GIGA CRM Phase 1 β Product Requirements Document (PRD)
Document Status: Draft v0.1
Created: 2026-07-10
Project Charter Approved: 2026-06-23
Document Owner: [TBD]
Approved By: [TBD]
Revision History
| Version |
Date |
Author |
Changes |
| v0.1 |
2026-07-10 |
β |
Initial framework, drafted from Project Charter |
Table of Contents
- Project Overview
- Business Background & Problem Statement
- Project Objectives & Success Criteria
- Scope Definition
- Stakeholders & User Roles
- Functional Requirements
- Non-Functional Requirements
- System Architecture Overview
- Data Requirements
- AI / Agent Requirements
- Integration Requirements
- Deliverable Mapping
- Milestones & Timeline
- Assumptions, Dependencies & Risks
- Acceptance Criteria
- Glossary
- Appendix
1. Project Overview
1.1 Project Name
GIGA CRM β AI Opportunity Platform (Phase 1)
1.2 Project Positioning
An AI-first CRM platform for the construction engineering industry, leveraging AI Agents to automate opportunity acquisition, research, and scheduling workflows β connecting the full pipeline from opportunity discovery to project handoff.
1.3 Document Purpose
This document is based on the approved Project Charter (Approved 2026-06-23). It decomposes the Phase 1 scope defined in the charter into actionable functional and non-functional requirements, serving as the basis for subsequent solution design, development, and acceptance testing.
1.4 Target Audience
- Product Managers & Business Analysts
- Solution Architects & Development Teams
- Testing Teams (UAT)
- Project Sponsors & Business Owners
2. Business Background & Problem Statement
2.1 Industry Background
Opportunity management in the construction engineering industry relies heavily on external data sources (Dodge, ConstructConnect, etc.) and manual processes. The current business team faces the following core pain points:
2.2 Current Pain Points
| ID |
Pain Point |
Impact |
| P-01 |
Disconnected systems, data silos |
Opportunity information scattered across multiple systems, no unified view |
| P-02 |
Heavy manual processes |
Researchers must manually screen opportunities, find contacts, and assess priorities |
| P-03 |
Inconsistent information |
Same opportunity has different statuses across systems |
| P-04 |
Limited visibility |
Management cannot view opportunity pipeline and team progress in real time |
| P-05 |
Inefficient handoff |
Researcher-to-Scheduler handoff relies on email/verbal communication, high risk of information loss |
2.3 Business Drivers
- Increase conversion rate: AI-driven screening and prioritization lets the team focus on high-value opportunities
- Reduce response time: Shorten the cycle from opportunity discovery to first outreach
- Improve data quality: Unify data sources, eliminate duplication and inconsistency
- Enhance traceability: Full-lifecycle status tracking, every action auditable
3. Project Objectives & Success Criteria
3.1 Phase 1 Objectives
Build an AI Opportunity Platform serving two core roles β Researchers and Schedulers β achieving end-to-end automation of opportunity acquisition, research, scheduling, and handoff.
3.2 Success Criteria
| ID |
Success Criterion |
Measurement |
Target |
| SC-01 |
Opportunity screening automation rate |
AI-processed / Total opportunities |
β₯ 70% |
| SC-02 |
Research summary generation time |
From import to summary output |
β€ 5 minutes |
| SC-03 |
Scheduling recommendation adoption rate |
Adopted / Total recommendations |
β₯ 60% |
| SC-04 |
Handoff information completeness rate |
Complete packages / Total handoffs |
β₯ 95% |
| SC-05 |
System availability |
Monthly uptime |
β₯ 99.5% |
| SC-06 |
Pilot user satisfaction |
Pilot user survey score |
β₯ 4.0 / 5.0 |
3.3 Expected Business Benefits
- Reduce researcher manual screening time by 50%+
- Shorten opportunity-to-outreach cycle by 30%+
- Eliminate duplicate data entry, achieve 95%+ data consistency
4. Scope Definition
4.1 Phase 1 Scope (In-Scope)
| Scope Area |
Includes |
| Opportunity Acquisition |
Auto-import opportunity data from Dodge / ConstructConnect |
| Researcher Workflow |
AI-assisted screening, enrichment, assessment, prioritization |
| Scheduler Workflow |
AI scheduling recommendations, communication plan review, task assignment |
| AI Research Summaries |
Auto-generate opportunity/project summaries for researchers |
| AI Scheduling Recommendations |
Generate scheduling recommendations based on rules and models |
| Human Review Gates |
Retain manual review at key nodes (Human-in-the-loop) |
| Agent Task Management |
AI Agents autonomously execute tasks and track status |
| Status Tracking |
Full-lifecycle opportunity status flow and visualization |
| Reporting Dashboard |
