Singapore’s 1st Neuro-Symbolic AI Company
Neuro-symbolic is where the field is heading. Gartner’s Hype Cycle for Artificial Intelligence, 2025 profiles neuro-symbolic AI as a form of composite AI that, in Gartner’s words, “can augment and automate decision making with less risk of unintended consequences” — and the research literature calls it “the third wave” of AI (Garcez & Lamb, 2023), after symbolic systems and today’s statistical models.
Safe Agentic AI
Enterprises want agentic AI — systems that plan, execute, and act on their own. Built on an LLM, that autonomy can act on its own hallucinations, unsupervised.
Reasonex™ keeps the autonomy and removes the risk.
Built for regulated industries
Same architecture. Six domains. The reasoning is mathematically necessary — not statistically probable.
The industries that can’t afford a wrong answer
In law, medicine, finance, and compliance, “probably right” is a liability. A hallucinated citation loses a case. A missed drug interaction costs a life. A misread capital rule draws a regulator. These industries don’t run on best guesses — they run on rules that must be applied exactly, and interpreted faithfully where they call for judgment.
That’s the one thing today’s AI cannot do. Large language models predict the most likely answer; they cannot guarantee the correct one. Reasonex was built the other way around. It reads the authoritative rules at their source, reasons over them deterministically — same facts, same answer, no language model anywhere in the reasoning path — and traces every conclusion back to the exact text that grounds it. Where the law is settled, it shows the path as firm. Where it calls for judgment, it hands that judgment to the professional. It never invents, and it never claims more than the source supports.
Legal Intelligence
General-purpose AI fails at legal work two ways: it fabricates citations, and it cannot reason deductively. A 2024 Stanford–Yale study (Magesh et al.) found that even leading RAG-based legal tools hallucinate on 17–33% of queries.
Clinical Decision Support
Medical guidelines are as intricate as legal statutes, and a wrong recommendation can cost a life. Reasonex reads the actual clinical guidelines and maps them onto the standard vocabularies hospitals already run on — SNOMED CT, ICD-10, LOINC, RxNorm — then traces the correct clinical pathway step by step, showing which steps follow firmly from the evidence and which call for clinical judgment.
HR AI
Every people decision — hiring, pay, leave, retrenchment — is governed by employment law, yet most are made by line managers without a lawyer in the room. In Singapore that means the Employment Act, the Workplace Fairness Act, and the company's own HR policy.
Insurance Underwriting & Claims
Deciding what a policy actually covers is a legal problem in disguise — a base form, narrowed by exclusions, restored by riders, capped by sublimits, gated by conditions. The answer depends entirely on the order and priority in which those layers apply — the part people, and probabilistic AI, routinely get wrong.
Intent-Based Networking in Telecom
Intent-based networking promises that an operator states the outcome and the network works out the configuration. That promise is now on the standards track: 3GPP TS 28.312 (Release 19) defines intent-driven management for 5G networks, TM Forum's IG1253 and Intent Ontology define how intent is expressed and negotiated in autonomous networks, and the ITU's IMT-2030 framework carries AI-native network autonomy forward into 6G.
Intent-Based Operations
Operations teams want to state the outcome — keep this service inside its target, never degrade emergency traffic — and have the system work out what that requires. Today that intent lives in brittle scripts that break the moment a situation falls outside what their author foresaw, or in LLM agents that decide probabilistically and can skip a step or invent an action — which, on live infrastructure, turns one fault into an outage.