CONFIDENTIAL -- AZ1.AI Inc. -- Internal Use Only
CFS-009: Development Roadmap
1. Executive Summary
The CODITECT Financial Suite development roadmap spans 36 months across 4 major phases, progressing from core financial engine to a fully autonomous, AI-powered global financial platform. Each phase delivers a complete, deployable product increment while building toward the full vision.
Guiding principle: Ship usable product early, expand scope iteratively. Every phase produces a product that partners can sell and clients can use. No "infrastructure-only" phases.
2. Roadmap Overview
Phase 1 (M1-6) Phase 2 (M7-12) Phase 3 (M13-18) Phase 4 (M19-24)
───────────────── ───────────────── ───────────────── ─────────────────
GL Engine Full Financial Global Platform Autonomous AI
Bank Rec Suite Platform
Doc Intelligence AP/AR Automation Consolidation Agent Workflows
Basic Compliance NLQ + FP&A Advanced Tax Federated Learning
Brazil + US Tax Engine Practice Mgmt Marketplace
100 partners E-Invoicing v2 EU + UK + Mexico India + Africa + AU
400 partners 1,200 partners 6,000 partners
3. Phase 1: Foundation (Months 1-6)
3.1 Objectives
| Objective | Target | Metric |
|---|---|---|
| Production GL engine with multi-currency, multi-entity | 100% IFRS/US GAAP/BR CPC coverage | Functional test suite |
| AI document intelligence v1 | >85% OCR accuracy, >80% auto-coding | Accuracy metrics |
| Bank reconciliation with AI matching | >85% auto-match rate | Match rate tracking |
| Brazil SPED compliance (ECD, ECF, EFD) | 100% SPED format compliance | Government acceptance |
| US GAAP reporting | Standard financial statements | CPA review |
| Partner portal v1 | Onboarding, training, client management | Partner adoption |
| 100 partners, 1,500 clients | Organic + Avivatec-sourced | ARR tracking |
3.2 Module Delivery Schedule
| Month | Deliverables |
|---|---|
| M1 | GL engine core (3-slot journal lines, multi-currency, multi-entity), PostgreSQL schema with RLS, API layer (FastAPI), basic React admin UI |
| M2 | Chart of accounts management (templates + custom), period management (open/close/lock), trial balance and balance sheet, basic IFRS/GAAP reporting |
| M3 | Bank reconciliation engine (rule-based + AI matching), bank statement import (OFX, CSV, MT940), document OCR pipeline v1 (Tesseract + LayoutLM), entity extraction for invoices and receipts |
| M4 | Brazil SPED module (ECD export, EFD-ICMS/IPI, EFD-Contribuicoes), NF-e integration (reading and matching), GL auto-coding v1 (XGBoost on transaction history), partner portal v1 (onboarding, training, dashboard) |
| M5 | Multi-entity management (entity hierarchy, intercompany transactions), currency revaluation (IAS 21 / ASC 830), US GAAP financial statements (income statement, balance sheet, cash flow), client onboarding workflow |
| M6 | Integration testing, security audit, performance optimization, beta partner deployment (10 firms), bug fixes and stabilization, production deployment |
3.3 Technical Milestones
| Milestone | Month | Criteria |
|---|---|---|
| Schema finalized | M1 | All core tables created, RLS policies active, migration framework in place |
| API v1 complete | M2 | All GL endpoints operational, OpenAPI spec published, auth/authz working |
| AI pipeline v1 | M3 | OCR + entity extraction + classification deployed, accuracy baselines established |
| SPED compliance certified | M4 | ECD/ECF/EFD files accepted by SPED validator, tested with sample data |
| Beta deployment | M5 | 10 partner firms onboarded, real client data flowing, support process active |
| GA release | M6 | Production infrastructure, monitoring, backup, DR tested, 100 partner target |
4. Phase 2: Financial Suite (Months 7-12)
4.1 Objectives
| Objective | Target | Metric |
|---|---|---|
| Accounts Payable automation | >90% straight-through processing | STP rate |
| Accounts Receivable management | >80% payment date prediction | Prediction accuracy |
| Natural Language Query engine | <3s response time, >85% query accuracy | Latency + accuracy |
| Forecasting engine (FP&A) | MAPE <15% on 12-month forecast | MAPE tracking |
| Tax engine v1 | 5 jurisdictions automated | Filing acceptance |
| E-invoicing v2 | Brazil + Mexico + Portugal | Government acceptance |
| 400 partners, 8,000 clients | 4x growth from Phase 1 | ARR tracking |
4.2 Module Delivery Schedule
| Month | Deliverables |
|---|---|
