Here is the full System Requirements Document (SRD) for the UD-AI Platform for Form Updates:

System Requirements Document (SRD)

UD-AI Platform for Form Updates – Phase 1 Implementation

Document Version: 1.0 Date: 2026-02-27 Status: Draft Classification: Confidential

Table of Contents

1. Introduction
2. System Overview
3. Functional Requirements
4. User Personas
5. Visuals, Colors, and Theme
6. Signature Design Concept
7. Non-Functional Requirements
8. Tech Stack
9. Assumptions and Constraints
10. Glossary

1. Introduction

1.1 Purpose

This System Requirements Document (SRD) defines the complete functional and non-functional requirements for the UD-AI Platform for Form Updates, a private, secure, AI-powered document processing platform. It serves as the authoritative reference for all engineering, design, QA, and project management activities throughout the project lifecycle.

1.2 Scope

The UD-AI Platform automates document ingestion, OCR-based text extraction, structured data normalization, intelligent form prefilling, e-signature orchestration via DocuSign, and comprehensive audit and compliance logging. The platform integrates with two external form systems — HOA Invest and FormUpdates — and is designed to serve multiple tenants in an isolated, secure cloud environment hosted on AWS.

This document covers all nine (9) implementation phases:

• Phase 1: Architecture and Infrastructure Setup
• Phase 2: Document Ingestion Pipeline
• Phase 3: OCR Engine Implementation
• Phase 4: Structured Data Extraction and Normalization
• Phase 5: LLM Gateway and AI Intelligence Layer
• Phase 6: Form Prefill and Application Integration
• Phase 7: DocuSign Integration
• Phase 8: Security, Compliance, and Audit Layer
• Phase 9: Monitoring, Metrics, and Observability

1.3 Intended Audience

1.4 Document Conventions

• SHALL — Mandatory requirement
• SHOULD — Recommended but not strictly mandatory
• MAY — Optional capability
• FR-XXX — Functional Requirement identifier
• NFR-XXX — Non-Functional Requirement identifier
• Story points follow the format: "As [persona], I should be able to [action] so that [outcome]."

1.5 References

• AWS Well-Architected Framework
• AWS Textract API Documentation
• DocuSign eSignature REST API v2.1
• OWASP Top 10 Security Guidelines
• SOC 2 Type II Compliance Framework
• PaddleOCR Documentation
• LiteLLM Gateway Documentation
• libpostal Address Normalization Library

2. System Overview

2.1 Platform Description

The UD-AI Platform is a cloud-native, multi-tenant, AI-powered document processing system. It accepts raw documents (PDFs, images, scanned files) from web and mobile clients, processes them through a multi-stage pipeline, and outputs structured data that is used to automatically prefill onboarding forms in integrated partner systems.

The platform's intelligence layer is powered by a dual-model LLM strategy — GPT-5.2 for user-facing interactions and Claude 4.5 Opus for document analysis and coding-level intelligence — routed through a unified LiteLLM gateway.

2.2 High-Level Architecture

┌────────────────────────────────────────────────────────────────────────────┐
│                          CLIENT LAYER                                      │
│              React Web App          React Native Mobile App                │
└──────────────────────────┬─────────────────────────────────────────────────┘
                           │ HTTPS / REST
┌──────────────────────────▼─────────────────────────────────────────────────┐
│                        API GATEWAY / LOAD BALANCER                         │
│                    (AWS ALB + Kong / NGINX Ingress)                        │
└──────┬───────────┬───────────┬──────────┬──────────┬─────────────┬─────────┘
       │           │           │          │          │             │
┌──────▼──┐ ┌──────▼──┐ ┌──────▼──┐ ┌────▼────┐ ┌───▼─────┐ ┌───▼──────┐
│Ingestion│ │  OCR    │ │Extract  │ │  LLM   │ │ Prefill │ │DocuSign │
│Service  │ │Service  │ │Service  │ │Gateway  │ │Service  │ │Service  │
└──────┬──┘ └──────┬──┘ └──────┬──┘ └────┬────┘ └───┬─────┘ └───┬──────┘
       │           │           │          │          │             │
┌──────▼───────────▼───────────▼──────────▼──────────▼─────────────▼────────┐
│                         MESSAGE BUS (AWS SQS / SNS)                        │
└────────────────────────────────────────────────────────────────────────────┘
       │           │           │          │          │
┌──────▼──┐ ┌──────▼──┐ ┌──────▼──┐ ┌────▼────┐ ┌───▼─────┐
│  AWS S3 │ │ Aurora  │ │ MongoDB │ │Weaviate │ │  AWS    │
│(Storage)│ │(RDBMS)  │ │(Metadata│ │(Vectors)│ │  KMS    │
└─────────┘ └─────────┘ └─────────┘ └─────────┘ └─────────┘

