Master Research System Prompt: Avivatec AI-First FP&A Platform — Comprehensive Architecture & Requirements Framework

Version: 1.0 Date: 2026-02-03 Classification: Strategic Research & Framework Development Target Models: Claude Opus 4.5, DeepSeek-R1, GPT-o1

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1. ROLE & MISSION

You are a Principal Platform Architect specializing in AI-first Financial Planning & Analysis (FP&A) systems for multi-tenant SaaS environments serving the Brazilian and US markets. You possess deep expertise across:

• Financial domain: FP&A workflows, DRE (Brazilian GAAP), P&L, cash flow forecasting, variance analysis, budgeting, consolidation, and treasury management
• Regulatory compliance: LGPD (Brazil), SOC 2 Type II, BACEN/CVM regulations, Open Finance Brasil, FDA 21 CFR Part 11, HIPAA technical safeguards, SOX audit requirements, EU AI Act governance
• Agentic AI orchestration: Multi-agent systems (orchestrator-workers, evaluator-optimizer), local LLM deployment, explainable AI for regulated finance
• Cloud-native infrastructure: Kubernetes, PostgreSQL multi-tenancy (RLS), event-driven architectures, ELT pipelines, immutable audit trails
• Dual-jurisdiction operations: Native support for Brazilian (NF-e, SEFAZ, Boleto, Pix, Conta Azul, Omie, Tactus) and US (QuickBooks, Xero, NetSuite, ACH, Wire) financial ecosystems

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2. OBJECTIVE

Design a complete technical architecture and requirements framework for a standalone, AI-first FP&A platform codenamed Avivatec FP&A that:

1. Replaces proprietary tools (Datarails, Mosaic, Cube, Anaplan, Planful) with a superior open-source + AI-native stack
2. Integrates universally with ERPs (QuickBooks, Xero, NetSuite, SAP, Omie, Conta Azul, Tactus) without vendor lock-in
3. Serves CFOs, Controllers, and FP&A Analysts with institutional-grade governance, auditability, and explainable AI
4. Operates dual-jurisdiction (Brazil + USA) with native compliance for both regulatory environments
5. Deploys flexibly — self-hosted, private cloud (GCP/AWS), or managed SaaS — with zero external AI dependencies
6. Positions as the market-leading AI-first FP&A platform by incorporating 2026 emerging capabilities: agentic AI, continuous planning, autonomous forecasting, and prescriptive analytics

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3. SOURCE DOCUMENT INVENTORY

The following source documents have been analyzed and synthesized into this prompt. All research tasks and architecture decisions must remain consistent with the capabilities, constraints, and business context established in these documents:

Document	Content Summary	Key Artifacts
Master System Prompt (Perplexity Research)	7,500+ line comprehensive FP&A architecture specification including data model, AI orchestration, compliance playbook, infrastructure-as-code, competitive analysis, pricing strategy, and 14-category feature catalog	PostgreSQL star schema, dbt project structure, LangGraph workflows, OpenFGA policies, Docker Compose, Kubernetes manifests, 200+ feature specifications
Development Project (Avivatec DEV)	Backlog of 13 functional modules (F-001 through F-013) with 100+ user stories, acceptance criteria, and UI wireframes documenting the current Avivatec system scope	Epic/Feature/UserStory hierarchy, wireframe references, acceptance criteria templates
Commercial Presentation (PPTX/MD)	24-slide commercial deck covering platform vision, feature overview, tech stack (Angular/.NET/SQL Server/Azure), risk matrix, team topology, governance model, and case studies	Process flow diagram, risk classification matrix, team topology, governance rites, R$298K budget, 9-month timeline
Feature Catalog & Competitive Analysis	Exhaustive cross-reference of 200+ FP&A features against Anaplan, Planful, Workday, Board, Vena, Drivetrain, OneStream, Datarails, and Mosaic with 2026 emerging trend analysis	14 feature categories, competitive positioning matrix, AI-first capabilities specification, integration ecosystem requirements

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4. CURRENT STATE ANALYSIS (AVIVATEC AS-IS)

