Investor Pitch · July 2026

The ERP that builds itself around every business.

AI Dynamic ERP turns plain-language business requirements into production-ready modules: real PostgreSQL tables, relationships, APIs, user interfaces, workflows, tenant isolation, and role-based access.

AI Module Builder Real Database Tables Multi-Tenant SaaS Per-Module RBAC Local AI Option
CategoryAI-Native Composable ERP
BeachheadGrowing SMEs & multi-branch operators
Entry ProductAI module generator + core workspace
ExpansionMarketplace, agents, integrations, enterprise
01 / Problem

Businesses change faster than conventional ERP can be configured.

01

Traditional ERP forces companies to adapt their operations to rigid, pre-built modules.

02

Custom software fits better, but requires developers, long implementation cycles, and continuous maintenance.

03

No-code tools can prototype workflows quickly, but often produce fragmented data models and limited backend control.

04

As businesses add spreadsheets, SaaS tools, and internal apps, data becomes duplicated, disconnected, and difficult for AI to use safely.

05

SMEs need software that can evolve with their process without repeatedly buying, migrating, and rebuilding entire systems.

02 / Market Opportunity

Three fast-growing markets are converging: ERP, low-code, and enterprise AI.

TAM Direction

Global composable business software

ERP, workflow automation, low-code development, and AI agents are collapsing into one programmable operating layer.

SAM Assumption

Digitally active SMEs in Indonesia & Southeast Asia

Target businesses with 10–500 employees, multiple workflows, and increasing operational complexity.

5-Year SOM Model

5,000 paying tenants

At an illustrative blended ARR of US$1,200, this supports approximately US$6M ARR before usage and services expansion.

03 / Why Now

AI adoption is broad, but most companies still struggle to redesign and operationalize workflows.

88%

of surveyed organizations reported regular AI use in at least one business function in McKinsey's 2025 survey.

~1/3

reported that their companies had begun scaling AI programs, showing the gap between experimentation and operational deployment.

23%

reported scaling an agentic AI system, while another 39% were experimenting with AI agents.

The bottleneck is no longer access to a model. It is converting business knowledge into governed data structures, connected workflows, permissions, and reliable software actions.

04 / Current Solutions

Existing categories solve part of the problem, but not the full adaptation loop.

Traditional ERP

Integrated and reliable, but rigid, expensive to customize, and slow to implement.

Vertical SaaS

Strong for one industry workflow, but creates new silos as the business expands.

No-Code Builders

Fast to prototype, but governance, backend extensibility, and complex relational data can become limiting.

Custom Development

Perfectly tailored, but requires engineering capacity and creates long-term maintenance dependency.

05 / Solution

Describe the operation. AI Dynamic ERP builds the business application.

Today

Business must fit the software

  • Choose from fixed modules
  • Hire consultants for customization
  • Maintain spreadsheets around the ERP
  • Wait weeks or months for changes
  • Duplicate data across tools
→
AI Dynamic ERP

Software continuously fits the business

  • Describe fields, rules, views, and workflow
  • Generate a validated module specification
  • Create real tables and relations
  • Publish UI, API, permissions, and audit trail
  • Extend the same tenant data graph
06 / Key Innovation

AI generates governed software artifacts—not uncontrolled code in production.

Schema-First

Real PostgreSQL tables

Every approved module receives production-grade tables, tenant keys, constraints, indexes, and migration history.

Relationship-Aware

One evolving business graph

New modules can reference existing modules through validated one-to-one, one-to-many, and many-to-many relations.

Governed Generation

Preview, validate, approve, publish

AI produces a declarative specification. A deterministic compiler validates and deploys the database and application artifacts.

07 / Product Workflow

From business requirement to a secure module in six controlled steps.

Describe

User explains the workflow, fields, roles, and desired views.

Model

AI converts intent into a structured module specification.

Validate

System checks naming, data types, relations, security, and migration risk.

Preview

User reviews schema, forms, tables, permissions, and workflow behavior.

Publish

Compiler creates migrations, APIs, UI metadata, and RBAC policies.

Operate

Teams use the module immediately with audit logs and tenant isolation.
08 / Product Modules

A foundation for businesses to build their own operating system.

Tenant Workspace

Organizations, branches, users, plans, usage, settings, and tenant-level isolation.

AI Module Studio

Prompt-based module design, specification editor, preview, validation, and publishing.

Dynamic Data Engine

Real tables, migrations, indexes, relations, CRUD services, validation, and versioning.

Dynamic UI Runtime

Forms, list views, detail views, filters, dashboards, navigation, and responsive layouts.

RBAC & Governance

Tenant admin, custom roles, module permissions, row-level rules, approvals, and audit logs.

Workflow Automation

Triggers, conditions, approvals, notifications, scheduled actions, and cross-module updates.

