The AI memory company.
We build the memory layer under enterprise AI: the part that knows the business and can prove it.
Our Thesis
The first era of enterprise AI was about deployment: getting chatbots and copilots into the hands of employees. The second era is about trust.
Right now, companies are scattering disconnected AI tools across their organisations. These tools guess context, hallucinate policies, and contradict each other. We believe the solution isn't smarter models. It's giving the models you already have a single, shared memory they can trust. Models are commoditising: whatever intelligence you can rent, your competitor can rent the same day. What can't be rented is what your organisation knows.
The same logic is unwinding the agent gold rush. Companies aren't building a hundred specialised agents because they need a hundred workers; each agent is a container for a narrow slice of context. Give AI one governed brain and you need fewer agents, not more: you define the workflows, and well-grounded AI carries them.
Symbolic AI, knowledge you can inspect, query and prove, failed commercially for forty years for one reason: every fact had to be hand-built by experts, and the companies that tried drowned in the cost. Large language models just dissolved that bottleneck: they can read your documents and propose the structure automatically. Loriq exists because the two halves finally complete each other: models that can read everything, and a governed memory that holds them to what's actually true.
That memory is SAGE. By combining machine reading with provable, source-traced knowledge, we ensure that when your AI speaks, it speaks with the authority of your actual data.
Why we're building this
Loriq started as an engineering problem, not a company. I'd watched enterprises roll out AI that was brilliant in a demo and unreliable in the field, not because the models were bad, but because they had no trustworthy memory of the business they were dropped into. Policies contradicted each other, documents went stale, and the AI answered confidently anyway.
So I built the engine first: SAGE existed before Loriq did. What started as an attempt to give one AI system a memory it could prove became a conviction: every enterprise AI will need this layer, and someone should build it properly, governed, source-traced, and deployed inside your infrastructure, not ours.
We're building Loriq in Australia, for organisations where a wrong answer has consequences. If that's your world, I'd like to hear what your AI gets wrong.
David Azzi, Founder, Loriq
Our Principles
Trust is provable, or it isn't trust
Every answer must be traceable back to its source. If it can't be shown, it shouldn't be said.
Enhance, don't replace
We build middleware. The AI you already own gets better, and nothing in your stack has to migrate.
Reason over search
Semantic similarity isn't enough. True enterprise AI requires structural, symbolic reasoning.
Built in Australia. Deployed in yours.
Loriq is Australian-built and Australian-owned, designed for data sovereignty from day one. We're growing. If hard problems in AI reliability sound like your kind of thing, introduce yourself.
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