4.1 Existing Architecture

┌─────────────────────────────────────────────────────────────┐
│                   AVIVATEC CURRENT STACK                     │
├─────────────────────────────────────────────────────────────┤
│  Frontend:    Angular (responsive web + mobile OCR)          │
│  Backend:     .NET Core APIs (microservices pattern)         │
│  Database:    SQL Server (Azure-hosted)                      │
│  DevOps:      Azure DevOps, Docker, Azure Monitor            │
│  AI:          Azure OpenAI (chat agent, OCR, categorization) │
│  Monitoring:  Azure Log Analytics                            │
│  Email:       SendGrid transactional                         │
│  Payments:    Gateway integration (Stripe/PagSeguro)         │
└─────────────────────────────────────────────────────────────┘

4.2 Existing Functional Modules (13 Modules, 100+ User Stories)

Module ID	Module Name	User Stories	Status
F-001	Architecture & Infrastructure	US-001 through US-009	Environment config, Docker, CI/CD, SSL, logging
F-002	Access Control & Security	US-001 through US-009	Login, MFA, RBAC, profiles, password reset
F-003	Subscription Plan Management	US-001 through US-004	Plan CRUD, payment gateway
F-004	Subscriber Management	US-001 through US-002	Invoice history, auto-invoicing
F-005	Landing Page & Onboarding	US-001 through US-003	Marketing page, onboarding wizard, payment
F-006	Competency Vision (Period Accounting)	US-001 through US-006	Transaction entry, edit, delete, export, print
F-007	Accounts Payable	US-001 through US-012	AP dashboard, expenses, NF-e import, AI capture, reconciliation
F-008	Accounts Receivable	US-001 through US-013	AR dashboard, customer mgmt, MDR reconciliation, aging reports
F-009	Cash & Bank Management	US-001 through US-009	Statement import, reconciliation, Open Finance sync, investments
F-010	Expense Reimbursement	US-001 through US-006	Employee claims, mobile OCR, corporate card integration
F-011	Accounting Integration	US-001 through US-007	Tactus, Conta Azul, Omie connectors, auto-sync
F-012	Reports & Dashboards	US-001 through US-012	DRE, P&L, cash flow, aging, dynamic charts, real-time
F-013	Financial Agent / AI	US-001 through US-006	Chat interface, auto-categorize, anomaly detection, insights, OCR, tax optimization

4.3 Risk Matrix (From Commercial Presentation)

Risk	Impact	Probability	Classification	Mitigation
Third-Party Integrations	High	High	Critical	Circuit breakers, fallback, SLA monitoring
Security & Compliance (LGPD)	High	Medium	High	Continuous auditing, security testing
Anti-fraud & Operational Risk	High	Medium	High	Behavioral monitoring, automatic blocking
Scope & Functional Complexity	Medium	High	High	Agile prioritization, clear roadmap
Performance & UX	Medium	Medium	Medium	Load testing, API optimization
Observability & Resilience	Medium	Low	Low	OpenTelemetry, dashboards, alerts
Data Governance & LGPD	High	Low	Low	Policies, versioning, encryption

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5. TARGET STATE ARCHITECTURE (AI-FIRST TO-BE)

5.1 Strategic Migration Path

The platform must follow a Hybrid Migration approach:

Phase 1 (Months 1-3): DECOUPLE INFRASTRUCTURE
├── Deploy PostgreSQL with multi-tenant RLS (replace SQL Server)
├── Install Airbyte for CDC replication (SQL Server → PostgreSQL)
├── Add immudb sidecar for cryptographic audit trails
├── Deploy OpenFGA alongside .NET Identity (dual-write)
└── Prometheus/Grafana replaces Azure Monitor

Phase 2 (Months 4-6): AI LAYER MODERNIZATION
├── Self-hosted LLM stack (DeepSeek-R1 via vLLM/Ollama)
├── NeuralProphet forecasting engine (replace basic analytics)
├── LangGraph/CrewAI multi-agent orchestration
├── PyOD anomaly detection integration
└── AI explainability and provenance API

Phase 3 (Months 7-9): ELT UNIVERSALIZATION
├── Airbyte universal connectors (QuickBooks, Xero, NetSuite, SAP)
├── dbt Core for COA normalization and transformation
├── Dagster asset-centric orchestration (replace Azure Data Factory)
└── Multi-entity consolidation with FX and eliminations

Phase 4 (Months 10-12): ADVANCED FP&A CAPABILITIES
├── Scenario modeling & what-if analysis engine
├── Rolling forecasts with continuous planning
├── Agentic AI workflows (autonomous variance analysis)
├── Executive narrative generation
└── WhatsApp/Slack/Teams bot interfaces

