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Executive Analysis: AI-First FP&A Platform Master Prompt

Document Overview​

Source: Master_System_Prompt__AI-First_Open-Source_FP_A_Pl.md
Size: ~7,500 lines | ~263KB
Analysis Date: 2026-02-03
Purpose: Research prompt for AI-first Financial Planning & Analysis platform architecture

Content Structure Analysis​

Document Composition​

SectionLines (Est.)Content TypeQuality
Core Architecture Requirements~300System design specifications⭐⭐⭐⭐⭐
Avivatec Gap Analysis~400Current vs. target state⭐⭐⭐⭐⭐
Integration Matrices~800ERP/Banking/CRM connectors⭐⭐⭐⭐⭐
AI Feature Prioritization~500ML/LLM roadmap⭐⭐⭐⭐⭐
Technical Specifications~2,500PostgreSQL/Terraform/Docker⭐⭐⭐⭐⭐
Pricing Strategy~300Market positioning⭐⭐⭐⭐
Go-to-Market~400Brazilian SMB focus⭐⭐⭐⭐
Research References~2,000Citations/footnotes⭐⭐⭐

Key Domains Covered​

┌─────────────────────────────────────────────────────────────────┐
│                    MASTER PROMPT COVERAGE                        │
├─────────────────────────────────────────────────────────────────┤
│  TECHNICAL ARCHITECTURE                                          │
│  ├── Data Ingestion (Airbyte/Meltano, COA harmonization)        │
│  ├── AI/ML Layer (DeepSeek-R1, NeuralProphet, LangGraph)        │
│  ├── Audit & Compliance (immudb, pgaudit, SOC2/HIPAA)           │
│  ├── RBAC (OpenFGA/Cerbos policy-as-code)                       │
│  ├── Infrastructure (Kubernetes, Dagster, Terraform)            │
│  └── UI/UX (Streamlit, NLQ, Excel sync)                         │
│                                                                  │
│  BUSINESS STRATEGY                                               │
│  ├── Market Analysis (FP&A competitive landscape 2026)          │
│  ├── Integration Ecosystem (50+ ERP/banking connectors)         │
│  ├── Pricing Model (tiered SaaS for Brazilian SMBs)             │
│  ├── Migration Path (Avivatec → Open Source hybrid)             │
│  └── GTM Strategy (Accountant distribution channel)             │
│                                                                  │
│  REGIONAL SPECIFICS                                              │
│  ├── Brazilian ERPs (Totvs, Omie, Conta Azul, Tactus)           │
│  ├── Open Finance Brazil (BACEN mandate)                        │
│  ├── Pix/Boleto payment integration                             │
│  └── LGPD compliance requirements                               │
└─────────────────────────────────────────────────────────────────┘

Critical Findings​

1. Architectural Vision​

The prompt designs a full-stack open-source FP&A platform with:

  • Zero proprietary licensing (MIT/Apache/BSD only)
  • Air-gapped AI capability (local LLMs via Ollama/vLLM)
  • Multi-tenant RLS (PostgreSQL row-level security)
  • Cryptographic audit trails (immudb Merkle trees)
  • Universal ERP ingestion (600+ Airbyte connectors)

2. Technology Stack Recommendations​

LayerChosen TechnologyAlternativeRationale
DatabasePostgreSQL + TimescaleDBSQL ServerOpen-source, RLS, JSONB
ELTAirbyteMeltanoGUI-friendly, 600+ connectors
AI ReasoningDeepSeek-R1 (local)Azure OpenAICompliance, cost (82% savings)
OrchestrationDagsterAirflowAsset-centric lineage
Agent FrameworkLangGraphCrewAIDeterministic finance workflows
Policy EngineOpenFGACerbosRelationship-based (Zanzibar)
ForecastingNeuralProphetProphet/ARIMA55-92% accuracy improvement

3. Avivatec Current State​

Existing platform built on:

  • Frontend: Angular
  • Backend: .NET Core APIs
  • Database: SQL Server
  • Cloud: Azure-native (Azure OpenAI, Log Analytics, DevOps)
  • Compliance: Brazilian BACEN/CVM approved

