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The MetaVector Platform

Every MetaVector product is built on a common, AI-native foundation. We don't bolt AI onto legacy software — we design from the model outward. That shared platform is why we can ship new products quickly while keeping them reliable and safe.

Four layers​

1. Orchestration​

A model-agnostic layer that plans and executes multi-step work with LLMs and autonomous agents. Agents can hand off to one another, call tools, and keep a human in the loop wherever a decision matters.

2. Knowledge & retrieval​

Retrieval-augmented generation (RAG) over vector search keeps answers grounded in your data and documents — not in the model's imagination. Every product can be pointed at a private knowledge source.

3. Guardrails & observability​

Inputs and outputs pass through validation, policy checks, and auditing. Every material decision is traceable back to the model, the knowledge, and the workflow that produced it — see Responsible AI.

4. Delivery​

Products deploy as cloud services, embeddable widgets, or — for regulated environments — portable bundles that run on-prem or fully air-gapped (see FactoAI Studio).

Principles that shape the architecture​

  • AI-first, not AI-added. The model is the core of the product, not a feature.
  • Grounded by default. Prefer retrieval and citations over unconstrained generation.
  • Cost-aware. Route work to the cheapest model that clears the quality bar (this is literally a product — CogniRank).
  • Human-in-the-loop. Automate the work; keep people in control of the outcomes.

The stack​

Large language models · multi-agent orchestration · vector search / RAG · React & Node.js · Python · containerized, cloud-native delivery.

Read on: Product Suite →