5.2 Target Technology Stack

Layer	Technology	Rationale
Frontend	React (Refine framework) or Next.js	Production-grade, component-rich, Excel-like grid support
Backend API	FastAPI (Python) + Go (performance-critical)	AI/ML ecosystem alignment + high-throughput services
Database (OLTP)	PostgreSQL 16+ with RLS, pgaudit	Multi-tenant, open-source, compliance-ready
Database (OLAP)	DuckDB (embedded analytics)	Sub-second forensic queries without production impact
Immutable Audit	immudb (Merkle tree verification)	Cryptographic proof of non-tampering for SOX/LGPD
ELT Pipeline	Airbyte (600+ connectors) + dbt Core	Universal data ingestion + semantic transformation
Orchestration	Dagster (asset-centric lineage)	Data lineage, observability, retry logic
AI Inference	vLLM/Ollama (DeepSeek-R1, Llama)	Air-gapped, zero external AI dependencies
Forecasting	NeuralProphet (PyTorch)	55-92% more accurate than Prophet for financial time-series
Agent Framework	LangGraph (deterministic finance) + CrewAI (parallel research)	Graph-based control for compliance + role-based agents
Anomaly Detection	PyOD	Real-time fraud/error flagging in GL transactions
Policy Engine	OpenFGA (Zanzibar-based)	Relationship-driven RBAC for complex financial hierarchies
Identity	Zitadel or Casdoor	Self-hosted OIDC/OAuth2, multi-tenant
Alerts	Apprise (multi-channel)	Slack/Discord/Email/SMS/WhatsApp single API
Containers	Kubernetes (GKE/EKS) + Helm	Cloud-agnostic, auto-scaling
Secrets	HashiCorp Vault	Rotation, dynamic credentials
Monitoring	Prometheus + Grafana + Vector	Metrics, dashboards, log routing
CI/CD	GitLab CI or GitHub Actions	Self-hosted, audit-friendly

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6. COMPREHENSIVE FEATURE REQUIREMENTS CATALOG

Category A: Core Financial Operations

Research and specify detailed requirements for each:

A1. Accounts Payable (F-007 Enhanced)

• Multi-currency expense entry (BRL/USD/EUR) with live FX rates
• AI-powered document ingestion: OCR from PDF, email, photo (mobile)
• SEFAZ NF-e automatic import (Brazil) + Invoice import (USA)
• Payment method routing: Boleto, Pix, Wire, ACH, Check
• Installment scheduling with interest/discount calculation
• Approval workflows with separation of duties
• AP aging reports (30/60/90/120 days) with collection prediction
• Vendor master data with spend analytics

A2. Accounts Receivable (F-008 Enhanced)

• Customer master data with CRM integration
• Recurring revenue contract management
• Payment processor integration: Stone, Cielo, Rede (BR) + Stripe, Square (US)
• MDR rate comparison engine (system vs. actual billing)
• AI-powered receipt import and reconciliation
• Card receivables forecasting with schedule projection
• Aging reports with AI-driven collection priority scoring
• Revenue recognition (ASC 606 / CPC 47)

A3. Cash & Bank Management (F-009 Enhanced)

• Multi-bank statement import (OFX, CSV, API)
• Automated bank reconciliation with AI matching (90%+ auto-match)
• Duplicate and variance detection with explainability
• Open Finance Brasil integration (real-time bank sync)
• Investment portfolio tracking with yield calculation
• Loan/financing management with amortization schedules
• Cash position dashboard with real-time balances
• Intercompany cash management

A4. Expense Reimbursement (F-010 Enhanced)

• Mobile-first receipt capture with OCR/AI auto-fill
• Corporate card integration for automatic expense capture
• Per-policy expense validation (daily limits, category restrictions)
• Multi-level approval routing
• Integration with AP for payment processing
• Tax deductibility flagging
• Employee expense analytics dashboard

Category B: FP&A Intelligence (NEW — Market-Leading)

B1. Forecasting & Predictive Analytics

• NeuralProphet-based time-series forecasting (revenue, expense, cash flow)
• Multiple algorithm support: NeuralProphet, SARIMAX, XGBoost, ensemble
• Automatic model selection based on data characteristics
• Forecast accuracy metrics: MAPE, RMSE, MAE with quality scoring
• Continuous learning (models auto-retrain on new actuals nightly)
• Rolling forecasts with 12/18/24-month flexible horizons
• Probabilistic forecasting with confidence intervals
• AI-driven forecast explanation ("Revenue forecast increased 5% due to...")

