Magora
How we work.
The operating model

How we work.

One Brain engine holding thousands of scoped brains, a growing bench of virtual employees, guardrailed playbooks for guaranteed quality, multiple LLMs working in harmony, one universal console, and compute that lives wherever the work needs it. This is the operating model behind every division we run.

People to create. Virtual employees to carry the load.
The operating model

People to create. Virtual employees to carry the load.

It's the lesson 25 years of hiring taught us: repetitive work quietly burns people out. So we drew a clear line. Virtual employees run the repetitive, high-volume work without tiring — and the people we hire are the creative minds who direct them, set the standard, and make the calls only a human should.

Every request runs through that pairing: human judgment on top, tireless execution underneath. It's why our teams stay small, senior, and genuinely creative.

Layer 1 — the Brain

One Brain engine. Thousands of brains, each scoped to the job.

Magora doesn't run on one big knowledge base. The Brain is an engine that holds thousands of brains — one per entity, per project, per division — each carrying exactly the knowledge its work needs. Real people feed them: decisions written down, playbooks authored and reviewed, customer history, code and process all curated into the right brain, the way we actually want it used.

Every brain is governed. Employees and virtual employees are granted privileges to specific brains, and use them only inside the playbooks and workflows they're cleared for. The engine can even compose new brains out of existing ones — assembling sources by ruleset — so a fresh use case gets its own purpose-built brain in minutes.

And the Brain keeps every relation straight: which brain belongs to which entity or project, which person or virtual employee may use it and with what privileges, and every execution that drew on its knowledge. That web of relationships is what makes the rest of the model possible.

1
Brain engine
1000s
brains, one per job
0
knowledge silos
One Brain engine. Thousands of brains, each scoped to the job.
Virtual employees and virtual hands, matched to the task.
Layer 2 — capability

Virtual employees and virtual hands, matched to the task.

A virtual employee owns a role end to end — client success, content, engineering support. Virtual hands are narrower: a single capability (research, drafting, monitoring, first-pass QA) dispatched into any workflow that needs it. Together they amplify every person on the team instead of replacing them.

See what we do
Layer 3 — guardrails

Talk to a virtual employee like a colleague. A playbook keeps it honest.

There's no dashboard to configure and no prompt to engineer. You ask, hand off, correct — exactly like working with a person. Behind that conversation sits a playbook: a pre-trained, tested, guardrailed workflow that keeps the output consistent no matter who asked, what day it is, or which model answered.

Playbooks are what turn "an AI replied" into "the work was done right."

You — onboard the new partner account and set up their first campaign.
Noah (VE, Partner Ops) — starting the partner-onboarding playbook: contract check, Brain profile, campaign template.
Noah (VE, Partner Ops) — guardrail check passed, campaign live, welcome email sent. Ready for your sign-off
The workflow pipeline

One request, one guaranteed-quality pipeline.

Every task — whatever the division, whoever asked — moves through the same operating pipeline. Nothing skips the guardrails.

1

Context loaded

Brain pulls relevant history

2

VE picks it up

Right virtual employee, right role

3

Playbook runs

Guardrailed, pre-trained steps

4

Model routed

Local or premium LLM, by need

5

QA check

Automated + human-in-the-loop

6

Delivered

Logged, costed, traceable

Local and premium models, working in harmony.
Layer 4 — multi-LLM harmony

Local and premium models, working in harmony.

No single model runs everything. A router classifies each request and sends it to the model that fits — a fast local model for routine work, a premium model when reasoning gets hard — so quality never depends on which LLM happened to answer, and cost never spirals.

2+
model tiers, always routed
0
single-vendor dependency
A single chat console runs the whole company.
Layer 5 — one console

A single chat console runs the whole company.

Tasks, approvals, reports, hand-offs — one universal console ties every division together. Whether you're managing content, code, or customers, you talk to the same interface, and it routes you to the right virtual employee for the job.

  • Cross-division visibility, one login
  • Every conversation is provenance-tracked
  • Works the same on desktop and mobile
Layer 6 — compute everywhere

Hundreds of nodes. The right node for the right task.

Sensitive workloads run on our own hardware; burst workloads spill to the cloud; light tasks run at the edge, close to the user. A task never cares which node answers it — only that the right one does, and that capacity scales up or down on demand.

100s
compute nodes, right-sized per task
3
tiers: owned, cloud, edge
on-demand
scale up, scale down, no waste

About the operating model.

What is the Magora Brain?
The Brain is our shared corporate knowledge layer — decisions, playbooks, customer history, code, and process, all connected. Real people build and curate it; every virtual employee draws on it before doing any work.
How do playbooks guarantee quality?
A playbook is a pre-trained, guardrailed workflow. Instead of freeform prompting, a virtual employee runs a known-good sequence of steps with checks built in, so output stays consistent regardless of who asked or which model answered.
Why multiple LLMs instead of one?
A router classifies each request and sends it to the model that fits — a fast local model for routine work, a premium model when reasoning gets hard. This keeps cost down without capping quality.
Where does the compute run?
Wherever it makes sense — owned hardware, cloud, or edge, across hundreds of nodes. The Brain routes each task to the right node and scales up or down on demand.

See the model at work in our divisions.

Every division we run — and everything we build — runs on this exact operating model. Take a look, or come say hello.