Agents are the easy part.
The agent data plane is not.
QAI is the data and cloud layer under agentic AI products: one Postgres carrying records, documents, spatial indexes and vector columns, an agent runtime with retrieval and conversation memory, and per-product credit metering. Four QAI products run on it today.
The platform, in four layers
One data plane, one agent runtime, one delivery layer and one Terraform-managed substrate underneath every QAI product.
QAI Data
One governed Postgres for records, documents and vectors
Relational records, documents, spatial indexes and vector columns in a single Postgres schema, under one permission model and one migration lineage.
QAI Agentic AI
The agent runtime: retrieval, memory and metered model calls
Persistent chats, replayed conversation history, and per-message model and token accounting — so an agent retrieves across the same database it writes to, and every call is attributable.
QAI Cloud
Delivery, edge and tenancy
The delivery layer in front of every QAI product — CDN and edge routing, verified-bot handling, rate limiting, and org-scoped tenancy with per-product credit metering.
QAI Infra
The AWS substrate, entirely in Terraform
Every environment is declared in Terraform and shipped through a pipeline with approval gates. There is no manual console change in the path to production.
QAI Finance
The research desk the QAI Fund invests in — interactive boards, per-company dossiers, and report decks built on frontier models and proprietary, web-grounded data. Every figure sourced, every thesis falsifiable.
QAI Earth
AI-powered geospatial sciences platform. Analyze satellite imagery, explore terrain with AI Geologists, and unlock spatial insights from our planet.
Research
QAI Lab works on the agentic-systems problems the platform has to solve — and ships the answers into the products.
Built by the lab that needed it
QAI Lab is an applied research lab working on agentic systems — retrieval, memory, tool use and evaluation — applied to public-market research, geospatial science and market mapping. Each one needed the same things underneath: a schema holding fundamentals, map layers and embeddings side by side, a runtime that could retrieve and remember, and a meter that said who used what.
QAI Finance, QAI Earth, QAI Insights and QVC run on the same schema and the same Terraform stacks. The products are not the offer — they are the load the layer has already carried.
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