| M7 | AP module (invoice capture, 3-way matching, approval workflows, payment scheduling), AI duplicate detection (similarity scoring), vendor management |
| M8 | AR module (invoice generation, aging reports, collection workflows), payment prediction model (time-series + classification), dunning optimization (automated sequence with AI timing) |
| M9 | NLQ engine v1 (Claude API integration, schema-aware SQL generation, safety validation), FP&A basic (budget vs actual, variance analysis, basic forecasting), dashboard and reporting enhancements |
| M10 | Tax engine v1 (Brazil CBS/IBS parallel calculation, US sales tax, Mexico CFDI integration), e-invoicing v2 (Mexico CFDI 4.0, Portugal SAF-T), advanced document intelligence (handwriting, Asian languages) |
| M11 | Forecasting engine (NeuralProphet + ARIMA ensemble, scenario modeling, SHAP explainability), cash flow forecasting (Monte Carlo simulation), advanced NLQ (multi-turn conversations, follow-up questions) |
| M12 | Integration testing, performance optimization, security audit, 400 partner milestone push, Mexico and Portugal market entry, stabilization |
4.3 AI Capability Evolution
| Capability | Phase 1 Baseline | Phase 2 Target |
|---|---|---|
| OCR accuracy | >85% | >92% (with learning loop) |
| Auto-coding accuracy | >80% | >88% (tenant-specific models) |
| Bank rec auto-match | >85% | >92% |
| Document classification | >90% | >95% |
| NLQ query accuracy | N/A | >85% |
| Forecast MAPE (12-month) | N/A | <15% |
| AP straight-through processing | N/A | >90% |
| Payment date prediction | N/A | >80% |
5. Phase 3: Global Platform (Months 13-18)
5.1 Objectives
| Objective | Target | Metric |
|---|---|---|
| Consolidation engine | Multi-entity, multi-currency elimination | Test suite |
| Practice management suite | Workflow, time tracking, client portal, billing | Partner adoption |
| EU compliance (France, Germany, Spain) | Factur-X, XRechnung, Verifactu | Government acceptance |
| UK compliance (MTD) | HMRC MTD API integration | HMRC acceptance |
| Advanced FP&A | NLQ-driven analysis, what-if scenarios | User engagement |
| 1,200 partners, 30,000 clients | 3x growth from Phase 2 | ARR tracking |
5.2 Module Delivery Schedule
| Month | Deliverables |
|---|---|
| M13 | Consolidation engine (intercompany elimination rules, multi-level entity hierarchy, minority interest), currency translation automation (temporal + current rate methods) |
| M14 | Practice management v1 (workflow engine, task management, deadline tracking, client portal), time tracking and billing, staff assignment and capacity planning |
| M15 | EU compliance: France (Factur-X, FEC, liasse fiscale), Germany (XRechnung, E-Bilanz, GoBD), compliance engine plugin architecture finalized |
| M16 | EU compliance: Spain (Verifactu, SII), UK (MTD VAT via HMRC API), Peppol network integration (Belgium, Portugal, Australia pathway) |
| M17 | Advanced FP&A (NLQ-driven scenario modeling, automated variance explanation, board-ready report generation), month-end close automation (bottleneck prediction, auto-scheduling, progress dashboard) |
| M18 | Integration testing across all jurisdictions, performance at scale testing (30K clients), security audit, EU market launch events, partner certification program expansion |
6. Phase 4: Autonomous AI Platform (Months 19-24)
6.1 Objectives
| Objective | Target | Metric |
|---|---|---|
| Autonomous agent workflows | Reconciliation, categorization, reporting without human intervention | Automation rate |
| Fixed asset management | Full lifecycle (acquisition through disposal) | Module completeness |
| Revenue recognition (ASC 606 / IFRS 15) | Contract analysis and automated recognition | Recognition accuracy |
| India, Nigeria, Australia markets | GST, FIRS, Peppol compliance | Government acceptance |
| Federated learning | Cross-tenant model improvement with privacy preservation | Model accuracy improvement |
| AI marketplace | Partner-contributed custom models and workflows | Marketplace listings |
| 6,000 partners, 210,000 clients (Year 5 trajectory) | ARR tracking |
6.2 Module Delivery Schedule
| Month | Deliverables |
|---|---|
| M19 | Autonomous reconciliation agent (fully automated bank rec with exception routing), autonomous categorization agent (zero-touch GL posting for high-confidence transactions) |
| M20 | Fixed asset management (acquisition, depreciation methods, revaluation, disposal, tax vs book depreciation), India GST e-invoice integration |