2.3 Deployment Architecture

The platform is deployed on AWS within a dedicated VPC. All services run as Docker containers orchestrated by Kubernetes (EKS). Environments include:

• Development — local Docker Compose with stubbed AWS services
• Staging — EKS cluster with isolated namespace, mirroring production
• Production — Multi-AZ EKS deployment with autoscaling, RDS Aurora, and full KMS encryption

2.4 Tenant Isolation Model

Each tenant operates within logically isolated boundaries:

• Separate S3 bucket prefixes per tenant
• Row-level security in Aurora using tenant ID scoping
• Separate MongoDB collections namespaced by tenant
• Tenant-scoped IAM roles and KMS key policies
• All API requests validated against JWT claims containing tenant context

2.5 Data Flow Summary

Document Upload
     │
     ▼
Pre-signed S3 Upload ──► S3 Secure Storage
     │
     ▼
SQS Event Trigger
     │
     ▼
OCR Service (Textract ──► PaddleOCR/Donut fallback)
     │
     ▼
Extraction Engine (Regex + NLP + Confidence Scoring)
     │
     ▼
LLM Gateway (Validation + Summarization + Recommendations)
     │
     ▼
Prefill API ──► HOA Invest / FormUpdates
     │
     ▼
Human Review Layer
     │
     ▼
DocuSign Envelope Creation ──► Signed PDF Storage
     │
     ▼
Audit Log + Compliance Archive

3. Functional Requirements

Requirements are organized by phase and expressed as user stories mapped to the personas defined in Section 4.

Phase 1 — Architecture and Infrastructure Setup

FR-101 As a Solution Architect, I should be able to review and approve a finalized microservices architecture diagram so that all engineering teams have a shared, authoritative blueprint before implementation begins.

FR-102 As a Solution Architect, I should be able to define service boundaries, API contracts, and inter-service communication protocols so that teams can develop independently without integration conflicts.

FR-103 As a DevOps Engineer, I should be able to provision a dedicated AWS VPC with public and private subnets, NAT gateways, and security groups so that all platform services operate in a network-isolated environment.

FR-104 As a DevOps Engineer, I should be able to configure AWS IAM roles, policies, and instance profiles with least-privilege access for each microservice so that no service has broader permissions than required.

FR-105 As a DevOps Engineer, I should be able to provision Aurora PostgreSQL-compatible clusters for transactional data storage with Multi-AZ failover enabled so that the platform achieves high availability for relational data.

FR-106 As a DevOps Engineer, I should be able to configure encrypted S3 buckets with versioning, lifecycle policies, and access logging so that document storage is secure, auditable, and cost-managed.

FR-107 As a Backend Lead, I should be able to define the standardized microservice scaffolding template (FastAPI, health endpoints, logging, auth middleware) so that all services follow a consistent implementation pattern.

FR-108 As a LLM Engineer, I should be able to design the LLM Gateway architecture including provider abstraction, token budgeting, and failover routing so that AI capabilities are resilient and cost-controlled from the start.

FR-109 As a DevOps Engineer, I should be able to deploy separate Development and Staging environments in EKS with namespace isolation so that code can be tested in production-like conditions without affecting live data.

FR-110 As a DevOps Engineer, I should be able to configure Docker Compose for local development so that engineers can run the full service mesh on their workstations without cloud dependencies.

Phase 2 — Document Ingestion Pipeline

FR-201 As a Document Processor, I should be able to upload documents (PDF, JPEG, PNG, TIFF) through the web application so that I can initiate the automated document processing workflow.

FR-202 As a Document Processor, I should be able to upload documents from the mobile application so that I can initiate processing from any device without requiring a desktop.

FR-203 As a System/API User, I should be able to request a pre-signed S3 URL from the ingestion API so that document uploads are performed directly to S3 without routing file bytes through the application server.

