Docxa
High Level Design (HLD)
Version: 1.0 Status: Architecture Review Draft Author: Abhijit Chatterjee
1. Purpose
This document defines the High Level Design (HLD) for the Docxa platform.
Docxa is an AI-assisted documentation intelligence system designed to generate structured software project documentation by combining stakeholder knowledge and technical project artifacts.
This document describes:
- System architecture
- Core components
- Data flows
- Architectural principles
- Deployment model
- Security and scalability considerations
Implementation details will be defined later in the Low Level Design (LLD).
2. System Overview
Docxa enables teams to automatically generate structured project documentation such as:
- Product Requirement Documents (PRD)
- Business Requirement Documents (BRD)
- Technical Requirement Documents (TRD)
- High Level Design (HLD)
- Low Level Design (LLD)
- Non-Functional Requirements (NFR)
- Architecture Decision Records (ADR)
The system gathers knowledge from:
- Stakeholder interviews
- Repository analysis
- Existing documentation
Docxa synthesizes this information into structured documentation artifacts.
3. C4 Level 1 — System Context
+-------------------------------------------------------+
| Stakeholders |
|-------------------------------------------------------|
| Product Managers | Architects | Developers | DevOps |
+-----------------------------+-------------------------+
|
v
+-------------+
| Docxa |
| Documentation
| Intelligence |
+-------------+
|
v
+--------------------+
| Project Repository |
| Source Code |
+--------------------+
4. C4 Level 2 — Container Architecture
+-----------------------------------------------------+
| Docxa System |
|-----------------------------------------------------|
| CLI Interface |
| VSCode Extension |
| Teams Integration |
| | |
| v |
| Command Processor |
| | |
| v |
| Knowledge Capture Engine |
| | |
| v |
| Evidence Store |
| | |
| v |
| Documentation Planner |
| | |
| v |
| Document Generation Engine |
+-----------------------------------------------------+
5. C4 Level 3 — Component Architecture
Document Generation Engine
+----------------------------------------------------------+
| Document Generation Engine |
|----------------------------------------------------------|
| Template Manager |
| Evidence Aggregator |
| LLM Interface |
| Output Formatter |
+----------------------------------------------------------+
Knowledge Capture Engine
+----------------------------------------------------------+
| Knowledge Capture Engine |
|----------------------------------------------------------|
| Interview Manager |
| Role Template Engine |
| Question Generator |
| Answer Parser |
| Evidence Builder |
+----------------------------------------------------------+
6. Evidence Model
+---------------------+
| Interview Evidence |
+---------------------+
|
v
+---------------------+
| Evidence Model |
|---------------------|
| Stakeholder Data |
| Repository Signals |
| Documentation Input |
+---------------------+
|
v
+---------------------+
| Documentation Gen |
+---------------------+
7. Interview Engine Architecture
+-----------------------+
| Interview Controller |
+-----------------------+
|
v
+-----------------------+
| Role Template Engine |
+-----------------------+
|
v
+-----------------------+
| Question Generator |
+-----------------------+
|
v
+-----------------------+
| Answer Parser |
+-----------------------+
|
v
+-----------------------+
| Evidence Builder |
+-----------------------+
8. Document Generation Pipeline
Evidence Sources
|
v
+---------------------+
| Evidence Aggregator |
+---------------------+
|
v
+---------------------+
| Template Resolver |
+---------------------+
|
v
+---------------------+
| LLM Interface |
+---------------------+
|
v
+---------------------+
| Markdown Formatter |
+---------------------+
|
v
Generated Documentation
9. Storage Architecture
.docxa/
analysis/
interviews/
evidence/
documents/
metadata/
10. Deployment Model
Local Execution
Developers run Docxa locally in project repositories.
Team Collaboration
Generated documentation is stored in version control.
Enterprise Deployment
Organizations may deploy Docxa within private infrastructure.
11. Security Considerations
- Code confidentiality must be maintained.
- Access control should restrict documentation generation.
- Sensitive information must not be exposed during AI processing.
12. Scalability Considerations
Docxa must support:
- large repositories
- monorepos
- multi-team collaboration
Future optimizations may include incremental repository analysis and evidence caching.