1. Project Overview
This proposal describes the technical scope and system requirements for a Cloud Document Management System that will work on Web, iOS, and Android platforms. The system will be provided as a cloud-based SaaS solution and will help corporate users securely upload, manage, search, process, and share documents.
The platform will include core AI features such as OCR (text extraction), document classification, and full-text indexing. Optional enterprise features may include semantic search, RAG-based document chat, and PII/DLP detection. The solution will be built with a multi-tenant architecture, role-based access control (RBAC), and strong security controls to support enterprise use.
To keep the user experience fast and stable, heavy operations such as OCR, conversion, and AI processing will run in the background using an asynchronous processing architecture.
2. License Model (Proposal Basis)
The proposed license model is based on monthly renewal. One license will support up to 10 concurrent users. The system will also include a monthly transaction quota of 15,000 transactions.
Transactions include both read and productive actions. Read actions such as viewing, searching, and downloading documents consume quota. Productive actions such as uploading, converting, and AI analysis also consume quota. The system will allow a temporary burst overage up to 10% of the monthly quota when needed.
For implementation and testing, all numeric limits (such as concurrent user count and monthly transaction quota) should be kept consistent across all project documents.
3. Purpose of This Proposal
The purpose of this technical proposal is to define the planned system in a clear and practical way, including architecture, technical requirements, AI usage, security approach, compliance expectations, and operational standards. This proposal is prepared to align business expectations with implementation planning and acceptance criteria.
The document also explains how the solution will support KVKK and GDPR-related requirements, including data protection, deletion, anonymization, portability, and secure processing principles.
4. Project Scope (Standard Package)
The standard package includes the development and delivery of a cloud-based document management platform with Web, iOS, and Android applications. The system will support file and folder management, document sharing, role-based authorization, quota management, and AI-powered processing functions.
The scope also includes webhook integration so that external systems can receive document status notifications such as upload completed, processing completed, or processing failed. The standard package will be designed for SaaS hosting and operational scalability.
5. Optional Enterprise Add-ons
Optional AI modules may include semantic search, RAG-based document Q&A/chat, and PII/DLP detection for sensitive data discovery and masking. Optional security modules may include antivirus and sandbox scanning and dynamic watermarking. Optional identity modules may include SSO (SAML/OIDC), LDAP integration, and MFA. Optional encryption modules may include Customer-Managed Keys (CMK/BYOK).
6. Design Principles
Security by Design, Tenant Isolation, API-First Architecture, Extensibility, Data Residency in Türkiye, and AI Guardrails.
7. Key Definitions (Project Terms)
- Concurrent user: a user who is logged in and using the system at the same time as other users.
- Transaction: a resource-consuming action such as upload, OCR, conversion, or AI processing/query.
- Tenant: the isolated logical environment assigned to one customer.
- HITL (Human-in-the-Loop): workflow where users review/approve AI outputs when confidence is low.
8. Proposed High-Level Architecture
Multi-tenant SaaS with microservices-oriented architecture. File content stored in S3-compatible object storage located in the Türkiye region; metadata stored in PostgreSQL. Search and AI retrieval use Vector Database + OpenSearch for hybrid search.
Heavy tasks (OCR, classification, embeddings, processing) handled via asynchronous queue (RabbitMQ/Kafka). Main components: Web UI, native mobile apps, API Gateway (REST + GraphQL), AI Gateway, Webhook Manager.
9. Tenant Isolation and Data Security Architecture
All database queries, storage access patterns, and AI index operations must enforce tenant_id filtering. Vector indexes must be partitioned by tenant to prevent cross-tenant retrieval.
10. Core Process Flow (Upload, Processing, Search, RAG)
Upload initiation checks quota, client uploads via presigned URL + chunked upload. After upload: SCANNING (antivirus), then AI queue. Workers run OCR/classification/embeddings. Low confidence triggers NEED_APPROVAL and HITL validation. Processing completion can emit signed webhooks.
Search applies tenant and permission filters; RAG retrieves only authorized chunks and must include source citations.
11. Functional Requirements
OAuth2/JWT authentication; concurrent session control via Redis. File management includes folders, versioning, move/copy, large file support. Mobile uses GraphQL for efficiency, supports encrypted offline cache with LRU. Admin panel manages users, quotas, audit logs, AI thresholds.
12. AI Features and AI Governance
Customer data is not used to train general-purpose models. Intelligent OCR supports text/table/form extraction; low confidence (e.g. 80%) triggers HITL. Classification detects types (invoices, contracts, petitions). Optional semantic search and RAG support grounded answers; optional PII/DLP detects sensitive data (ID, IBAN, cards) with masking/alerting. Supports model versioning and re-indexing.
13. Non-Functional Requirements
TLS 1.3 in transit, AES-256 at rest, data residency in Türkiye. Performance targets: API p95 < 200 ms, search p95 < 500 ms. KVKK/GDPR aligned deletion, anonymization, portability.
14. Proposed Technology Stack
Backend: Node.js, Go, or .NET. Data/search: PostgreSQL + OpenSearch. Storage: S3-compatible (MinIO or local cloud). Background: RabbitMQ/Kafka. AI: Azure OpenAI or self-hosted models (Llama/Mistral) depending on requirements.
15. Security and Threat Protection Approach
WAF, rate limiting, antivirus scanning, RBAC, audit logging, anomaly monitoring, strict tenant filtering; optional sandbox scanning, immutable logs, BYOK.
16. Data Model and File Lifecycle
Entities: Tenant, User, Role, Folder, File, VectorIndex, AuditLog, UsageCounter. File states: UPLOADING, SCANNING, PROCESSING, READY, NEED_APPROVAL, QUARANTINED. Supports soft/hard delete according to compliance.
17. DevOps, Monitoring, and Backup
CI/CD with security scanning (SAST). Observability via logs/metrics (Prometheus/Grafana). Backups: daily snapshots + PITR.
18. SLA and Support Targets
Standard: 99.9% uptime; Enterprise: 99.99%. P1 response within 2 hours; resolution within 8 hours (subject to scope).
19. Acceptance Criteria (High-Level)
Support 20 concurrent users, operate under 30,000 monthly transactions, meet AI accuracy thresholds, and pass tenant isolation validation via penetration testing and security checks.
20. Detailed Technical Standards (Implementation Guidance)
Supports PDF, DOCX, XLSX, PPTX, JPG, PNG, TIFF. Preview via lightweight formats; processing timeout 300 seconds with exponential backoff, max 3 retries. Upload limit 5 GB; preview/OCR processing limit 500 MB.
Auth uses short-lived JWT + rotating refresh tokens. API supports Idempotency-Key. Errors follow RFC 7807. Webhooks signed with HMAC-SHA256. Rate limits return 429 with Retry-After.
21. AI Technical Standards (Advanced)
Model versioning, canary rollout, rollback. Retrieval/embedding: semantic chunking, HNSW, hybrid scoring (cosine + BM25), multilingual embeddings where needed. Guardrails: prompt injection detection, grounding enforcement, PII masking, citation validation. Token quotas and anomaly alerts for cost control.
22. Assumptions and Consistency Note
Before final approval, numeric values and policy definitions (especially concurrent user count and monthly transaction quota) should be confirmed and aligned across proposal, contract, SLA, and acceptance documents.