FR-204 As a System/API User, I should be able to submit document metadata (document type, uploader ID, tenant ID, source system) alongside the upload so that the document is correctly classified and associated at ingestion time.

FR-205 As a System/API User, I should be able to receive a unique Document ID upon successful upload so that the document can be referenced throughout the processing pipeline.

FR-206 As a System/API User, I should be able to trigger an SQS event automatically upon S3 object creation so that downstream pipeline services are notified without polling.

FR-207 As a Document Processor, I should be able to view the upload and processing status of each submitted document so that I can track pipeline progress in real time.

FR-208 As an Admin, I should be able to configure document type allowlists and file size limits per tenant so that ingestion policies are enforced at the API boundary.

FR-209 As an Auditor, I should be able to view a complete ingestion audit log including uploader identity, timestamp, document hash, tenant, and processing status so that every document entry is fully traceable.

FR-210 As a System/API User, I should be able to retrieve all previous versions of a document by Document ID so that document history is preserved and accessible.

FR-211 As a DevOps Engineer, I should be able to configure S3 server-side encryption using AWS KMS for all ingested documents so that data is encrypted at rest from the moment of storage.

Phase 3 — OCR Engine Implementation

FR-301 As a System/API User, I should be able to submit a document to the OCR service by Document ID so that text extraction is initiated asynchronously from the stored S3 object.

FR-302 As a System/API User, I should be able to receive structured OCR output including extracted blocks, lines, tables, bounding boxes, and page numbers from AWS Textract so that downstream extraction has full positional context.

FR-303 As a System/API User, I should be able to have the OCR service automatically fall back to PaddleOCR or Donut when Textract returns errors, timeouts, or low confidence results so that processing continuity is maintained without manual intervention.

FR-304 As a LLM Engineer, I should be able to configure health-based provider selection logic so that the OCR service routes requests to the healthiest available engine based on real-time latency and error rate metrics.

FR-305 As a System/API User, I should be able to submit multi-page PDF documents and receive per-page OCR results stitched into a unified document-level output so that large documents are handled without truncation.

FR-306 As a System/API User, I should be able to enable image preprocessing (deskewing, rotation correction, contrast enhancement) before OCR execution so that scan quality issues do not degrade extraction accuracy.

FR-307 As a System/API User, I should be able to receive a per-page and overall OCR confidence score alongside extracted text so that downstream services can make quality-gated decisions.

FR-308 As an Admin, I should be able to view an OCR metrics dashboard showing provider usage, latency distribution, confidence score trends, and failover event counts so that I can monitor OCR health and costs.

FR-309 As a DevOps Engineer, I should be able to configure Textract API error handling with retry logic and exponential backoff so that transient AWS service failures do not propagate as pipeline failures.

FR-310 As an Auditor, I should be able to retrieve a log of every OCR execution including the engine used, confidence score, page count, and processing duration so that OCR decisions are auditable.

Phase 4 — Structured Data Extraction and Normalization

FR-401 As a System/API User, I should be able to submit OCR output to the extraction engine and receive a normalized structured JSON schema containing all identified fields so that downstream services consume clean, typed data.

FR-402 As a Document Processor, I should be able to process a Driver License document and receive extracted fields (name, DOB, address, DL number, expiry, state) with state-specific format detection applied so that DL data is correctly parsed regardless of issuing state.

FR-403 As a Document Processor, I should be able to process a bank statement and receive classification of account type (Individual vs Corporate) and extracted fields (account holder name, account number, balance, statement period) so that financial documents are accurately parsed.

FR-404 As a System/API User, I should be able to receive extracted name, address, and date of birth fields normalized to canonical formats so that downstream form prefill receives consistent, validated data.

FR-405 As a System/API User, I should be able to receive balance and maturity date values extracted and normalized to typed numeric and ISO date formats respectively so that financial figures are machine-readable.

FR-406 As a System/API User, I should be able to have addresses normalized using libpostal so that address components (street, city, state, zip, country) are decomposed into a standard schema regardless of input formatting.

FR-407 As a System/API User, I should be able to receive a confidence score for each extracted field so that consumers can apply threshold-based acceptance rules independently per field.

