Governed AI for the systems your Business runs on
Megapodes exists so organizations can use AI to build systems of record — under explicit human governance, on infrastructure they control.
Our mission
Every business needs custom software — CRMs, ERPs, operations dashboards, inventory trackers. Building them is expensive and slow, and it demands specialized skills. No-code platforms help, but they lock your data into their cloud. Custom development is flexible, but it takes months.
Megapodes is our answer: a platform where you describe what you need in natural language, an AI agent builds it as validated configuration — never code — you approve every change, and it runs on your own infrastructure. No cloud AI. No per-seat fees. No data leaving your network.
Why “Megapodes”?
Megapodes are birds that build elaborate incubation mounds — carefully constructed, self-sustaining structures engineered to work without constant intervention. That is the standard we hold the platform to: built once, operated safely, verifiable at every step — under explicit human governance. Not a system you are asked to trust blindly, but one whose every change you can preview, approve, and audit.
Core principles
1. Metadata, not code
An application is validated declarative metadata — collections, fields, relations, pages, blocks, workflows, permissions — interpreted by a fixed, hand-built, tested runtime. The AI emits configuration, not code. One Zod schema per artifact governs both generation and runtime validation. Because the AI emits validated configuration — never executable code — it cannot inject arbitrary logic; every proposal is schema-checked and dry-run before you approve it.
2. Human in the loop
Every structural mutation — whether proposed by AI or made manually — requires preview, human approval, transactional apply, and an append-only audit log entry. There is no “confirm all” shortcut. Destructive items are individually confirmed, with the data at risk named. The human is always the gatekeeper — and that gate is the product, not friction.
3. Two AI planes, hard boundary
Build-time AI creates and modifies structure. Runtime AI Employees work on data with role-bound, data-only tools. A hard boundary — the AI security boundary — separates them, enforced server-side and tested adversarially. Build-time AI cannot operate on data. Runtime AI is hard-denied the build surface. Neither can assume the other’s powers.
4. Self-hostable by default
Your data stays on your infrastructure. A local LLM (via Ollama) runs on your GPU host. There is no cloud LLM dependency — the system fails closed if the local model is unavailable, never degrading silently. Air-gapped deployment is supported. You own your data, your models, and your infrastructure.
5. Fail-closed, not fail-open
If the AI cannot satisfy a gate, the operation fails loudly. AI features fail closed: if the local model is unavailable, AI-assisted operations stop and report why, while the running application continues under normal manual operation. If a security check cannot be satisfied, access is denied with a 403. No silent degradation, no fallback to unsafe behavior.
6. Microkernel extensibility
All capabilities register through kernel extension points — field types, block types, workflow nodes, auth providers, data-source drivers, AI tools. Plugins are npm packages with a manifest and lifecycle. Nothing patches core. Built-in capabilities use the same plugin system, proving the architecture works. The platform is extensible without being fragile.
Accountability you can verify
All capabilities register through kernel extension points — field types, block types, workflow nodes, auth providers, data-source drivers, AI tools. Plugins are npm packages with a manifest and lifecycle. Nothing patches core. Built-in capabilities use the same plugin system, proving the architecture works. The platform is extensible without being fragile.
The tech stack
We chose widely audited, operationally mature technology — PostgreSQL, Node.js, Redis — components your teams already know how to secure and operate. Node.js 22 LTS and Fastify 5 for the API. React 18 with Ant Design 5 for the UI. PostgreSQL 18 with pgvector for data and embeddings. Redis for caching and queue coordination, BullMQ for background work. MinIO/S3 for object storage. Ollama for local LLM inference. LangChain and LangGraph for AI orchestration. Zod for schema validation. Turborepo and pnpm for the monorepo.
Every choice prioritizes reliability, security, and self-hostability over novelty. The one genuinely novel part is the AI — and even that is constrained, validated, and gated behind human approval.
Who we are
Megapodes was founded in 2025 as a product-first engineering company. We hold our public claims to the same standard we hold the platform: verifiable, or not made. That is why you will not find customer logos, testimonials, or usage statistics on this site yet — we publish evidence, not projections.
Megapodes is built by Megapodes Technologies Pvt. Ltd. , incorporated in India, serving customers globally.
Become a design partner
We're onboarding a limited number of enterprise design partners: direct access to the founding team, influence on the roadmap, and guided pilots in your environment — including on-premises and air-gapped deployments.
Work with the platform
Talk to our engineers
Ask about architecture, the security model, plugin development, or deployment in your environment.
Docs
Read the documentation, API reference, and plugin development guide.
See governed AI building under your control
Walk through the approval gate, the audit log, and a self-hosted deployment with our team.
Self-hosted — your data never leaves your network · Every change human-approved and audit-logged · SSO/OIDC available · We support your security review.
The governed AI application platform. A local AI builds business systems as validated configuration, every change is human-approved, and it all runs on your infrastructure.