AI Business Assistant

Natural-language query, summaries, anomaly detection, recommendations, and governed actions.

Integration Hub

REST APIs, webhooks, import/export, accounting, payments, commerce, and messaging connectors.

09 / Architecture

Cloud application reliability with the option to keep reasoning local.

Experience Layer

Next.js Application

Tenant dashboard, module studio, dynamic screens, admin, analytics, and API routes.

Next.jsReactTypeScriptServer Actions
Control Plane

Module Specification & Compiler

Schema DSL, validation engine, dependency graph, migration planner, UI metadata compiler, policy generator, and version registry.

PrismaMigration EngineRBACAudit LogJob Queue
Data & Intelligence

Neon + Ollama

Tenant-owned operational data in PostgreSQL with local or private model inference for planning and assistance.

PostgreSQLNeonOllamapgvectorObject Storage
10 / Business Model

Pricing scales with business complexity through users, module capacity, automation, and AI usage.

Starter

Small teams

$39 / tenant / month
  • Up to 5 users
  • Up to 5 active modules
  • Core RBAC
  • Basic reports
  • Shared AI quota
Growth

Scaling SMEs

$99 / tenant / month
  • Up to 20 users
  • Up to 15 active modules
  • Workflow automation
  • API & integrations
  • Expanded AI quota
Business

Complex operations

$249 / tenant / month
  • Up to 75 users
  • Up to 40 active modules
  • Advanced RBAC
  • Approvals & audit trail
  • AI agents & priority support
Enterprise

Unlimited scale

Custom
  • Unlimited users
  • Unlimited modules
  • Private cloud / VPC
  • Local AI deployment
  • SSO, compliance & SLA
Expansion Revenue

User & module add-ons

Customers can add user packs or module slots without immediately moving to a higher tier.

Usage Revenue

AI, automation, storage & API

Recurring usage charges grow as tenants run more agents, workflows, integrations, and data operations.

Services Revenue

Implementation & marketplace

Additional revenue from onboarding, migration, private deployment, premium module templates, and partner services.

Add-On

+5 users

$15 / month
Add-On

+5 module slots

$25 / month
Add-On

+50 GB storage

$10 / month
Add-On

Additional AI agent

$35 / month

Draft pricing for investor modeling. Final limits, add-on rates, and AI quotas should be validated through design-partner interviews, infrastructure cost measurement, and willingness-to-pay testing.

11 / Go-To-Market

Start with businesses that already feel the pain of outgrowing spreadsheets.

Beachhead Verticals

Distribution, services, clinics, education, and multi-branch retail

These businesses have repeatable operations, relational data, permission needs, and frequent workflow changes.

Acquisition

Founder-led sales + implementation partners

Win initial design partners directly, then scale through software agencies, ERP consultants, and industry specialists.

Land & Expand

Start with one painful workflow

Land through inventory, membership, procurement, field service, or CRM—then expand into connected modules and AI automation.

12 / Moat

Every deployed module strengthens the product's business-process intelligence.

Module Blueprint Library

Reusable patterns for data models, permissions, workflows, and vertical-specific operations.

Schema Relationship Graph

Deep understanding of how modules connect inside each tenant and across anonymized patterns.

Governed Compiler

Deterministic deployment infrastructure that separates AI planning from production execution.

Switching Depth

As modules, data, automations, permissions, and integrations accumulate, the platform becomes the operating layer.

13 / Roadmap

Build reliability first, then intelligence and ecosystem leverage.

Phase 1 · Foundation

Core platform

  • Multi-tenant auth
  • Tenant users & RBAC
  • Module spec DSL
  • Real-table generation
  • Dynamic CRUD UI
Phase 2 · Relationships

Composable operations

  • Cross-module relations
  • Safe schema evolution
  • Import/export
  • Approval workflows
  • Audit & rollback
Phase 3 · Intelligence

AI operating layer

  • Natural-language analytics
  • Agent tool calling
  • Anomaly detection
  • Suggested automations
  • Local AI deployment
Phase 4 · Ecosystem

Distribution at scale

  • Module marketplace
  • Partner console
  • Vertical templates
  • Enterprise controls
  • Regional expansion
14 / Funding Ask

Raising an illustrative US$750K pre-seed to prove repeatable module generation and reach product-market validation.

18-month objectives

01

Launch a stable multi-tenant MVP with governed module generation.

02

Onboard 20–30 design partners across 3–5 target verticals.

03

Reach 100 paying tenants and validate expansion revenue.

04

Build the initial implementation-partner channel.

Use of funds

Product & Eng.
45%
AI & Infra
20%
Go-To-Market
20%
Security & Legal
10%
Operations
5%

Funding amount and milestones are draft investor assumptions and should be updated based on founder runway, hiring plan, and validated customer pipeline.