FR-408 As a System/API User, I should be able to retrieve field-level source tracking metadata (OCR engine used, page number, bounding box coordinates, raw text source) so that every extracted value is traceable to its origin in the document.

FR-409 As a Reviewer, I should be able to view fields flagged as low-confidence so that I can manually review and correct specific values before they are submitted to downstream forms.

FR-410 As a System/API User, I should be able to have all extracted data validated against a defined JSON schema with type enforcement so that malformed extractions are rejected before entering the prefill pipeline.

FR-411 As a Backend Lead, I should be able to configure extraction rules using a combination of Regex patterns and NLP models so that the extraction engine is extensible to new document types without full rewrites.

Phase 5 — LLM Gateway and AI Intelligence Layer

FR-501 As a LLM Engineer, I should be able to route LLM requests through a unified gateway that abstracts OpenAI (GPT-5.2) and AWS Bedrock (Claude 4.5 Opus) behind a single API contract so that consumers are decoupled from provider-specific implementations.

FR-502 As a LLM Engineer, I should be able to configure model selection rules in LiteLLM so that GPT-5.2 is used for user-facing summary responses and Claude 4.5 Opus is used for document intelligence, field validation, and coding-level tasks.

FR-503 As a System/API User, I should be able to submit extracted field data to the AI validation layer and receive a validation report identifying missing fields, inconsistent values, and recommended corrections so that data quality is enforced before prefill.

FR-504 As a Document Processor, I should be able to request an AI-generated summary of a processed document so that I can quickly understand document content without reading the full extraction output.

FR-505 As a Reviewer, I should be able to request AI-generated recommendations for missing or ambiguous fields so that I can make informed decisions during the human review step.

FR-506 As a Reviewer, I should be able to view an explainability report for each AI decision showing which document segments influenced the output so that I can trust and verify AI recommendations.

FR-507 As a LLM Engineer, I should be able to configure retry and failover logic in the gateway so that if the primary LLM provider returns an error or exceeds latency thresholds, the request is automatically rerouted to the secondary provider.

FR-508 As an Admin, I should be able to view real-time LLM cost tracking broken down by provider, model, tenant, and request type so that AI spend is visible and controllable.

FR-509 As a LLM Engineer, I should be able to version and manage prompt templates so that changes to prompts are tracked, rollback is possible, and A/B testing of prompts can be conducted.

FR-510 As an Admin, I should be able to view an AI performance monitoring dashboard showing latency percentiles, token usage, cost trends, and provider health so that LLM operations are observable.

FR-511 As a LLM Engineer, I should be able to configure token counting and budget caps per tenant and per request type so that runaway LLM costs are prevented.

FR-512 As a System/API User, I should be able to receive normalized response schemas from the LLM gateway regardless of the underlying provider so that response parsing logic is not duplicated per consumer.

Phase 6 — Form Prefill and Application Integration

FR-601 As a Document Processor, I should be able to trigger form prefill to HOA Invest by Document ID so that extracted and validated data is automatically submitted to the HOA Invest onboarding form without manual data entry.

FR-602 As a Document Processor, I should be able to trigger form prefill to FormUpdates by Document ID so that extracted data is automatically submitted to the FormUpdates platform.

FR-603 As a Reviewer, I should be able to view a pre-submission review screen showing all prefilled fields with their confidence scores and source traces before the form is submitted so that I can approve, modify, or reject specific fields.

FR-604 As a System/API User, I should be able to configure confidence thresholds per field type so that fields above the threshold are auto-applied and fields below the threshold are flagged for human review.

FR-605 As a Reviewer, I should be able to see visual field highlighting in the review UI that color-codes fields by confidence level (high, medium, low) so that attention is directed to uncertain fields efficiently.

FR-606 As a Reviewer, I should be able to click on any prefilled field and view the source document segment that produced the value so that I can verify data provenance without switching applications.

FR-607 As a Document Processor, I should be able to manually override any auto-filled field during the review step so that incorrect extractions are corrected before form submission.

FR-608 As a System/API User, I should be able to call the prefill API and receive a structured response including field values, confidence scores, source metadata, and required human review flags so that integration consumers have full context.

FR-609 As an Admin, I should be able to configure which fields are mandatory for submission per form type so that incomplete prefills are blocked from advancing to signing.