| M21 | Revenue recognition engine (ASC 606 / IFRS 15 contract analysis, performance obligation identification, automated journal generation), Nigeria FIRS integration |
| M22 | Federated learning framework (privacy-preserving cross-tenant model training, differential privacy, secure aggregation), AI model marketplace v1 |
| M23 | Advanced autonomous workflows (month-end close agent, compliance filing agent, anomaly investigation agent), Australia Peppol integration, Colombia DIAN integration |
| M24 | Platform stabilization, autonomous workflow refinement, Year 3 planning, scale testing (100K+ clients), security audit, performance certification |
7. Technology Evolution
7.1 Stack Evolution by Phase
| Layer | Phase 1 | Phase 2 | Phase 3 | Phase 4 |
|---|---|---|---|---|
| Frontend | React 19 + Ant Design | + D3.js charts, advanced dashboards | + Real-time collaboration | + AI-generated insights overlay |
| API | FastAPI (Python) | + GraphQL subscriptions | + WebSocket real-time | + Agent-to-Agent protocol |
| Database | PostgreSQL 16 + RLS | + Read replicas, partitioning | + Citus sharding | + Multi-region replication |
| Cache | Redis | + Redis Cluster | + Redis Streams | + Distributed cache (multi-region) |
| Messaging | NATS | + NATS JetStream | + Event sourcing for audit | + Cross-region event mesh |
| AI/ML | Tesseract, LayoutLM, XGBoost | + Claude API, NeuralProphet, vLLM | + SHAP, custom fine-tunes | + Federated learning, agent framework |
| Infrastructure | GKE (single region) | + Multi-zone HA | + Multi-region (US + EU + BR) | + Edge compute for latency |
| Observability | Prometheus + Grafana | + Jaeger tracing, Loki logs | + SLO dashboards, PagerDuty | + AI-driven anomaly alerts |
7.2 Database Scaling Strategy
| Phase | Strategy | Capacity |
|---|---|---|
| Phase 1 | Single PostgreSQL 16 instance, pgBouncer connection pooling | 1,500 clients, ~50GB |
| Phase 2 | Primary + read replicas, table partitioning by tenant | 8,000 clients, ~300GB |
| Phase 3 | Citus distributed PostgreSQL (sharding by tenant_id) | 30,000 clients, ~1.5TB |
| Phase 4 | Multi-region Citus clusters with cross-region replication | 210,000 clients, ~15TB |
8. Team Plan
8.1 Team Growth
| Role | Phase 1 (M1-6) | Phase 2 (M7-12) | Phase 3 (M13-18) | Phase 4 (M19-24) |
|---|---|---|---|---|
| Engineering | ||||
| Backend engineers | 3 | 6 | 10 | 14 |
| Frontend engineers | 2 | 3 | 5 | 6 |
| AI/ML engineers | 2 | 3 | 5 | 7 |
| DevOps/SRE | 1 | 2 | 3 | 4 |
| QA engineers | 1 | 2 | 3 | 4 |
| Product | ||||
| Product manager | 1 | 2 | 3 | 4 |
| UX designer | 1 | 1 | 2 | 2 |
| Domain | ||||
| Accounting domain expert | 1 | 2 | 3 | 4 |
| Compliance/tax specialist | 1 | 2 | 4 | 6 |
| Partner Success | ||||
| Partner success managers | 1 | 3 | 6 | 12 |
| Technical advisors | 1 | 2 | 4 | 6 |
| Training managers | 0 | 1 | 2 | 3 |
| Total | 15 | 29 | 50 | 72 |
8.2 Key Hires by Phase
| Phase | Critical Hires | When | Why |
|---|---|---|---|
| Phase 1 | Lead Backend Architect | M1 | GL engine design and implementation leadership |
| Phase 1 | AI/ML Lead | M1 | Document intelligence pipeline architecture |
| Phase 1 | Brazil Compliance Expert | M1 | SPED/NF-e compliance certification |
| Phase 2 | NLP Engineer | M7 | NLQ engine development |
| Phase 2 | Forecasting Specialist | M9 | Time-series model ensemble |
| Phase 2 | US Tax Expert | M7 | US sales tax and reporting |
| Phase 3 | EU Compliance Lead | M13 | Multi-jurisdiction EU expansion |
| Phase 3 | Practice Management PM | M13 | Workflow and practice management product |
| Phase 3 | Security Architect | M13 | Multi-region security architecture |
| Phase 4 | ML Platform Engineer | M19 | Federated learning infrastructure |
| Phase 4 | Agent Framework Lead | M19 | Autonomous workflow engine |
9. Quality & Release Strategy
9.1 Release Cadence
| Release Type | Frequency | Content | Process |
|---|---|---|---|
| Major | Every 6 months (phase boundary) | New modules, major features, new jurisdictions | Full regression, beta period, staged rollout |
| Minor | Every 2 weeks (sprint) | Enhancements, minor features, bug fixes | Automated testing, canary deployment |
| Patch | As needed | Critical bugs, security fixes | Hotfix pipeline, immediate deployment |
| Compliance | As required | Regulatory updates, rate changes, format changes | Emergency pipeline, <48hr for critical |
9.2 Quality Gates
