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AiQL combines symbolic graph representation, deep learning, and human-in-the-loop verification into a platform for knowledge-intensive operations. It is aimed at healthcare, finance, government, and other regulated industries where answers must be precise, traceable, and defensible. The platform sits as a reasoning layer over existing organizational data: databases, enterprise systems, documents and policies, expert knowledge, and external standards. What customers use is described under Products. Ingestion and query sit on the Middleware API. The parent company is in Singapore, with subsidiaries in the United States and Saudi Arabia. See Legal.

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How AiQL differs from generic AI

Generic systems search documents and guess. AiQL reasons across a structured knowledge layer that the organization maintains.
  • Answers include step-by-step reasoning chains and audit trails, rather than a black-box output.
  • Accuracy depends on encoded organizational knowledge, not on which model is called.
  • As structured knowledge grows, answers become more precise. Model-only systems can lose precision as information volume grows.
  • Inference cost does not have to rise with every query the way usage-priced model calls do. Value is meant to compound in the knowledge layer instead.

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How knowledge compounds

1

Experts sculpt knowledge

Business users contribute rules, processes, decisions, and judgment.
2

Knowledge compounds

Each use refines the knowledge layer. Institutional intelligence builds.
3

Reasoning improves

More structured knowledge produces more precise, explainable answers.
4

The organization adopts

Better answers increase daily use, which feeds the cycle.
AI model outputs can depreciate as information volume increases. AiQL is designed so that as organizational knowledge is scaled and structured, the system becomes more capable.

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Where it is aimed

These are target segments, not named clients. Client profiles and geographic focus are on Market. Named traction belongs on Clients.

Healthcare

Treatment protocols across standards, insurance, and regulations. Clinical reasoning that can be applied beyond a single physician.

Financial services

Credit decisioning with explainable rationale chains. Regulatory interpretation that is auditable and consistent. Fraud pattern reasoning with traceable logic.

Pharma and life sciences

Drug interaction reasoning across layered regulatory requirements. Pharmacovigilance with decision traceability. Regulatory submissions supported by structured guidelines and protocols.

Construction and real estate

Contract interpretation across FIDIC, SCA, and local regulations, with documented reasoning for permit decisions. Design specifications that reuse the organization’s expertise and past work.