Opportunity funnel, team performance, AI output quality visualization |
| Notifications & Reminders |
Key event-triggered notifications (email/in-system) |
4.2 Out of Phase 1 Scope (Out-of-Scope)
| Excluded Item |
Rationale |
Future Phase |
| Mobile App |
Phase 1 supports Web only |
Phase 2 |
| Customer Portal |
External customer self-service platform |
Phase 2 |
| Multi-language Support |
Phase 1 supports English only |
Phase 2 |
| Advanced BI Analytics |
Phase 1 provides basic dashboards; deep analysis in later iterations |
Phase 2 |
| Third-party Data Source Expansion |
Phase 1 integrates Dodge and ConstructConnect only |
Phase 2 |
| Automated Contracting |
Contract/signing process not in this phase |
Phase 3 |
| Full Historical Data Migration |
Only migrate active opportunity data for pilot regions |
Phase 2 |
4.3 Scope Boundary Diagram
βββββββββββββββββββββββββββββββββββββββββββββββββββββββββββ
β GIGA CRM Phase 1 β
β β
β ββββββββββββ ββββββββββββ ββββββββββββ β
β βOpp. Acq. ββββΆβResearcherββββΆβScheduler β β
β β(AI Import)β βWorkflow β βWorkflow β β
β ββββββββββββ ββββββ¬ββββββ ββββββ¬ββββββ β
β β β β
β βΌ βΌ β
β ββββββββββββββ ββββββββββββ β
β βHuman Reviewβ βHandoff Pkgβ β
β β Gate β β Generated β β
β ββββββββββββββ ββββββββββββ β
β β β
β βΌ β
β ββββββββββββββββββββββ β
β βStatus Tracking + β β
β β Dashboard β β
β ββββββββββββββββββββββ β
β β
βββββββββββββββββββββββββββββββββββββββββββββββββββββββββββ
Data Sources: Dodge | ConstructConnect
5. Stakeholders & User Roles
5.1 Stakeholders
| Role |
Responsibilities |
Key Concerns |
| Project Sponsor |
Project authorization & resource approval |
ROI, milestone achievement |
| Business Owner |
Business requirement definition & priorities |
Process improvement, user adoption |
| Product Manager |
Requirement management & prioritization |
Requirement completeness, delivery cadence |
| Solution Architect |
Technical solution design |
Architecture soundness, scalability |
| Development Team |
Feature development & unit testing |
Code quality, technical feasibility |
| AI/ML Engineer |
AI Agent & model development |
Model accuracy, inference performance |
| Testing Team |
Test planning & execution |
Defect rate, acceptance pass rate |
| Pilot Users |
Trial usage & feedback |
Usability, feature completeness |
5.2 User Personas
Persona A: Researcher
βββββββββββββββββββββββββββββββββββββββ
β Researcher β
βββββββββββββββββββββββββββββββββββββββ€
β Responsibilities: β
β - Review AI-screened opportunities β
β - Enrich and assess opportunities β
β - Generate or review AI summaries β
β - Determine opportunity priority β
β - Hand off qualified opportunities β
β to Scheduler β
β β
β Pain Points: β
β - Manual screening is time-consumingβ
β - Information scattered, multi-system switching β
β - No unified priority standards β
β β
β Expectations: β
β - AI pre-screening reduces 70% manual work β
β - One-stop workspace β
β - AI summaries for quick overview β
βββββββββββββββββββββββββββββββββββββββ
Persona B: Scheduler
βββββββββββββββββββββββββββββββββββββββ
β Scheduler β
βββββββββββββββββββββββββββββββββββββββ€
β Responsibilities: β
β - Receive handed-off opportunities β
β - Review communication plans β
β - Arrange follow-ups per AI recs β
β - Manage opportunity status flow β
β - Monitor pipeline health β
β β
β Pain Points: β
β - Incomplete handoff information β
β - Scheduling relies on experience, lacks data β
β - Status tracking via spreadsheets, poor real-time β
β β
β Expectations: β
β - Complete, structured handoff packages β
β - Data-driven AI scheduling recs β
β - Real-time dashboard monitoring β
βββββββββββββββββββββββββββββββββββββββ
Persona C: Manager
| Attribute |
Description |
| Responsibilities |
Monitor team performance, approve key decisions, review summary reports |
| Pain Points |
Lack of real-time visibility, scattered data |
| Expectations |
Real-time dashboards, one-click report export |
6. Functional Requirements
6.1 Module Overview
| Module ID |
Module Name |
Priority |
Related Deliverable |
| F-01 |
Platform Foundation & User Management |
P0 |
D-01 |
| F-02 |
Opportunity Data Import |
P0 |
D-02 |
| F-03 |
Opportunity Screening & Filtering |
P0 |
D-03 |
| F-04 |
Opportunity Enrichment & Assessment |
P1 |
D-04 |
| F-05 |
Communication Plan Review |
P1 |
D-05 |
| F-06 |
Human Review Gate |
P0 |
D-06 |
| F-07 |
Handoff Package Management |
P1 |
D-07 |
| F-08 |
Reporting & Visualization |
P2 |
D-08 |
| F-09 |
Pilot Enablement |
P1 |
D-09 |
| F-10 |
Status Tracking |
P0 |
D-10 |
| F-11 |
Notifications & Reminders |
P2 |
D-11 |
Objective: Build platform infrastructure, provide user authentication, permission management, and basic configuration capabilities.