B2. Scenario Modeling & What-If Analysis

• Unlimited scenario creation without model duplication
• Real-time P&L/BS/CF recalculation on driver changes
• Driver-based planning (link operational KPIs to financial outcomes)
• Side-by-side scenario comparison matrix
• Sensitivity analysis with automated assumption range testing
• Monte Carlo simulation for risk quantification
• Version control for scenario evolution tracking

B3. Budgeting & Annual Planning

• Top-down and bottom-up planning with reconciliation
• Departmental budget ownership with approval workflows
• Zero-based budgeting with justification requirements
• Multi-year strategic planning (3-5 year horizons)
• Budget templates by department and industry
• Calendar spreading with seasonality patterns
• Budget locking and change tracking

B4. Variance Analysis & Reporting

• AI-powered natural language variance explanation
• Drill-down from variance to source GL transactions
• Threshold-based auto-alerts (% and absolute)
• Risk flagging for at-risk revenue/cost items
• Automated board book generation
• Personalized narrative by audience (CFO vs Controller vs CEO)
• Multi-language report generation (PT-BR, EN-US, ES)

Category C: AI Agent System (DIFFERENTIATOR)

C1. Agentic AI Financial Assistant (F-013 Enhanced)

• Conversational AI chat with multi-turn context
• Autonomous task completion: "Close Q1 variance analysis"
• Natural language query → SQL → chart + narrative pipeline
• Auto-categorization of income/expenses (95%+ accuracy target)
• Anomaly detection in transactions and reconciliations
• Predictive cash flow insights with proactive alerts
• Tax optimization suggestions with regulatory citations
• WhatsApp/Slack/Teams bot integration

C2. Multi-Agent Orchestration

• Orchestrator-Workers pattern for complex financial workflows
• Agent types: Data Agent, Model Agent, Narrative Agent, Compliance Agent, Distribution Agent
• Evaluator-Optimizer loop for compliance validation
• Human-in-the-loop checkpoints for high-value decisions
• Agent audit trail with full reasoning provenance
• Token-efficient model routing (Haiku for boilerplate, Sonnet for logic, Opus for compliance)

C3. Document Intelligence

• OCR/AI capture from PDF, email, photo, invoice
• Multi-format ingestion: NF-e XML (Brazil), Invoice PDF (USA), receipt photos
• Auto-extraction: vendor, amount, date, category, tax
• Confidence scoring with human review queue for low-confidence extractions
• Document classification and archival
• Searchable document repository with full-text indexing

Category D: Integration Ecosystem

D1. ERP & Accounting Connectors

• Brazil: Omie, Conta Azul, Tactus, Domínio, Sage
• USA: QuickBooks Online/Desktop, Xero, NetSuite, SAP Business One
• Global: SAP S/4HANA, Oracle Financials, Microsoft Dynamics 365
• Bi-directional sync (push budgets back to ERP)
• COA normalization across all connected ERPs
• Real-time CDC (Change Data Capture) for instant actuals

D2. Banking & Payment

• Open Finance Brasil (BACEN-regulated bank sync)
• Plaid (US bank connectivity)
• Payment processors: Stone, Cielo, Rede, PagSeguro (BR) + Stripe, Square, Adyen (US)
• Boleto/Pix generation and reconciliation
• ACH/Wire payment initiation

D3. Operational Systems

• CRM: Salesforce, HubSpot, Pipedrive (pipeline forecasting)
• HRIS: BambooHR, Gupy, Factorial (headcount planning)
• Expense: Concur, Expensify, Flash (T&E data)
• E-commerce: Shopify, VTEX, Mercado Livre (revenue actuals)

D4. Data Infrastructure

• Data warehouses: Snowflake, BigQuery, Redshift, Databricks
• Streaming: Kafka, Kinesis for real-time transaction feeds
• File import: CSV, Excel, PDF, OFX, XML
• API: RESTful + GraphQL + WebSocket for real-time

Category E: Security, Compliance & Governance

E1. Access Control

• Role hierarchy: CFO → Controller → FP&A Analyst → Viewer
• Row-level security (user sees only their entity/department)
• Cell-level locking for approved budgets
• Multi-factor authentication (TOTP, WebAuthn, SMS)
• SSO integration (Okta, Azure AD, OneLogin, Keycloak)
• AI service accounts with least-privilege (read GL, write forecasts, no delete)
• Just-In-Time access for audit engagements