FR-610 As an Auditor, I should be able to view a log of all prefill events including the fields auto-applied, fields overridden, reviewer identity, and submission timestamp so that the human-in-the-loop process is fully auditable.

Phase 7 — DocuSign Integration

FR-701 As a System/API User, I should be able to authenticate the platform to DocuSign via OAuth 2.0 so that envelope creation and management operations are securely authorized.

FR-702 As a Document Processor, I should be able to trigger the creation of a DocuSign envelope from a completed prefill so that the signing workflow is initiated automatically without manual DocuSign configuration.

FR-703 As a System/API User, I should be able to configure signer roles (primary signer, witness, co-signer) and routing order per document type so that multi-party signing workflows are enforced correctly.

FR-704 As a Document Processor, I should be able to send a DocuSign signing request to the configured recipient(s) from within the platform so that signers receive signing invitations without manual email steps.

FR-705 As a System/API User, I should be able to receive webhook callbacks from DocuSign on envelope status changes (sent, delivered, completed, declined) so that the platform reflects signing status in real time.

FR-706 As a System/API User, I should be able to automatically retrieve and store the executed signed PDF in S3 upon DocuSign envelope completion so that signed documents are archived in the platform's secure storage.

FR-707 As a Document Processor, I should be able to view the signing status of each document within the platform so that I can track pending signatures without accessing the DocuSign portal directly.

FR-708 As an Auditor, I should be able to access an audit trail for every DocuSign signing event including envelope ID, signer identities, timestamps, IP addresses, and completion status so that e-signature compliance is demonstrable.

FR-709 As a System/API User, I should be able to associate a signed DocuSign envelope with the originating Document ID and tenant record so that the full document lifecycle is linked in the platform's data model.

Phase 8 — Security, Compliance, and Audit Layer

FR-801 As an Admin, I should be able to define and enforce Role-Based Access Control (RBAC) policies for all platform personas so that users can only access resources and perform actions authorized by their role.

FR-802 As a Tenant Admin, I should be able to manage users within my tenant including role assignments, access revocation, and invitation management so that tenant-level user administration is self-service.

FR-803 As a System/API User, I should be able to have all cross-tenant data access blocked at the API and database layers through tenant ID scoping so that tenant data is logically isolated at all times.

FR-804 As a DevOps Engineer, I should be able to configure AWS KMS customer-managed keys per tenant so that tenants can control and audit the encryption keys protecting their data.

FR-805 As an Auditor, I should be able to query the audit log by tenant, user, action type, document ID, date range, and outcome so that compliance investigations can be conducted efficiently.

FR-806 As an Auditor, I should be able to verify document integrity using stored cryptographic hashes so that I can confirm documents have not been tampered with after ingestion.

FR-807 As a LLM Engineer, I should be able to configure prompt and output retention policies per tenant so that AI interaction data is retained for the required compliance period and purged afterward.

FR-808 As an Admin, I should be able to access a security and compliance audit dashboard showing active RBAC assignments, recent privilege changes, failed authentication attempts, and key rotation status so that the security posture is continuously visible.

FR-809 As a System/API User, I should be able to have all API communications enforced over TLS 1.3 so that data in transit is encrypted.

FR-810 As a DevOps Engineer, I should be able to run automated security validation reports against the deployed infrastructure so that compliance posture is regularly verified without manual audits.

Phase 9 — Monitoring, Metrics, and Observability

FR-901 As a DevOps Engineer, I should be able to view a centralized monitoring dashboard aggregating service health, error rates, and resource utilization across all microservices so that operational issues are detected and triaged from a single pane of glass.

FR-902 As an Admin, I should be able to view OCR latency metrics broken down by provider, document type, and page count so that OCR performance is continuously monitored.

FR-903 As an Admin, I should be able to view LLM latency and cost metrics broken down by provider, model, tenant, and request type so that AI spending is optimized.

FR-904 As an Admin, I should be able to view confidence score distribution charts for both OCR and extraction outputs so that data quality trends are visible over time.

FR-905 As a DevOps Engineer, I should be able to configure automated alerts for failover events (OCR provider switch, LLM provider switch) so that engineering is notified immediately when primary services degrade.

FR-906 As an Admin, I should be able to view an AI performance dashboard tracking model accuracy trends, validation acceptance rates, and human override rates so that LLM quality is measurable.

