage Confirmed knowledge must be stored in an environment controlled by the company. Phase 1 should support: * Structured Knowledge Units * Tags * Customer / Project / Product / Person associations * Version history * Evidence traceability * Semantic search * Data export and migration A vector database may support retrieval but should **not become the sole source of enterprise truth**. The architecture should avoid unnecessary dependency on a single LLM, ASR provider, or SaaS platform. **Enterprise knowledge must remain portable.** --- ## 9. Multilingual Requirement YARNS & COLORS operates internationally. Meetings may include Chinese, English, mixed Chinese-English, Japanese, Italian, French, and other languages. Phase 1 may primarily validate: * Chinese * English * Mixed Chinese-English However, the architecture must allow additional languages to be introduced later. Mature multilingual ASR and LLM technologies should be used where appropriate. --- ## 10. Explicitly Out of Scope for Phase 1 Phase 1 does not require: * Complete enterprise Knowledge Graph * Large-scale cross-meeting intelligence * Advanced automated trend analysis * Deep ERP / CRM / OA integration * Company-wide high-concurrency deployment * Full customization for every language * Automated employee performance evaluation * Autonomous management decisions * Full integration with future Digital Employees These belong to later phases. --- ## 11. What Defines Success? The key validation question is: **Can a real enterprise meeting be reliably transformed into trusted, confirmable, traceable, searchable, and reusable enterprise knowledge?** If this loop works reliably in real business conditions, Phase 1 is successful. --- ## 12. Indicative Budget & Timeline ### Phase 1 Budget **USD 4,000–8,000** This is a general reference rather than a fixed ceiling. Proposals outside this range may be considered if they demonstrate additional value, a stronger technical approach, or a materially different delivery model. ### Target Timeline **6–8 weeks** Alternative timelines may be proposed with clear justification and milestone planning. All recurring or third-party costs — including ASR, LLM usage, cloud infrastructure, databases, storage, APIs, software licenses, and other external services — must be disclosed separately from development fees. --- ## 13. Milestone-Based Payment Indicative structure: * **20%** — Architecture + Knowledge Model + Acceptance Plan * **30%** — Meeting Capture + Transcript + Knowledge Extraction * **30%** — Human Confirmation + Knowledge Storage + Search * **20%** — Pilot Acceptance + Deployment + Documentation + Handover Final milestones will be defined in the SOW. --- ## 14. What We Expect in Your Proposal Please address: 1. Your understanding of the business value of this Mission 2. Your proposed Phase 1 architecture and implementation approach 3. How the design can support Phases 2–4 without a complete rebuild 4. Recommended meeting-audio capture approach 5. Recommended ASR and speaker-diarization approach 6. Multilingual handling 7. Knowledge Unit structure 8. Knowledge Storage design 9. Evidence traceability 10. Enterprise data security 11. AI models, frameworks, and components you would use 12. Who will actually perform the development 13. Relevant projects personally delivered 14. Proposed timeline 15. Fixed project quotation 16. Expected recurring third-party costs 17. Recommended maintenance model 18. The three biggest technical or delivery risks Please begin your proposal with: **MEETING INTELLIGENCE MISSION 002** We are not looking for the proposal with the most features. We are looking for: **Builders who understand the value of enterprise knowledge and know how to turn mature AI technologies into a system that works in real business operations.** Strong performers may be invited to participate in later YARNS & COLORS Digital Workforce Missions. Detailed enterprise data, meeting samples, system access, internal workflows, and full acceptance criteria will only be provided to shortlisted candidates under appropriate confidentiality arrangements.
Project ID: 40683511
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MEETING INTELLIGENCE MISSION 002 The real risk here isn't transcription, it's letting AI drafts quietly become "company truth" without a confirmation gate. I'll build Phase 1 around Whisper (or Azure Speech for zh-en diarization) feeding a Postgres + pgvector store, with Knowledge Units held in a Draft to Confirmed lifecycle so evidence, speaker and timestamp stay linked to the source clip. Vector search sits beside Postgres, never replaces it, which keeps knowledge portable off any single LLM later. I can jump in right away. 1. Zoom, Teams, Tencent Meeting or in-room mics for capture? 2. Self-hosted on your Suzhou infra, or Azure China acceptable? Let's get this moving. Shayan
$4,000 USD in 20 days
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MEETING INTELLIGENCE MISSION 002 YARNS & COLORS needs more than meeting summarization: it needs a trusted record that becomes confirmable, traceable enterprise knowledge without breaking future roadmap expansion. In Phase 1, we will implement the core loop, real meeting capture → transcript → knowledge extraction → human confirmation → structured storage → retrieval, so Knowledge Units remain evidence-backed and versioned. Phase 1 architecture: - Capture & transcription: approved audio capture/upload, timestamped transcript, practical speaker diarization, and reminder logic for meeting duration management. - Knowledge extraction: convert transcripts into independent Knowledge Units (requirements, decisions + rationale, risks, commitments/actions, opportunities, technical issues, verification-needed items), each retaining source linkage (meeting id, speaker, timestamps, evidence spans). - Governance: Draft → Pending Confirmation → Confirmed → Updated/Superseded → Archived workflow; AI outputs never overwrite raw source. - Storage & retrieval: portable structured storage with semantic search as an assist (vector index optional), tags and associations (customer/project/product/person), full version history, export/migration support. Multilingual approach: use mature multilingual ASR/LLM services with a design that isolates provider choice to allow later language expansion. Delivery: we will build iteratively against an acceptance plan focused on reliability in real business
$4,000 USD in 6 days
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Hello, MEETING INTELLIGENCE MISSION 002 I have already completed similar AI knowledge, meeting intelligence, transcription, and enterprise workflow projects. I have strong experience in web development, APIs, AI integrations, and scalable software solutions, and I can quickly understand your business requirements and turn real meeting conversations into structured, searchable, and traceable knowledge. I can cover meeting capture, multilingual transcription, speaker identification, knowledge extraction, human confirmation, evidence tracking, storage, and semantic search while keeping the architecture ready for future phases. How do you currently plan to capture meeting audio, through live recording, uploaded files, or both? I would be happy to discuss the Phase 1 architecture and acceptance flow in a quick meeting and share relevant project examples.