| Req ID |
Requirement Description |
Priority |
Acceptance Criteria |
| F-01-01 |
Support user registration, login, password reset |
P0 |
Users can complete registration-to-login flow independently |
| F-01-02 |
Support role-based access control (RBAC) |
P0 |
Three roles: Researcher/Scheduler/Manager with mutually exclusive permissions |
| F-01-03 |
Support organizational structure management |
P1 |
Users can be managed by team/department |
| F-01-04 |
Support system configuration management |
P1 |
Admins can configure data source connections, AI parameters, etc. |
| F-01-05 |
Support operation audit logging |
P1 |
All key operations are logged and queryable |
| F-01-06 |
Support session management & timeout |
P0 |
Auto-logout after 30 minutes of inactivity |
6.3 F-02: Opportunity Data Import
Objective: Automatically import opportunity data from external sources, with cleaning and deduplication support.
| Req ID |
Requirement Description |
Priority |
Acceptance Criteria |
| F-02-01 |
Support scheduled Dodge data import |
P0 |
Daily auto-pull, with manual trigger support |
| F-02-02 |
Support scheduled ConstructConnect data import |
P0 |
Daily auto-pull, with manual trigger support |
| F-02-03 |
Data field mapping & transformation |
P0 |
External fields auto-mapped to internal data model |
| F-02-04 |
Data deduplication logic |
P0 |
Same project from multiple sources auto-identified and merged |
| F-02-05 |
Import exception handling & alerting |
P0 |
Notify admin on import failure, support retry |
| F-02-06 |
Support manual CSV/Excel import |
P1 |
Admins can upload files for supplementary import |
| F-02-07 |
Import history & traceability |
P1 |
View records, counts, and status for each import |
Data Import Flow:
Dodge API βββ
ββββΆ Field Mapping βββΆ Dedup & Merge βββΆ Data Cleaning βββΆ DB
ConstructConnect API βββ β
βββ Error? βββΆ Alert Notification
βββ Success? βββΆ Log Record
6.4 F-03: Opportunity Screening & Filtering
Objective: AI auto-screens opportunities, Researcher reviews and confirms, reducing manual workload.
| Req ID |
Requirement Description |
Priority |
Acceptance Criteria |
| F-03-01 |
AI screening rule configuration |
P0 |
Admins can configure criteria (industry, region, scale, stage, etc.) |
| F-03-02 |
AI opportunity scoring |
P0 |
Each opportunity has AI score (0-100) with scoring rationale |
| F-03-03 |
Opportunity list view |
P0 |
List/card views, sortable, filterable, paginated |
| F-03-04 |
Batch operations |
P1 |
Batch accept/reject/adjust priority |
| F-03-05 |
Screening results export |
P2 |
Export to Excel/CSV |
| F-03-06 |
Screening rule versioning |
P2 |
Rule changes traceable, with rollback support |
Screening Flow:
Opp. Pool βββΆ AI Scoring βββΆ Auto-filter Low Scores βββΆ Researcher Review
β
βββ Accept βββΆ Enter Enrichment
βββ Reject βββΆ Flag & Record Reason
βββ Adjust βββΆ Modify Priority & Accept
6.5 F-04: Opportunity Enrichment & Assessment
Objective: Enrich and deeply assess opportunities that pass screening.
| Req ID |
Requirement Description |
Priority |
Acceptance Criteria |
| F-04-01 |
Auto information enrichment |
P1 |
AI auto-supplements contacts, company info, history, etc. |
| F-04-02 |
AI research summary generation |
P0 |
One-click summary (project overview, key contacts, competitive landscape) |
| F-04-03 |
Opportunity assessment checklist |
P1 |
Structured checklist (project scale, timeline, decision-makers, etc.) |
| F-04-04 |
Priority tag management |
P1 |
Support High/Medium/Low and custom tags |
| F-04-05 |
Assessment history |
P1 |
Each assessment result traceable, comparable |
| F-04-06 |
AI summary manual editing |
P0 |
Researchers can edit/correct AI-generated summaries |
6.6 F-05: Communication Plan Review
Objective: AI generates communication plan recommendations; Scheduler reviews and confirms before execution.
| Req ID |
Requirement Description |
Priority |
Acceptance Criteria |
| F-05-01 |
AI communication plan generation |
P1 |
Generate recommended communication method, frequency, talking points |
| F-05-02 |
Communication plan template management |
P1 |
Pre-built templates, support customization |
| F-05-03 |
Communication plan approval flow |
P0 |
Scheduler review β Manager confirmation (high priority) |
| F-05-04 |
Communication record tracking |
P1 |
Record each communication outcome, linked to opportunity |
| F-05-05 |
Communication effectiveness analysis |
P2 |
Track reply rate, conversion rate, etc. |
6.7 F-06: Human Review Gate (Human-in-the-Loop)
Objective: Retain manual review at key nodes in the AI automation flow to ensure quality and compliance.