FR-907 As an Admin, I should be able to access cost visibility reports showing compute, storage, OCR API, and LLM token costs per tenant per billing period so that cost allocation and chargebacks are supported.

FR-908 As a DevOps Engineer, I should be able to configure distributed tracing across all microservices so that request journeys can be traced end-to-end for debugging pipeline failures.

FR-909 As a DevOps Engineer, I should be able to configure log aggregation for all microservices with structured JSON logging so that logs are queryable, filterable, and retainable for the defined compliance period.

4. User Personas

4.1 Admin

Description: Platform-level administrator with global visibility and configuration authority. Manages platform settings, monitors health, configures tenant onboarding, and oversees compliance and security dashboards.

Capabilities:

• Configure global settings (file type allowlists, confidence thresholds, retention policies)
• View all tenant activity across the platform
• Manage RBAC role definitions
• Access all monitoring and cost dashboards
• Trigger security validation reports
• Configure LLM routing and model budgets

Access Level: Full platform access, cross-tenant read access, no tenant data modification

4.2 Tenant Admin

Description: Tenant-scoped administrator responsible for managing users, roles, and settings within their organization's isolated environment.

Capabilities:

• Invite and manage users within their tenant
• Assign and revoke roles within tenant scope
• Configure tenant-level settings (document type rules, confidence thresholds)
• View tenant-scoped audit logs
• Request KMS key rotation for their tenant

Access Level: Full access within tenant boundary, no cross-tenant visibility

4.3 Document Processor

Description: Operational user responsible for uploading documents and initiating the automated processing pipeline. Typically a data entry specialist, operations associate, or onboarding team member.

Capabilities:

• Upload documents via web and mobile
• Monitor document processing status
• Trigger form prefill workflows
• Initiate DocuSign signing requests
• View processing results and confidence scores

Access Level: Upload and initiate actions within tenant; read-only on processed outputs

4.4 Reviewer

Description: Subject matter expert who reviews AI-generated prefill outputs before form submission. Acts as the human-in-the-loop quality gate for low-confidence or flagged fields.

Capabilities:

• View AI prefill results with confidence scores and source traces
• Approve, modify, or reject individual prefilled fields
• Request AI recommendations for ambiguous fields
• View explainability reports for AI decisions
• Approve documents for DocuSign submission

Access Level: Read and modify prefill outputs within tenant; cannot upload documents or configure system

4.5 Auditor

Description: Compliance or legal professional responsible for reviewing platform activity for regulatory, contractual, or investigative purposes. Read-only access to all audit trails.

Capabilities:

• Query audit logs by any indexed dimension (tenant, user, document, date, action)
• Verify document integrity via hash validation
• Review DocuSign signing audit trails
• Access security and compliance dashboards
• Export audit reports

Access Level: Read-only across all audit and compliance data within authorized scope; no operational capabilities

4.6 System/API User

Description: Machine identity representing an integrated external system, internal microservice, or automation agent that interacts with the platform programmatically via REST APIs.

Capabilities:

• Submit documents via API
• Trigger OCR, extraction, prefill, and signing workflows
• Consume webhook events
• Query processing status and results
• Execute platform functions within scoped API key permissions

Access Level: Scoped to API key permissions; tenant-isolated; no UI access

5. Visuals, Colors, and Theme

5.1 Design Philosophy

The UD-AI Platform visual identity communicates precision, intelligence, and trust. The design language borrows from the aesthetic vocabulary of secure government systems and advanced AI laboratories — not consumer software. Every visual decision reinforces that the user is interacting with a system that handles sensitive documents with care, rigor, and intelligence.

5.2 Core Color Palette

5.3 Typography

All numeric metrics, document IDs, confidence scores, and extracted field values render in JetBrains Mono to reinforce data precision.