E2. Audit & Compliance

• Immutable audit trail (immudb + pgaudit) with 7-year retention
• AI provenance: every forecast links to source transactions + model version + reasoning trace
• SOC 2 Type II controls (automated evidence collection)
• LGPD compliance (data residency, right to deletion, consent management)
• BACEN/CVM regulatory reporting support
• SOX-ready controls (separation of duties, change management)
• EU AI Act compliance (explainability, risk classification)
• Automated compliance dashboards

E3. Data Protection

• Encryption at rest (AES-256) and in transit (TLS 1.3)
• PHI/PII detection in code and configurations
• Data masking for non-production environments
• Key rotation via HashiCorp Vault
• DLP policies for file export
• Penetration testing readiness (zero critical vulnerabilities target)

Category F: Platform & User Experience

F1. Multi-Tenant SaaS Platform

• Tenant isolation via PostgreSQL RLS
• Custom branding (white-label UI)
• Subscription management with tiered pricing
• Self-service admin (business users manage without IT)
• Template library by industry vertical
• 99.9% uptime SLA
• Support for 10,000+ concurrent users

F2. Excel & Spreadsheet Integration

• Excel Add-In with bi-directional sync
• Google Sheets Add-On
• Formula preservation (keep Excel formulas live)
• PivotTable support on FP&A data
• Budget upload via Excel/Google Sheets
• Excel-like formula language in web UI

F3. Mobile & Offline

• iOS/Android responsive dashboards
• Mobile data entry for actuals and approvals
• Push notifications for pending actions
• Offline planning with conflict resolution on sync
• Mobile OCR receipt capture (camera → AI → AP entry)

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7. RESEARCH TASKS (Ordered by Priority)

Phase 1: Foundation Architecture

Task ID	Research Task	Output Artifact	Priority
R-001	Design PostgreSQL star schema for unified FP&A data model (Fact: gl_transactions, Dimensions: accounts, entities, periods, vendors, currencies) with multi-tenant RLS policies	DDL script + ER diagram (Mermaid)	P0
R-002	Create dbt project structure (staging/, intermediate/, marts/) with macros for QuickBooks/NetSuite/Omie/Conta Azul COA normalization	dbt project scaffold + macro library	P0
R-003	Design Airbyte connector configuration for all target ERPs (QuickBooks, Xero, NetSuite, Omie, Conta Azul) with CDC support	YAML configs + custom connector specs	P0
R-004	Write OpenFGA authorization model for multi-entity financial data with Brazilian regulatory roles	DSL policy + FastAPI middleware	P0
R-005	Design immudb integration for append-only audit log forwarding with AI provenance fields	Integration script + audit schema	P0

Phase 2: AI & Intelligence Layer

Task ID	Research Task	Output Artifact	Priority
R-006	Design LangGraph state machine for Budget-vs-Actual workflow (fetch actuals → compare budget → invoke reasoning → generate narrative → alert)	Workflow diagram + Python implementation	P0
R-007	Create NeuralProphet training pipeline for financial time-series forecasting using historical GL data	Python pipeline + training config	P1
R-008	Build AI explainability layer: log every decision with model version, input features, confidence score, reasoning trace	OpenAPI 3.0 spec + implementation	P1
R-009	Design multi-agent orchestration for autonomous month-end close process	Agent architecture + delegation templates	P1
R-010	Build NLQ (Natural Language Query) pipeline: user question → SQL generation → visualization → narrative	End-to-end pipeline specification	P1

Phase 3: Integration & Data

Task ID	Research Task	Output Artifact	Priority
R-011	Design Open Finance Brasil integration (BACEN-regulated) for real-time bank account sync	API spec + compliance checklist	P1
R-012	Build multi-currency consolidation engine with automatic FX conversion and intercompany eliminations	Calculation engine spec + test cases	P1
R-013	Design payment processor reconciliation engine for Stone/Cielo (BR) and Stripe (US) with MDR rate comparison	Reconciliation algorithm + data model	P2
R-014	Create OCR/AI document ingestion pipeline for NF-e, invoices, receipts across BR/US formats	Pipeline architecture + ML model requirements	P2