| Req ID |
Requirement Description |
Priority |
Acceptance Criteria |
| F-06-01 |
Review gate configuration |
P0 |
Admins can configure which nodes require manual review |
| F-06-02 |
Review task assignment |
P0 |
Review tasks auto-assigned to appropriate roles |
| F-06-03 |
Review interface |
P0 |
Display AI decision context, support approve/reject/return |
| F-06-04 |
Review SLA management |
P1 |
Set review time limits, auto-escalate on timeout |
| F-06-05 |
Review feedback loop |
P1 |
Review results fed back to AI model for continuous optimization |
Review Gate Nodes:
AI Screening βββΆ [Gate 1] Researcher confirms opportunity
β
AI Enrichment βββΆ [Gate 2] Researcher confirms summary
β
AI Comm. Plan βββΆ [Gate 3] Scheduler confirms plan
β
AI Handoff Pkg βββΆ [Gate 4] Scheduler confirms receipt
6.8 F-07: Handoff Package Management
Objective: Structure handoff information from Researcher to Scheduler, ensuring completeness.
| Req ID |
Requirement Description |
Priority |
Acceptance Criteria |
| F-07-01 |
Auto handoff package generation |
P1 |
AI compiles opp info, summary, assessment into handoff package |
| F-07-02 |
Handoff package template |
P0 |
Standardized template: project overview, key contacts, assessment conclusion, recommended actions |
| F-07-03 |
Handoff confirmation flow |
P0 |
Send β Recipient confirms β Complete/Return |
| F-07-04 |
Handoff package versioning |
P1 |
Multi-version support, new version on return-and-modify |
| F-07-05 |
Handoff statistics report |
P2 |
Track handoff count, return rate, avg processing time |
Handoff Package Structure:
βββββββββββββββββββββββββββββββββββ
β Handoff Package β
βββββββββββββββββββββββββββββββββββ€
β 1. Project Basic Info β
β - Project name, address, type β
β - Owner/GC information β
β 2. Opportunity Assessment Summaryβ
β - AI summary + manual edits β
β - Score & priority β
β 3. Key Contacts β
β - Name, role, contact info β
β - Decision chain analysis β
β 4. Recommended Actions β
β - AI recommendations + β
β Researcher additions β
β 5. Attachments β
β - Related documents, images β
βββββββββββββββββββββββββββββββββββ
6.9 F-08: Reporting & Visualization
Objective: Provide visual dashboards for opportunity funnel, team performance, and AI output quality.
| Req ID |
Requirement Description |
Priority |
Acceptance Criteria |
| F-08-01 |
Opportunity funnel dashboard |
P1 |
Show opportunity count and conversion rate by stage |
| F-08-02 |
Team performance dashboard |
P1 |
Display processing volume, efficiency metrics by user/team |
| F-08-03 |
AI output quality report |
P1 |
AI scoring accuracy, adoption rate, summary quality score |
| F-08-04 |
Custom reports |
P2 |
Generate custom reports with selectable dimensions/metrics |
| F-08-05 |
Report export |
P2 |
Export to PDF/Excel |
| F-08-06 |
Scheduled report emails |
P2 |
Auto-send daily/weekly reports |
6.10 F-09: Pilot Enablement
Objective: Provide training, documentation, and support for pilot users to ensure smooth transition.
| Req ID |
Requirement Description |
Priority |
Acceptance Criteria |
| F-09-01 |
User operation manual |
P1 |
Cover operation guides for all functional modules |
| F-09-02 |
Online training courses |
P1 |
Role-based training videos and exercises |
| F-09-03 |
In-system guided tips |
P2 |
Guided tutorial for first-time users |
| F-09-04 |
Pilot support channel |
P0 |
Provide ticket/instant messaging support |
| F-09-05 |
Pilot feedback collection |
P1 |
In-system feedback entry, regular user opinion collection |
6.11 F-10: Status Tracking
Objective: Full-lifecycle opportunity status flow and visualization, with audit traceability.
| Req ID |
Requirement Description |
Priority |
Acceptance Criteria |
| F-10-01 |
Opportunity state machine definition |
P0 |
Clearly define states and transition rules |
| F-10-02 |
Auto status transition |
P0 |
AI Agent advances status based on workflow |
| F-10-03 |
Manual status intervention |
P0 |
Users can manually adjust status (requires permission) |
| F-10-04 |
Status change history |
P0 |
Complete record of status changes (who, when, why) |
| F-10-05 |
Status board view |
P1 |
Kanban-style display of opportunities by status |
| F-10-06 |
Timeout alerts |
P1 |
Alert when opportunity stays in a status beyond threshold |
Opportunity State Machine:
ββββββββββββ ββββββββββββ ββββββββββββ ββββββββββββ
β Imported ββββββΆβAI Screen ββββββΆβPending βββββΆβEnriching β
β β β β βReview β β β
ββββββββββββ ββββββββββββ ββββββββββββ ββββββ¬ββββββ
β
ββββββββββββ ββββββββββββ ββββββββββββ β
β Closed βββββββCompleted βββββββSchedulingβββββββββ
β β β β β β
ββββββββββββ ββββββββββββ ββββββββββββ
β² β
β ββββββββββββ β
ββββββββββββ Rejected ββββββββββββββββ
β β
ββββββββββββ
6.12 F-11: Notifications & Reminders
Objective: Key event-triggered notifications to ensure timely response from relevant personnel.