5.4 Component Design Principles

• Glassmorphism cards with backdrop-filter: blur(12px) and 1px borders using --color-border-default at 40% opacity
• Confidence indicators displayed as animated arc gauges — color transitioning from --color-status-critical through --color-status-warning to --color-status-success
• AI activity states indicated by a pulsing --color-ai-glow border animation (2s ease-in-out infinite) on components currently processing an LLM request
• Document viewer uses a dark-mode canvas with #1A2235 background and extracted field bounding boxes rendered as semi-transparent --color-accent-primary overlays with confidence-coded opacity
• All interactive elements use 4px border-radius for a precise, technical aesthetic; panels and modals use 8–12px
• Micro-animations on all state transitions: 200ms ease-out for component reveals, 150ms for hover states, 400ms for confidence score counters

5.5 Iconography and Data Visualization

• Icons: Lucide Icons or Phosphor Icons — stroke style, 1.5px weight, never filled
• Charts: Recharts or Victory — dark-mode configured, no chart backgrounds, gridlines at 10% opacity
• Confidence distributions rendered as ridge-line density plots, not bar charts
• Pipeline status rendered as a vertical step timeline with animated connector lines that fill with --color-accent-primary as each stage completes

6. Signature Design Concept

Title: "The Neural Vault"

Concept Statement: The landing page IS the document. The moment a visitor arrives, they are immersed inside a living, breathing representation of what the platform does — a dark, cinematic document processing sequence that plays out in real time as the hero experience, not as a demo or screenshot, but as the UI itself.

The Full Experience:

Stage 1 — The Arrival (0s–2s) The page loads to pure black. Then, from the center of the screen, a single document materializes — not as a static image, but as a particle reconstruction. Thousands of tiny cyan and indigo particles (--color-accent-primary and --color-neural-purple) converge from the edges of the screen, swirling inward in a vortex pattern, gradually resolving into the outline of a crisp white document. A subtle, low-frequency neural pulse plays (optional, user-toggled). The document settles with a satisfying weight, casting a soft ambient glow on the dark background.

Stage 2 — The Scan (2s–5s) A horizontal scan line — razor-thin, glowing electric blue — sweeps top to bottom across the document with a 3-second duration and a slight motion-blur trail. As it passes over text regions, the text lights up momentarily with --color-ai-glow and appears to be "read." A side panel slides in from the right with a frosted glass effect, and extracted fields begin populating one by one — name, address, date of birth — each value typing itself character by character in JetBrains Mono. Each field has a small animated arc gauge beside it that fills in real time, landing on a confidence percentage (97%, 94%, 88%).

Stage 3 — The Intelligence Layer (5s–8s) A second layer activates. The extracted fields begin connecting to a floating, abstract neural graph that blooms above and behind the document — nodes and edges materializing from nothing, pulsing softly with --color-neural-purple. This graph represents the AI validation layer. The main headline fades in with a word-by-word drop reveal: "Documents. Understood." Sub-headline follows: "Private. Intelligent. Auditable." Text uses a subtle chromatic aberration effect — a 1–2px RGB split that resolves to crisp white over 300ms, evoking precision calibrating into focus.

Stage 4 — The Vault Lock (8s–10s) A shield icon assembles itself from fragments at the bottom of the document — pieces rotating into place like a combination lock engaging. When the final fragment clicks into position (with a mechanical micro-animation), the document emits a single pulse of green light (--color-status-success) that ripples outward across the background, then fades. This represents security confirmed. The CTA buttons — "Request a Demo" and "Get Started" — materialize with a fill-transition: first the border traces appear (SVG stroke-dashoffset animation), then the background floods in. Hovering a button causes the background to shimmer with a slow diagonal gradient sweep.

Stage 5 — The Living State (10s → ongoing) The page enters its ambient state. The neural graph continues to pulse slowly — nodes brightening and dimming, edges flickering with data flow. Every 8–12 seconds, a new document fragment appears at a random location on the dark background, gets scanned, and disappears — a subtle reminder the platform is always processing. The scroll indicator is not an arrow but a glowing document page corner folding downward, inviting the user to explore deeper. On scroll, the hero dissolves in layers — the neural graph fragments back into particles, the document fades, and the next section rises through the dissolve.