$6,000 USD in 7 days
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You already have the gold sitting in meetings, customer visits, and supplier talks. ERP never catches why a decision happened, what the customer really wants, or who promised what. I can start right now. In 24-48 hours you get a live working sample on your kind of conversation: talk in, then a clean knowledge card with decisions, commitments, recurring problems, and market signals, ready for your team to search later. You stay in control of what is stored. I wire the rest so first-hand talk becomes living knowledge, not another forgotten recording. Share one recent meeting note or recording and I will build that sample on it.
$4,500 USD in 3 days
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As a Full Stack Developer with extensive experience in AI Integration, I am confident that I can provide the transformative solution that YARNS & COLORS is seeking for this project. My approach has always been to turn ideas into powerful digital products, and creating an enterprise knowledge extractor aligns perfectly with that goal. Over the past 8+ years, I have successfully delivered 200+ projects, including AI chatbots, AI agents, and workflow automation, which underscore my expertise in transforming raw data into actionable insights. My services don't stop at project handover. I believe that long-term technical support is vital for any mission-critical system like yours. Therefore my mission as your freelancer will extend beyond completing Phase 1 to ensuring that the underlying architecture is future-proof - capable of scaling without necessitating a complete rebuild, based on your Phase 2-4 objectives. Let's team up today and build something amazing together: a solution that empowers YARNS & COLORS by turning your key conversations into valuable, reusable enterprise knowledge!
$4,999 USD in 25 days
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MEETING INTELLIGENCE MISSION 002 This is not a meeting-summary tool; the valuable outcome is turning real conversations into trusted enterprise knowledge with evidence, human confirmation, and a portable source of truth. I’d design Phase 1 around a canonical Meeting + Knowledge Unit model, with AI outputs always linked back to transcript segments, speakers, timestamps, and original material rather than allowing generated text to overwrite source data. I’d use mature multilingual ASR with diarization where practical, structured extraction for the 10 Knowledge Unit types, a confirmation workflow, relational storage for authoritative records, and vector search as a retrieval layer only. The API and data model would keep future cross-meeting intelligence and Digital Workforce integrations possible without overbuilding Phase 1. My matching skills are AI workflow architecture, LLM integration, speech/transcription pipelines, knowledge extraction, backend systems, semantic search, and audit-friendly data design. 1. Should Phase 1 be deployed in your own cloud environment for data control, or can managed services be used for ASR/LLM processing? 2. For the pilot, will users upload meeting audio after the meeting, or is live capture during meetings a required part of acceptance? Reply with those two decisions and I can frame the Phase 1 architecture around your real operating constraints. Best regards, Opeyemi
$5,000 USD in 30 days
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MEETING INTELLIGENCE MISSION 002 Hi, I'm Dr. Santosh Kumar Nanda, PhD in AI/ML, 18+ years building enterprise AI systems across Banking, Telecom, and Audit domains. The hard part here isn't the transcript or summary; it's making the extracted knowledge trusted enough that people actually act on it. Get that wrong, and the system becomes a tool people log into occasionally but never build habits around. Relevant experience: Built an agentic GenAI system for financial audit data (Azure OpenAI, LangGraph, AgentOps), turning large volumes of audit conversations into structured findings. Nothing became official until a human confirmed it, with full traceability to source, the same trust mechanism this Mission needs. Improved audit review throughput by over 40%. Designed a multi-agent RAG system (AutoGen, LangGraph, Agentic-RAG) for enterprise document Q&A across Telecom and Banking clients, pulling from multiple sources while keeping every answer traceable and avoiding lock-in to any single AI vendor, the same portability Phase 1 needs. I'd personally lead development. My approach favors proven, mature AI components over building from scratch, the real risk here isn't the AI, it's whether the system earns trust in daily operations. Happy to walk through the technical design and a fixed quotation on a call. Best regards, Dr. Santosh Kumar Nanda
$6,000 USD in 30 days
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Greetings! MEETING INTELLIGENCE MISSION 002 I understand the business value is to transform valuable enterprise conversations into reusable, traceable knowledge that goes beyond what traditional systems capture. My Phase 1 design would support meeting audio capture, transcription with speaker diarization, trusted record generation, and knowledge extraction into structured units including customer requirements, decisions, risks, and commitments. I would implement a human confirmation workflow to ensure accuracy, with a lifecycle from draft to confirmed to archived. I would store knowledge in a company controlled environment with traceability, semantic search, and version history. The architecture would be modular to support future phases without rebuilding. I would use mature ASR, multilingual LLM, and vector database technologies while ensuring enterprise knowledge remains portable. I can share relevant projects personally delivered. I can complete Phase 1 within 6 to 8 weeks. Let me know your preferred start date and I can share a detailed proposal. Thanks, Revival
$4,000 USD in 30 days
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