| Req ID |
Requirement Description |
Priority |
Acceptance Criteria |
| F-11-01 |
Review task notification |
P0 |
Notify assigned personnel when new review task is created |
| F-11-02 |
Handoff notification |
P0 |
Notify both parties on handoff package send/receive |
| F-11-03 |
Timeout reminder |
P1 |
Remind 1 hour before task timeout |
| F-11-04 |
Import exception notification |
P0 |
Notify admin on data import failure |
| F-11-05 |
Daily summary email |
P2 |
Send daily work summary to users |
| F-11-06 |
Notification preferences |
P2 |
Users can configure receiving method and frequency |
Notification Channels:
- In-system messages
- Email notifications
- (Future phase) Feishu/WeCom push
7. Non-Functional Requirements
| ID |
Requirement |
Metric |
| NFR-P-01 |
Page load time |
First screen β€ 3s (P95) |
| NFR-P-02 |
API response time |
Standard API β€ 500ms (P95), complex query β€ 2s (P95) |
| NFR-P-03 |
AI summary generation time |
β€ 30s (P95) |
| NFR-P-04 |
Data import throughput |
β₯ 5,000 opportunities per import |
| NFR-P-05 |
Concurrent user support |
β₯ 100 concurrent users |
7.2 Availability Requirements
| ID |
Requirement |
Metric |
| NFR-A-01 |
System availability |
β₯ 99.5% (monthly) |
| NFR-A-02 |
Planned maintenance window |
Once per month, 48-hour advance notice |
| NFR-A-03 |
Recovery Time Objective (RTO) |
β€ 4 hours |
| NFR-A-04 |
Recovery Point Objective (RPO) |
β€ 1 hour |
7.3 Security Requirements
| ID |
Requirement |
Description |
| NFR-S-01 |
Data transmission encryption |
Site-wide HTTPS / TLS 1.2+ |
| NFR-S-02 |
Data storage encryption |
Sensitive fields encrypted at rest |
| NFR-S-03 |
Authentication mechanism |
Support SSO (OAuth 2.0 / SAML) |
| NFR-S-04 |
Least privilege |
Role-based fine-grained access control |
| NFR-S-05 |
Audit logs |
Retained β₯ 1 year, tamper-proof |
| NFR-S-06 |
Data backup |
Daily full + real-time incremental backup |
7.4 Scalability Requirements
| ID |
Requirement |
Description |
| NFR-E-01 |
Horizontal scaling |
Core services support horizontal scaling, stateless design |
| NFR-E-02 |
Data source extensibility |
Data ingestion layer supports plug-in style new data sources |
| NFR-E-03 |
AI model extensibility |
AI inference layer supports hot model replacement |
| NFR-E-04 |
Multi-tenancy readiness |
Architecture pre-reserves multi-tenancy capability (Phase 2) |
7.5 Compatibility Requirements
| ID |
Requirement |
Description |
| NFR-C-01 |
Browser compatibility |
Chrome 100+, Edge 100+, Safari 15+ |
| NFR-C-02 |
Resolution support |
Minimum 1280Γ720, recommended 1920Γ1080 |
| NFR-C-03 |
Responsive design |
Support tablet landscape access (basic adaptation) |
8. System Architecture Overview
8.1 Architecture Overview
βββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββ
β GIGA CRM Architecture β
β β
β βββββββββββββββββββββββββββββββββββββββββββββββββββββββ β
β β AI Agent Runtime (Sandboxed) β β
β β βββββββββββ βββββββββββ βββββββββββ β β
β β βScreen β βSummary β βSchedule β β β
β β βAgent β βAgent β βAgent β β β
β β βββββββββββ βββββββββββ βββββββββββ β β
β βββββββββββββββββββββββββ¬ββββββββββββββββββββββββββββββ β
β β β
β βββββββββββββββββββββββββΌββββββββββββββββββββββββββββββ β
β β CRM Core Operational Database β β
β β Opp. DB | Contacts | Status | Audit | Config β β
β βββββββββββββββββββββββββ¬ββββββββββββββββββββββββββββββ β
β β β
β βββββββββββββββββββββββββΌββββββββββββββββββββββββββββββ β
β β Operational Modules β β
β β ββββββββ ββββββββ ββββββββ ββββββββ ββββββββ β β
β β βImportβ βScreenβ βEnrichβ βHandoffβ βReportβ β β
β β ββββββββ ββββββββ ββββββββ ββββββββ ββββββββ β β
β βββββββββββββββββββββββββ¬ββββββββββββββββββββββββββββββ β
β β β
β βββββββββββββββββββββββββΌββββββββββββββββββββββββββββββ β
β β Data Layer β β
β β Dodge API | CC API | File Storage | Cache β β
β βββββββββββββββββββββββββββββββββββββββββββββββββββββββ β
βββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββ
8.2 Architecture Layer Description
| Layer |
Name |
Responsibility |
Key Technology (Suggested) |
| L1 |
AI Agent Runtime |
AI Agent execution environment, sandboxed |
Python / LangChain / Containerized |
| L2 |
CRM Core Database |
Core business data storage |
PostgreSQL / Redis (cache) |
| L3 |
Operational Modules |
Business function modules |
