ASI designs AI as layered systems: cognition bases, reasoning loops, agents, and safety gates. This page gives a high-level view — not source code, but the scaffolding we use when we build with you.
We start at the bottom: which models, tools, and data do you actually have — and which do you trust? Instead of chasing every new API, we normalise around a small, composable capability set.
We treat models as interchangeable components: GPT-style LLMs, embeddings, search, code execution, custom APIs. The architecture must survive a model swap.
We design around the data you actually have: documents, logs, CRMs, product databases. Retrieval is not an afterthought — it shapes how we phrase tasks and expectations.
Interfaces are where humans and systems meet: chat, dashboards, automations, notifications. We bias toward low-friction entry points with clear expectations of what the AI can and cannot do.
We rarely ship “one big agent”. Instead, we give names and boundaries to a small number of roles — each with a clear job and handoff points back to humans.
Safety is not a filter you bolt on later. We build it into the loop: what the system is allowed to do, how it justifies itself, and when it must stop.
Invariants are non-negotiable rules: “never fabricate transaction IDs”, “totals must match ledger sums”, “don’t proceed when personally identifiable information is missing or unclear”.
We favour architectures where you can see why the AI acted the way it did: intermediate steps, source citations, decisions to escalate or abstain.
Sometimes the safest output is “I don’t know” or “this needs a human”. We design systems that can say that explicitly, rather than hallucinating confidence to keep the UI happy.
Every organisation has a different mix of constraints: regulation, legacy systems, teams, incentives. The architecture is the bridge between “what models can do” and “what you can safely deploy”.