Four layers, every product​

• Orchestration — model-agnostic planning and execution with LLMs and autonomous agents that hand off, call tools, and keep a human in the loop.
• Knowledge & retrieval — RAG over vector search keeps answers grounded in your data.
• Guardrails & observability — validation, policy checks, and full traceability of every material decision.
• Delivery — cloud services, embeddable widgets, or portable bundles that run air-gapped for regulated environments.

Cost is an engineering problem​

Running a frontier model for every task is wasteful. So we built CogniRank — a gateway that routes each request to the cheapest model that still clears the quality bar, cutting routine spend by 60–90%. Responsible use of compute is part of responsible AI.

Grounded, transparent, human-in-the-loop​

These aren't slogans — they're constraints in the architecture. Read more in our Responsible AI commitments, or dig into the Platform docs.

— The MetaVector engineering team

The problem​

The distance between "I have an idea" and "I have a working prototype" is still measured in weeks. Scaffolding, wiring, and deployment eat the time that should go into the idea itself.

How Think2App works​

1. Describe it. Tell Think2App what you want in natural language.
2. Generate. It scaffolds a full project — UI, logic, and structure — in seconds.
3. Refine. Edit in an in-browser Monaco IDE while an AI assistant applies iterative code diffs from your conversational prompts.
4. Preview & ship. Run it live in a sandboxed Docker runtime, then deploy in one click.

Why it matters​

Think2App collapses the idea-to-prototype loop from weeks to minutes, and keeps you in control of the code the whole way. It's the clearest expression of our belief that software should be AI-native, not AI-assisted.

Think2App is in beta. Request early access to start building.

Our thesis is simple: the best software of the next decade won't have AI added to it. It will be AI from the first line — designed around models, agents, and retrieval rather than retrofitted onto legacy workflows.

Why "MetaVector"​

A vector is how machines represent meaning. Meta is what sits above and connects them. We build the layer that turns raw model capability into products people can trust with real work — the connective intelligence between an idea and a working outcome.

What we're building​

We're shipping a suite of products across the work lifecycle:

• Think2App — describe an idea, get a working app.
• InvoicePing — run your business over WhatsApp.
• GuidrAI — enterprise AI support agents you can trust.
• AiSDR — autonomous sales development, end to end.
• ...and more on the Products page.

Our principles​

• AI-first, not AI-added.
• Grounded by default — retrieval and citations over guesswork.
• Human-in-the-loop — automate the work, keep people in control.

This is day one. If you want to build the future of AI products with us — as an early user, a partner, or a teammate — get in touch.

— The MetaVector Labs team