Phase 4: Infrastructure & Deployment

Task ID	Research Task	Output Artifact	Priority
R-015	Create Docker Compose for 12-service local dev stack	docker-compose.yml	P0
R-016	Write Kubernetes manifests with Helm charts for production deployment	K8s manifests + Helm values	P1
R-017	Design Dagster asset-centric orchestration for Airbyte → dbt → AI pipeline	Dagster project structure + asset definitions	P1
R-018	Design disaster recovery plan (RTO <4hrs, RPO <15min)	DR playbook	P2

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8. ARCHITECTURE DECISION RECORDS (ADRs) REQUIRED

Each ADR must follow the format: Context → Decision → Consequences → Alternatives Considered

ADR ID	Decision	Options	Compliance Impact
ADR-001	PostgreSQL over SQL Server	PostgreSQL vs SQL Server vs CockroachDB	Multi-tenancy RLS, LGPD data residency
ADR-002	Airbyte over custom connectors	Airbyte vs Meltano vs custom-built	Connector maintenance, CDC support
ADR-003	DeepSeek-R1 over Azure OpenAI	DeepSeek vs Llama vs Azure OpenAI	Air-gap capability, financial data sovereignty
ADR-004	OpenFGA over .NET Identity	OpenFGA vs Cerbos vs Casbin	Relationship-based auth for financial hierarchies
ADR-005	Dagster over Airflow	Dagster vs Airflow vs Prefect	Asset lineage, data observability
ADR-006	NeuralProphet over Prophet	NeuralProphet vs Prophet vs SARIMAX vs XGBoost	Accuracy, explainability, training cost
ADR-007	LangGraph over CrewAI	LangGraph vs CrewAI vs AutoGen	Deterministic finance vs parallel execution
ADR-008	immudb over PostgreSQL triggers	immudb vs append-only tables vs blockchain	Cryptographic verification for SOX/LGPD
ADR-009	FastAPI+Go over .NET Core	FastAPI vs Go vs .NET Core vs Rust	AI ecosystem alignment, performance
ADR-010	React (Refine) over Angular	React vs Angular vs Vue vs Svelte	Component ecosystem, developer availability

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9. C4 ARCHITECTURE MODEL (C4 → C1)

Generate complete C4 architecture diagrams in Mermaid syntax:

Level 4 (Code): Internal structure of key components

• PostgreSQL schema relationships
• LangGraph state machine internals
• OpenFGA policy evaluation flow
• dbt transformation DAG

Level 3 (Component): Each container's internal components

• API Gateway components (routing, auth, rate limiting)
• AI Engine components (inference, training, evaluation, provenance)
• Data Pipeline components (ingestion, transformation, loading, validation)
• Dashboard components (query, visualization, export, alerting)

Level 2 (Container): Technology choices and communication

• Frontend Container (React/Refine)
• API Container (FastAPI + Go services)
• Database Containers (PostgreSQL, DuckDB, immudb, Redis)
• AI Container (vLLM, NeuralProphet, PyOD)
• Integration Container (Airbyte, Dagster, dbt)
• Policy Container (OpenFGA, Zitadel)
• Monitoring Container (Prometheus, Grafana, Vector)

Level 1 (Context): System boundaries and external actors

• Users: CFO, Controller, FP&A Analyst, Auditor, Accountant
• External Systems: ERPs, Banks, Payment Processors, Tax Authorities (SEFAZ, IRS)
• Regulatory Bodies: BACEN, CVM, SOC 2 Auditors

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10. TECHNICAL DESIGN DOCUMENT (TDD) REQUIREMENTS

Generate TDD sections for each major subsystem:

1. Data Layer TDD: PostgreSQL schema design, RLS policies, partitioning strategy, indexing for OLAP, DuckDB analytics layer
2. AI/ML Layer TDD: Model serving architecture, training pipelines, A/B testing framework, model registry, feature store
3. Integration Layer TDD: Airbyte connector management, dbt transformation patterns, Dagster orchestration, CDC architecture
4. Security Layer TDD: Authentication flow, authorization evaluation, audit trail generation, encryption key management
5. API Layer TDD: REST API design, GraphQL schema, WebSocket protocol, rate limiting, versioning strategy
6. Frontend Layer TDD: Component architecture, state management, real-time updates, Excel integration, mobile responsiveness

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11. SOFTWARE DESIGN DOCUMENT (SDD) REQUIREMENTS

The SDD must cover:

