ASI works with founders, operators, and investors to design AI systems that stay stable under pressure: clear architectures, disciplined reasoning layers, and safety baked into the design.
We sit between strategy decks and raw implementation — translating hype and uncertainty into concrete, inspectable systems.
We help you cut through vendor noise and vague promises.
Named, narrow agents that quietly do the work — not hype demos.
Safety is an architectural property, not a disclaimer.
Standard language models behave like brilliant storytellers. IGLR behaves more like a careful analyst with guardrails — prioritising internal consistency and the ability to say “I don’t know” over fluent guesses.
IGLR is research-in-progress, not a product. But the principles already influence every system we design.
View the IGLR demo →A few representative outcomes from recent projects. Details are simplified and anonymised, but the structures and failure modes are real.
A seed-stage SaaS founder was drowning in investor updates, customer emails, and product prioritisation. Standard LLM automations were inconsistent and occasionally hallucinated details. We designed a small set of named agents, wrapped in a reasoning loop, that reduced weekly “thinking load” by ~40% with zero Monday-morning surprises.
An operations lead needed summaries across conflicting reports from engineering, support, and sales. Most tools blended the contradictions into a single narrative. Our gating layer refused to blur facts and instead highlighted gaps, disagreements, and unknowns — enabling better decisions with fewer meetings.
A fund exploring a frontier AI company wanted more than pitch-deck theatre. We built a reasoning pipeline that separated facts, claims, and speculation, then graded risk across technical debt, data exposure, and alignment. The output was a clear risk map the investor used to shape terms and follow-up questions.
If you’re operating near the edge of AI capability and need systems you can trust, not just demos, we should talk.