Microservices / Modular Monolith |
| L4 |
Data Layer |
External data integration & file storage |
API Gateway / S3-compatible storage |
8.3 Key Architecture Decisions (Pending Solution Design)
| Decision Point |
Option A |
Option B |
Recommendation |
| Deployment Architecture |
Modular Monolith |
Microservices |
Phase 1: Modular Monolith to reduce complexity |
| AI Inference |
Cloud API |
On-premise |
Hybrid: Cloud API for summaries, local model for screening |
| Frontend Framework |
React |
Vue |
Based on team tech stack |
| Message Queue |
RabbitMQ |
Kafka |
Phase 1: RabbitMQ, sufficient for needs |
9. Data Requirements
9.1 Core Data Entities
| Entity |
Description |
Key Fields |
Source |
| Opportunity |
Core business object |
ID, project name, address, type, stage, score, status |
Dodge / CC |
| Contact |
Opportunity-linked contact |
ID, name, role, phone, email, company |
Enrichment / Manual |
| Company |
Owner/GC company |
ID, name, address, type, industry |
Enrichment / Manual |
| Assessment |
Opportunity assessment record |
ID, opp ID, assessor, score, summary, date |
System generated |
| HandoffPackage |
Handoff information package |
ID, opp ID, sender, receiver, content, status |
System generated |
| AgentTask |
AI Agent executed task |
ID, type, input, output, status, timestamp |
System generated |
| AuditLog |
Operation audit |
ID, user, action, object, time, details |
System recorded |
9.2 Data Retention Policy
| Data Type |
Retention Period |
Archive Strategy |
| Active opportunity data |
Permanent |
β |
| Closed opportunities |
3 years |
Archive to cold storage after 3 years |
| Audit logs |
1 year |
Compress and archive after 1 year |
| Agent task records |
6 months |
Aggregate stats then delete details after 6 months |
| AI summary history |
2 years |
Retain only final version after 2 years |
10. AI / Agent Requirements
10.1 AI Agent Definitions
| Agent Name |
Responsibility |
Input |
Output |
Trigger |
| Screening Agent |
Opportunity scoring & filtering |
Raw opportunity data |
Score + screening result |
Scheduled / Event |
| Summary Agent |
Generate research summaries |
Opportunity + enrichment info |
Structured summary |
User request |
| Scheduling Agent |
Generate scheduling recommendations |
Opportunity + history + rules |
Scheduling recommendation |
Status change |
| Enrichment Agent |
Information supplementation |
Basic opportunity info |
Supplemented contacts/company info |
Event triggered |
10.2 Agent Operation Modes
βββββββββββββββββββββββββββββββββββββββββββ
β Agent-assisted Mode β
β AI provides recommendations/drafts β β
β Human reviews and confirms β
β Applies to: Screening, Summary, β
β Communication Plan β
βββββββββββββββββββββββββββββββββββββββββββ€
β Agent-operated Mode β
β AI executes autonomously β β
β Notifies human of results β
β Applies to: Data Import, Enrichment, β
β Status Transitions β
βββββββββββββββββββββββββββββββββββββββββββ€
β Human-gated Mode β
β AI prepares β β
β Must be approved by human to proceed β
β Applies to: Handoff Package Send, β
β High-priority Opportunity Processing β
βββββββββββββββββββββββββββββββββββββββββββ
10.3 AI Quality Requirements
| Metric |
Target |
Measurement |
| Screening accuracy |
β₯ 80% |
Manual sampling verification |
| Summary completeness |
β₯ 90% |
Key information coverage rate |
| Scheduling recommendation reasonableness |
β₯ 60% adoption rate |
User adoption ratio |
| Enrichment information accuracy |
β₯ 85% |
Manual sampling verification |
| AI response time |
β€ 30s |
P95 latency |
10.4 AI Feedback Loop
AI Output βββΆ Human Review βββΆ Review Result
β
ββββββββ΄βββββββ
β Accept β Reject/Modify
β β
βΌ βΌ
Record Positive Record Negative + Correction
β β
ββββββββ¬ββββββββ
β
βΌ βΌ
Periodic Model Retraining / Prompt Optimization
11. Integration Requirements
11.1 External System Integrations
| System |
Integration Method |
Data Flow |
Frequency |
Priority |
| Dodge |
REST API |
Pull opportunity data |
Daily |
P0 |
| ConstructConnect |
REST API |
Pull opportunity data |
Daily |
P0 |
| Email System (SMTP) |
SMTP |
Send notifications |
Event-triggered |
P0 |
| SSO (OAuth 2.0) |
OAuth |
User authentication |
Real-time |
P0 |
| File Storage (S3) |
SDK |
Store attachments |
Real-time |
P1 |
11.2 Integration Interface Specifications