Gap: Proprietary lock-in, limited ERP connectors, no cryptographic audit

4. Migration Strategy (Hybrid)​

Phase 1 (Months 1-3): Infrastructure Decoupling
  └── PostgreSQL parallel deployment + Airbyte CDC sync
  
Phase 2 (Months 4-6): AI Layer Modernization  
  └── Replace Azure OpenAI with self-hosted DeepSeek-R1
  
Phase 3 (Months 7-9): ELT Universalization
  └── Replace custom connectors with Airbyte
  
Phase 4 (Parallel): Policy-as-Code RBAC
  └── Migrate .NET Identity to OpenFGA

5. Cost Impact​

ComponentAvivatec (Azure)Open SourceSavings
Database$500/mo$150/mo70%
AI API$2,000/mo$400/mo (GPU)80%
DevOps$200/mo$0 (GitLab)100%
Monitoring$300/mo$0 (Prometheus)100%
TOTAL$3,000/mo$550/mo82%

Prompt Quality Assessment​

Strengths​

  1. Comprehensive Technical Depth: Production-ready code samples (PostgreSQL DDL, Terraform, Python)
  2. Research-Backed: 100+ citations to arxiv papers, documentation, GitHub repos
  3. Decision Matrices: Clear ADR-style comparisons for technology choices
  4. Regional Expertise: Deep Brazilian market understanding (ERPs, Open Finance, tax)
  5. Iterative Structure: Built-in refinement protocol with clarifying questions

Weaknesses​

  1. Monolithic Size: 7,500 lines exceeds optimal context window management
  2. Reference Noise: ~2,000 lines of footnotes/citations add bulk without immediate utility
  3. Scope Creep: Covers 12+ domains in single prompt (dilutes focus)
  4. Missing Validation: No test cases or acceptance criteria for generated outputs
  5. Version Drift: References 2025-2026 products that may change

Recommendation: Modular Prompt Strategy​

Should You Run the Master Prompt As-Is?​

No. The 7,500-line monolithic prompt will:

  • Exceed practical token budgets (>100k tokens)
  • Produce shallow outputs across all domains vs. deep outputs in any
  • Create "lost in the middle" attention degradation

Optimal Approach: Decomposed Research Sprints​

┌─────────────────────────────────────────────────────────────────┐
│                    RECOMMENDED EXECUTION                         │
├─────────────────────────────────────────────────────────────────┤
│                                                                  │
│  SPRINT 1: Data Architecture (Week 1)                           │
│  ├── PostgreSQL schema + RLS policies                           │
│  ├── Airbyte connector configuration                            │
│  └── dbt transformation models                                  │
│                                                                  │
│  SPRINT 2: AI/ML Pipeline (Week 2)                              │
│  ├── LangGraph workflow for variance analysis                   │
│  ├── NeuralProphet forecasting pipeline                         │
│  └── Explainability API specification                           │
│                                                                  │
│  SPRINT 3: Compliance & Security (Week 3)                       │
│  ├── OpenFGA authorization model                                │
│  ├── immudb audit trail integration                             │
│  └── SOC2/HIPAA control mapping                                 │
│                                                                  │
│  SPRINT 4: Infrastructure (Week 4)                              │
│  ├── Docker Compose for local dev                               │
│  ├── Kubernetes manifests                                       │
│  └── Terraform GCP modules                                      │
│                                                                  │
│  SPRINT 5: Brazilian Market (Week 5)                            │
│  ├── Open Finance Brazil integration                            │
│  ├── Totvs/Omie/Conta Azul connectors                          │
│  └── LGPD compliance automation                                 │
│                                                                  │
└─────────────────────────────────────────────────────────────────┘

Next Steps​

  1. Review: 02-CODITECT-IMPACT-ANALYSIS.md for strategic implications
  2. Execute: 03-RESEARCH-PROMPTS-TIER1.md for highest-priority deep dives
  3. Expand: 04-RESEARCH-PROMPTS-TIER2.md for secondary research tracks
  4. Develop: 05-CODITECT-PRODUCT-IDEAS.md for platform feature opportunities