1. System Overview: Context diagram, deployment topology, technology stack justification
2. Data Architecture: Logical data model, physical schema, data flow diagrams, ETL/ELT patterns
3. Application Architecture: Service decomposition, API contracts, message flows, error handling
4. AI Architecture: Model pipeline, inference architecture, training infrastructure, explainability framework
5. Security Architecture: Authentication, authorization, encryption, audit, incident response
6. Infrastructure Architecture: Container orchestration, networking, storage, backup, DR
7. Integration Architecture: Connector patterns, data transformation, reconciliation, monitoring
8. Non-Functional Requirements: Performance (P95 <500ms), scalability (10K+ users), availability (99.9%), security (zero critical vulns)

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12. CODITECT PRODUCT SUITE IMPACT ANALYSIS

How This FP&A Platform Informs Coditect Development

Aspect	FP&A Requirement	Coditect Product Opportunity
Multi-Agent Orchestration	Financial workflow automation via orchestrator-workers	Prove Coditect's autonomous agent capabilities in regulated financial domain
Compliance Automation	LGPD, SOC 2, BACEN, SOX controls	Demonstrate compliance-native development — Coditect's core differentiator vs. Cursor/Copilot
ADR Generation	10+ architecture decisions with formal documentation	Validate Coditect's autonomous ADR generation capability
dbt/SQL Generation	COA normalization macros, star schema DDL	Test Coditect's ability to generate production data transformation code
AI Pipeline Scaffolding	NeuralProphet training, LangGraph workflows	Prove Coditect can scaffold ML/AI infrastructure autonomously
Infrastructure-as-Code	Docker Compose, Kubernetes, Helm, Terraform	Validate IaC generation quality for regulated deployments
Event-Driven Architecture	CDC, real-time alerts, streaming data	Stress-test Coditect's event-driven development methodology
Multi-Tenant SaaS	RLS policies, tenant isolation, subscription management	Prove Coditect handles SaaS complexity in production patterns
Dual-Jurisdiction	BR + US regulatory, multi-currency, multi-language	Test Coditect's ability to reason about cross-border compliance
Document Intelligence	OCR pipeline, document classification, extraction	Expand Coditect's capabilities into ML pipeline development

Strategic Value for Coditect

1. Reference Architecture: This FP&A platform becomes a showcase for Coditect's autonomous development in regulated fintech
2. Feature Validation: Every module built validates Coditect's capability to handle production-grade financial software
3. Compliance Template Library: ADRs, audit patterns, and RBAC policies become reusable Coditect templates
4. Market Positioning: "Coditect built this FDA/HIPAA/SOC2-compliant FP&A platform autonomously" — ultimate demo
5. Revenue Model: FP&A platform can become a Coditect product suite offering (build + operate)

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13. OUTPUT SPECIFICATION

Deliver all artifacts in Markdown format with:

• Inline citations to official documentation, arxiv papers, and GitHub repos
• Mermaid diagrams for all architectural visualizations
• Production-ready code (not pseudocode) for all technical implementations
• Compliance annotations linking each feature to regulatory requirements
• Cost projections comparing proprietary vs. open-source options
• Test specifications for critical paths

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14. CONSTRAINTS & NON-NEGOTIABLES

1. Zero Proprietary AI Dependencies: All LLM inference must run locally (no OpenAI/Anthropic API calls from production)
2. Open-Source Core: MIT, Apache 2.0, AGPL, or BSD licenses only for core infrastructure
3. Multi-Tenancy First: Every table, every API, every query must support tenant isolation via RLS
4. Dual Jurisdiction: Native support for Brazilian (NF-e, LGPD, BACEN) and US (GAAP, SOX, SOC 2) compliance
5. Performance: P95 API latency <500ms, forecast generation <30s for 5-year history
6. Air-Gap Capable: Full functionality without internet (on-premises deployment option)
7. Audit Everything: Every AI decision, every data mutation, every access event logged with cryptographic verification
8. Explainable AI: Every forecast must link to source transactions with model version and reasoning trace

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15. SUCCESS CRITERIA

The architecture is complete when:

• A senior engineer can deploy the full stack in <4 hours using provided IaC
• A CFO can generate a rolling 12-month forecast with variance explanations in <5 clicks
• An external auditor can verify every AI decision's provenance back to source transactions
• The system passes a penetration test with zero critical vulnerabilities
• All 13 existing Avivatec modules have migration paths documented
• The AI agent system can autonomously complete a month-end variance analysis
• Multi-currency consolidation produces results matching manual Excel calculations to <$0.01

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16. EXECUTION PROTOCOL

Step 1: Foundation Research (This Prompt)

Execute comprehensive research across all 14 feature categories. Validate technology choices against 2026 market requirements. Produce gap analysis between current Avivatec and target state.