Dodge API Integration
βββ Endpoint: [TBD]
βββ Authentication: API Key
βββ Frequency: Daily 02:00 UTC
βββ Data Format: JSON
βββ Error Handling: Retry 3 times, 5-min interval
βββ Alerting: Notify admin after 2 consecutive failures
ConstructConnect API Integration
βββ Endpoint: [TBD]
βββ Authentication: API Key
βββ Frequency: Daily 03:00 UTC
βββ Data Format: JSON
βββ Error Handling: Retry 3 times, 5-min interval
βββ Alerting: Notify admin after 2 consecutive failures
12. Deliverable Mapping
12.1 Charter Deliverable to PRD Requirement Mapping
| Deliverable ID |
Deliverable Name |
Corresponding Module |
Related Requirements |
Acceptance Status |
| D-01 |
Platform Foundation |
F-01 |
F-01-01 ~ F-01-06 |
Pending |
| D-02 |
Data Import |
F-02 |
F-02-01 ~ F-02-07 |
Pending |
| D-03 |
Opportunity Screening |
F-03 |
F-03-01 ~ F-03-06 |
Pending |
| D-04 |
Enrichment & Assessment |
F-04 |
F-04-01 ~ F-04-06 |
Pending |
| D-05 |
Comm. Plan Review |
F-05 |
F-05-01 ~ F-05-05 |
Pending |
| D-06 |
Human Review Gate |
F-06 |
F-06-01 ~ F-06-05 |
Pending |
| D-07 |
Handoff Package |
F-07 |
F-07-01 ~ F-07-05 |
Pending |
| D-08 |
Reporting & Visualization |
F-08 |
F-08-01 ~ F-08-06 |
Pending |
| D-09 |
Pilot Enablement |
F-09 |
F-09-01 ~ F-09-05 |
Pending |
| D-10 |
Status Tracking |
F-10 |
F-10-01 ~ F-10-06 |
Pending |
| D-11 |
Notifications & Reminders |
F-11 |
F-11-01 ~ F-11-06 |
Pending |
13. Milestones & Timeline
13.1 Milestone Overview
| Milestone |
Target Date |
Key Deliverables |
Status |
| M1: Solution Design |
June 2026 |
Technical solution design document |
β
Completed |
| M2: Core Platform |
July 2026 |
F-01, F-02, F-10 foundation features |
π In Progress |
| M3: AI Workflows |
August 2026 |
F-03, F-04, F-05, F-06, F-07 |
β³ Pending |
| M4: UAT |
September 2026 |
Full-function testing + defect resolution |
β³ Pending |
| M5: Pilot Go-Live |
September 2026 |
Pilot user launch |
β³ Pending |
| M6: Stabilized |
October 2026 |
System stable operation |
β³ Pending |
| M7: Closure |
October 2026 |
Project closure and handover |
β³ Pending |
13.2 Phase 1 Feature-to-Milestone Mapping
July (M2) August (M3) September (M4-M5) October (M6-M7)
βββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββ
F-01 Platform ββββββββ
F-02 Data Import ββββββββ
F-10 Status Trk ββββββββ
F-03 Screening ββββββββ
F-04 Enrichment ββββββββ
F-05 Comm. Plan ββββββββ
F-06 Review Gate ββββββββ
F-07 Handoff Pkg ββββββββ
F-08 Reporting ββββββββ
F-09 Pilot Enable ββββββββ
F-11 Notifications ββββββββ
UAT Testing Pilot Live Stabilize&Close
14. Assumptions, Dependencies & Risks
14.1 Assumptions
| ID |
Assumption |
Impact |
| A-01 |
Dodge and ConstructConnect APIs remain stable and available during the project |
Data import dependency |
| A-02 |
Pilot users are willing to participate in testing and provide feedback |
UAT and pilot success |
| A-03 |
AI model accuracy meets minimum business requirements (β₯ 80%) |
System usability |
| A-04 |
Project team is staffed, no key role vacancies |
On-time delivery |
14.2 Dependencies
| ID |
Dependency |
Dependent Party |
Impact |
Mitigation |
| D-01 |
Dodge API access & documentation |
Data provider |
Blocks data import development |
Request access early, confirm API docs |
| D-02 |
ConstructConnect API access |
Data provider |
Same as above |
Same as above |
| D-03 |
SSO system integration |
IT Infrastructure team |
Blocks auth development |
Coordinate integration timing early |
| D-04 |
AI model training data |
Business team |
Affects AI accuracy |
Collect historical labeled data |
| D-05 |
Pilot user list confirmation |
Business owner |
Affects UAT scheduling |
Confirm by end of August |
14.3 Risk Register
| ID |
Risk |
Probability |
Impact |
Risk Level |
Mitigation Strategy |
| R-01 |
Dodge/CC API changes or unavailability |
Medium |
High |
π΄ High |
Design data source abstraction layer; manual import fallback |
| R-02 |
AI model accuracy below target |
Medium |
High |
π΄ High |
Reserve model tuning time; rules engine fallback |
| R-03 |
Low pilot user adoption |
Medium |
Medium |
π‘ Medium |
Early training; simplify workflows; rapid feedback iteration |
| R-04 |
Project staff changes |
Low |
High |
π‘ Medium |
Designate backups for key roles; document-driven management |
| R-05 |
