Context, playbooks, virtual employees, multi-LLM routing, and compute — how we design and run business divisions where humans and AI collaborate as one team.
Everyone chases the newest model. We think the real edge is somewhere else entirely: the shared, living context that every model — local or premium — gets to draw on. Here's why a knowledge Brain outlasts any single LLM, and what changes once your company runs on one.
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Asking an AI to "be careful" isn't a strategy. Here's how trained, versioned playbooks turn ad-hoc prompting into guaranteed, auditable quality across every division.
Why we never bet the company on a single inference provider — and how sensitive, burst, and latency-critical workloads each get the compute tier they actually need.
A fast local model handles routine work; a premium model steps in only when reasoning gets hard. Here's the routing logic that keeps quality high and cost low.
A virtual employee joins with domains, skills, and access — not a blank prompt window. Here's what onboarding one actually looks like next to a human hire.
Our org charts now show virtual employees next to human ones, with real reporting lines. Here's why that's not a gimmick — it's how accountability actually works.
Every new Magora division reuses the same Brain, the same playbooks, and the same virtual employees — only the industry changes. Here's the repeatable build pattern.
One login, every division, full provenance. We break down the non-negotiable requirements for a console that lets you talk to your whole automated company like a colleague.
If you can't trace an automated decision back to the data and the playbook that produced it, you don't have automation — you have a black box. Here's how we log everything.
Some tasks can wait for the cloud. Others can't. Here's how we decide what runs at the edge — and why that decision is invisible to the person on the other end.
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Talk to us about what a fully virtual-managed division could look like for your team — or explore how we work.