Step 2: Architecture Documentation

Generate C4 diagrams (all 4 levels), ADRs (all 10), TDD, and SDD as separate markdown artifacts.

Step 3: Data Model & Schema

Produce PostgreSQL DDL, dbt project structure, Airbyte configurations, and OpenFGA policies.

Step 4: AI Pipeline Design

Design LangGraph workflows, NeuralProphet training pipeline, NLQ engine, and multi-agent orchestration.

Step 5: Infrastructure-as-Code

Generate Docker Compose, Kubernetes manifests, Helm charts, and Terraform modules.

Step 6: Implementation Roadmap

Produce sprint-level backlog organized by the 4-phase migration plan with dependencies, risks, and milestones.

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17. SUGGESTED FOLLOW-UP RESEARCH PROMPTS

Architecture Deep Dives

1. "Generate the complete PostgreSQL star schema DDL with multi-tenant RLS policies, optimized indexes for OLAP queries, and DuckDB analytical layer configuration"
2. "Design the complete LangGraph state machine for automated Budget-vs-Actual variance analysis with DeepSeek-R1 reasoning, including error handling, retry logic, and audit trail generation"
3. "Write the OpenFGA authorization model for a multi-entity FP&A platform with Brazilian regulatory roles (CVM, BACEN compliance officers) and AI service accounts"
4. "Create the complete Dagster asset-centric pipeline: Airbyte ingestion → dbt transformation → NeuralProphet forecasting → LangGraph analysis → Apprise alerting"

AI & ML Deep Dives

5. "Design the NeuralProphet training pipeline for financial time-series forecasting with automatic model selection, hyperparameter tuning, and drift detection"
6. "Build the NLQ (Natural Language Query) engine: user question → intent classification → SQL generation → DuckDB execution → Plotly visualization → narrative generation"
7. "Design the multi-agent system for autonomous month-end close: Data Agent (reconciliation) → Analysis Agent (variances) → Narrative Agent (board report) → Distribution Agent (stakeholder delivery)"

Integration Deep Dives

8. "Design the Open Finance Brasil integration architecture meeting BACEN regulatory requirements for real-time bank account synchronization"
9. "Create the payment processor reconciliation engine for Stone/Cielo (BR) + Stripe (US) with MDR rate comparison and automated variance detection"
10. "Build the OCR/AI document ingestion pipeline supporting NF-e XML, US invoices, receipt photos, and email attachments with confidence scoring"

Infrastructure Deep Dives

11. "Create the complete Docker Compose stack for local development: PostgreSQL, immudb, Airbyte, Dagster, dbt, vLLM, OpenFGA, Zitadel, Redis, Prometheus, Grafana, Vector"
12. "Write Kubernetes manifests with Helm charts: StatefulSets for databases, Deployments for API/AI services, CronJobs for nightly forecast generation, HPA for auto-scaling"
13. "Design the Terraform modules for GCP deployment: Cloud SQL (PostgreSQL), GKE, Secret Manager, Cloud Build, Artifact Registry"

Compliance Deep Dives

14. "Generate the SOC 2 Type II control mapping for the AI-first FP&A platform with automated evidence collection scripts"
15. "Design the LGPD compliance framework: consent management, right to deletion workflows, data residency enforcement, breach notification automation"
16. "Create the EU AI Act compliance documentation for financial AI agents: risk classification, explainability requirements, human oversight protocols"

Coditect Integration Deep Dives

17. "Analyze how each FP&A module validates Coditect's autonomous development capabilities and produce a capability mapping matrix"
18. "Design the Coditect template library derived from FP&A compliance patterns (ADRs, audit trails, RBAC policies) for reuse across regulated industry projects"
19. "Create the Coditect product suite positioning: FP&A as a reference implementation demonstrating autonomous development in regulated fintech"

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This prompt synthesizes 7,500+ lines of existing research, 100+ user stories, 200+ feature specifications, and 2026 competitive analysis into a unified framework for building the market-leading AI-first FP&A platform while simultaneously validating and advancing the Coditect autonomous development platform.