Scope creep |
Medium |
Medium |
π‘ Medium |
Strict change management; Phase 2 backlog |
| R-06 |
Data migration quality issues |
Low |
Medium |
π’ Low |
Pre-migration data cleansing; batch migration verification |
15. Acceptance Criteria
15.1 Functional Acceptance Criteria
Acceptance criteria for each functional requirement are defined in Section 6. Overall functional acceptance must meet:
- All P0 requirements: 100% implemented and tested
- All P1 requirements: β₯ 90% implemented and tested
- P2 requirements: delivered as feasible, do not block go-live
15.2 UAT Acceptance Criteria
| Acceptance Item |
Standard |
| Test case coverage |
β₯ 90% of functional requirements have test cases |
| Test pass rate |
β₯ 95% |
| Critical defects |
0 P0 defects |
| Major defects |
β€ 5 P1 defects (with fix plan) |
| Pilot user acceptance |
Pilot users sign off |
15.3 Go-Live Checklist
- [ ] All P0 features pass UAT
- [ ] Performance metrics met (Section 7.1)
- [ ] Security review passed
- [ ] Data backup & recovery process verified
- [ ] Ops monitoring & alerting configured
- [ ] User manual and training materials ready
- [ ] Pilot user training completed
- [ ] Emergency rollback plan ready
16. Glossary
| Term |
Abbreviation |
Definition |
| Opportunity |
Opp |
Potential construction project business opportunity |
| Researcher |
β |
Role responsible for screening, enriching, and assessing opportunities |
| Scheduler |
β |
Role responsible for receiving handed-off opportunities and arranging follow-ups |
| Enrichment |
β |
Process of supplementing and completing opportunity information |
| Handoff Package |
β |
Structured information package from Researcher to Scheduler |
| Human Review Gate |
β |
Key manual review node in AI workflow |
| Agent-assisted |
β |
AI-assisted mode: AI provides recommendations, human confirms |
| Agent-operated |
β |
AI autonomous mode: AI executes, then notifies human |
| Human-gated |
β |
Human-gated mode: AI prepares, human must approve to proceed |
| Dodge |
β |
Construction project information data service provider |
| ConstructConnect |
CC |
Construction project information data service provider |
| RBAC |
Role-Based Access Control |
Role-based access control |
| RTO |
Recovery Time Objective |
Target time for system recovery after failure |
| RPO |
Recovery Point Objective |
Target data recovery point after failure |
| UAT |
User Acceptance Testing |
User acceptance testing |
| WBS |
Work Breakdown Structure |
Work breakdown structure |
17. Appendix
Appendix A: Charter Key Points Summary
Core points from the Project Charter (Approved 2026-06-23), for PRD alignment reference.
- Project Name: GIGA CRM
- Assessment/Approval Date: 2026/06/23
- Phase 1 Objective: Build AI Opportunity Platform
- Core Users: Researchers, Schedulers
- Data Sources: Dodge, ConstructConnect
- Architecture: AI-first CRM
- Deliverables: 11 items (D-01 ~ D-11)
Appendix B: Open Items
| ID |
Open Item |
Owner |
Due Date |
Status |
| O-01 |
Dodge API endpoint & authentication method |
β |
β |
Pending |
| O-02 |
ConstructConnect API endpoint & authentication method |
β |
β |
Pending |
| O-03 |
SSO system type & protocol |
β |
β |
Pending |
| O-04 |
AI model selection (cloud vs. on-premise) |
β |
β |
Pending |
| O-05 |
Deployment environment (cloud / on-premise) |
β |
β |
Pending |
| O-06 |
Pilot user list & scale |
β |
β |
Pending |
| O-07 |
Frontend tech stack selection |
β |
β |
Pending |
Appendix C: Requirements Traceability Matrix Template
| Req ID |
Requirement Description |
Source (Charter Clause) |
Priority |
Status |
Design Doc |
Dev Task |
Test Case |
| F-01-01 |
User registration & login |
Charter-Deliverable D-01 |
P0 |
Draft |
TBD |
TBD |
TBD |
| F-02-01 |
Dodge data import |
Charter-Deliverable D-02 |
P0 |
Draft |
TBD |
TBD |
TBD |
| ... |
... |
... |
... |
... |
... |
... |
... |
End of Document
This PRD is Draft v0.1 framework version. It requires the following process before finalization:
1. Business review β Confirm scope and priorities
2. Architect review β Confirm technical feasibility
3. Development team review β Confirm implementation approach
4. Project manager approval β Confirm schedule and resources
5. Final release β v1.0 Baseline
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