| Gate | Criteria | Automated |
|---|---|---|
| Unit tests | >90% code coverage, all passing | Yes (CI) |
| Integration tests | All API contracts verified, cross-module flows tested | Yes (CI) |
| Compliance tests | SPED/CFDI/MTD output validation against government schemas | Yes (CI) |
| Performance tests | P95 latency <200ms, throughput >1000 RPS | Yes (load test suite) |
| Security scan | Zero critical/high findings (SAST + DAST) | Yes (CI) |
| Accessibility | WCAG 2.1 AA compliance | Semi-automated |
| Domain review | Accounting accuracy verified by domain expert | Manual |
| Partner acceptance | 3+ partners sign off on beta | Manual |
9.3 Deployment Strategy
| Environment | Purpose | Update Cadence |
|---|---|---|
| Development | Active development, feature branches | Continuous (every commit) |
| Staging | Integration testing, QA validation | Daily (merge to main) |
| Canary | 2% of production traffic, early warning | Every minor release |
| Production | Full traffic, all tenants | After 24hr canary with no alerts |
10. Risk Management
10.1 Technical Risks
| Risk | Probability | Impact | Mitigation |
|---|---|---|---|
| AI accuracy below targets | Medium | High | Continuous model retraining, human-in-the-loop fallback, tenant-specific models |
| SPED format changes mid-development | High | Medium | Dedicated compliance monitor, modular format engine, rapid update pipeline |
| Scale bottlenecks at 10K+ clients | Medium | High | Early load testing, Citus sharding plan, read replica architecture |
| Security breach | Low | Critical | SOC 2 Type II, penetration testing, bug bounty, encryption at all layers |
| Key person dependency | Medium | High | Documentation-first culture, pair programming, knowledge sharing sessions |
10.2 Market Risks
| Risk | Probability | Impact | Mitigation |
|---|---|---|---|
| Slow partner adoption in US | Medium | High | Enhanced onboarding, ROI guarantees, referral incentives, AICPA partnership |
| Competitor AI feature launch | High | Medium | Speed to market, deeper domain AI, partner economics differentiation |
| Brazil tax reform delays | Medium | Low | Modular CBS/IBS engine, parallel old + new system support |
| Economic downturn | Low | Medium | Position as efficiency/cost-saving tool, maintain low pricing |
11. Budget Summary
11.1 Development Investment
| Category | Phase 1 | Phase 2 | Phase 3 | Phase 4 | Total |
|---|---|---|---|---|---|
| Engineering salaries | $450K | $870K | $1.5M | $2.1M | $4.92M |
| AI/ML infrastructure | $30K | $60K | $120K | $200K | $410K |
| Cloud infrastructure | $20K | $50K | $120K | $250K | $440K |
| Tools & licenses | $15K | $25K | $40K | $50K | $130K |
| Domain experts | $60K | $120K | $210K | $300K | $690K |
| QA & security | $20K | $40K | $80K | $120K | $260K |
| Partner success | $50K | $150K | $360K | $630K | $1.19M |
| Total | $645K | $1.315M | $2.43M | $3.65M | $8.04M |
11.2 Revenue vs. Investment
| Period | Cumulative Investment | ARR | Revenue (Cumulative) | Burn |
|---|---|---|---|---|
| End Phase 1 (M6) | $645K | $1.5M/yr run rate | $750K | -$0 (revenue covers) |
| End Phase 2 (M12) | $1.96M | $7.7M/yr run rate | $4.6M | Cash flow positive |
| End Phase 3 (M18) | $4.39M | $28M/yr run rate | $18.5M | Profitable |
| End Phase 4 (M24) | $8.04M | $79M/yr run rate | $53.5M | Highly profitable |
12. Success Criteria
12.1 Phase Gate Criteria
| Phase | Go/No-Go Criteria |
|---|---|
| Phase 1 -> 2 | 100 partners active, 1,500 clients, >85% AI accuracy baselines, SPED certified, partner NPS >30 |
| Phase 2 -> 3 | 400 partners, 8,000 clients, NLQ operational, forecast MAPE <15%, 3 jurisdictions live |
| Phase 3 -> 4 | 1,200 partners, 30,000 clients, 8+ jurisdictions, practice management adopted by >50% of Gold+ partners |
| Phase 4 complete | 3,000+ partners, 90,000+ clients, autonomous workflows >60% automation rate, federated learning operational |
12.2 North Star Metrics
| Metric | Definition | Year 1 Target | Year 3 Target |
|---|---|---|---|
| Partner capacity multiplier | Avg clients per firm before vs. after CODITECT | 2x | 5x |
| AI automation rate | % of transactions processed without human intervention | 60% | 85% |
| Time to first client | Days from partner sign-up to first live client | 21 days | 7 days |
| Net Revenue Retention | Annual cohort revenue retention | 110% | 130% |
Hal Casteel CEO/CTO, AZ1.AI Inc.
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