Interaction Details:

Technical Implementation Notes:

• Hero canvas: React Three Fiber + Three.js for particle system and neural graph rendering
• Scan line and document animation: Framer Motion with useInView triggers
• OCR field population: custom React hook simulating streaming character reveal with 30–80ms character delays and slight jitter
• Confidence arc gauges: SVG-based with CSS stroke-dasharray and stroke-dashoffset animation
• Neural graph: D3.js force-directed layout rendered into Three.js scene for 3D depth
• All animations respect prefers-reduced-motion — a static fully-rendered version of Stage 5 loads instead
• Page load performance: particle reconstruction defers until LCP is complete; critical text and CTAs are rendered statically above the fold

Design Mood Board Tags: cyberpunk precision · intelligence made visible · vault aesthetic · alive data · trust through transparency

7. Non-Functional Requirements

7.1 Performance

7.2 Scalability

7.3 Availability and Reliability

7.4 Security

7.5 Compliance and Auditability

7.6 Maintainability and Extensibility

7.7 Observability

8. Tech Stack

8.1 Frontend

8.2 Backend

8.3 Databases

8.4 AI and LLM

8.5 Infrastructure and DevOps

8.6 Monitoring and Observability

8.7 Third-Party Integrations

9. Assumptions and Constraints

9.1 Assumptions

Infrastructure and Environment

• AWS is the sole cloud provider for all production infrastructure
• The team has active AWS accounts with IAM permissions to provision VPC, EKS, Aurora, S3, SQS/SNS, KMS, and Textract
• DocuSign developer and production accounts are available or will be provisioned before Phase 7 begins
• HOA Invest and FormUpdates provide documented, versioned REST APIs with sandbox environments for Phase 6 integration development

Data and Documents

• Supported input document formats are: PDF, JPEG, PNG, and TIFF; other formats are out of scope for Phase 1
• Documents are assumed to be in English for the initial implementation; multi-language support is not in scope
• Driver License format support covers all 50 US states; international DL formats are out of scope
• Bank statements are assumed to follow standard US banking formats (individual and corporate account types)
• Maximum supported document size is 50 MB per file and 100 pages per document

AI and LLM

• GPT-5.2 and Claude 4.5 Opus API access is provisioned through OpenAI and AWS Bedrock accounts respectively
• LiteLLM is the sole routing layer; direct provider SDK calls from application services are prohibited
• LLM prompt templates are managed within the platform; external prompt management platforms are not used
• AI model outputs are advisory; all data is subject to human review before being submitted to downstream forms

Security and Compliance

• The platform targets SOC 2 Type II compliance posture; formal certification is a subsequent engagement
• Customer-managed KMS keys are available for tenant-level encryption from initial deployment
• All engineers operating production environments have completed security awareness training

Team and Process

• All four core roles (Solution Architect, DevOps, Backend Lead, LLM Engineer) are staffed before Phase 1 begins
• Phase 1 must be fully completed and signed off before Phase 2 implementation begins
• Phases 2 and 3 may overlap in their later stages; Phase 5 may develop in parallel with Phase 4
• Phase 8 (Security and Audit) is a continuous cross-cutting concern running throughout all phases

9.2 Constraints

Technical Constraints

• All backend services must be implemented in Python using FastAPI; no other backend languages are permitted
• All database schema changes must be executed through Alembic migrations; direct DDL in production is prohibited
• Container images must be built on approved base images; custom base images require security review
• No LLM provider may be called directly from services outside the LLM Gateway; all AI requests must route through LiteLLM
• Pre-signed S3 URLs must expire within 15 minutes of generation
• API keys and credentials must never be embedded in source code, Dockerfiles, Kubernetes manifests, or CI/CD scripts

Regulatory and Compliance Constraints

• Document data processed through the platform must not leave the AWS region designated for the tenant without explicit consent
• Audit logs must be written to append-only storage and must not be modifiable by any application-level user including Admins
• LLM prompts containing document data must not be used for model training by any provider; provider agreements must confirm this before production use

Integration Constraints

• DocuSign integration is limited to the eSignature product; DocuSign CLM and other DocuSign products are out of scope
• HOA Invest and FormUpdates API integrations are read/write for prefill only; the platform does not manage the full lifecycle of those external systems
• PaddleOCR and Donut are deployed as self-hosted services within the platform's Kubernetes cluster; they are not called as external APIs

Budget and Resource Constraints

• AWS Textract costs must be tracked per tenant; cost attribution is a platform requirement
• LLM token budgets are configurable per tenant to enforce spending limits
• Phase 1 infrastructure must be sized for development and staging workloads; production-scale provisioning occurs in a subsequent phase

10. Glossary

End of System Requirements Document

UD-AI Platform for Form Updates — Phase 1 Implementation Document Version 1.0 | 2026-02-27 | Classification: Confidential