01 · Your hardware
The local model
Runs on infrastructure you own, inside your network. Drafting, summarizing, searching, answering, the daily volume. Routine work stays where it already is.
All systems local01 · How it works
Two models. One of them is yours.
A router reads each job and decides where it runs. Most jobs never leave your building. The ones that need more go out cleanly scoped, and you set that boundary. Long jobs run in the background, so nobody sits watching a spinner.
02 · The router
Where each job goes
Reads the job, decides local or frontier, and enforces the boundary you set. Anything you have ruled out does not cross it, and you can see what went where.
03 · When it's needed
A frontier model
The rare hard case goes out, cleanly scoped, and comes back. Local economics with a frontier ceiling. If the connection drops, the local model keeps working and the rest queues.
In one sentence, without the jargon. Routing just means something decides which model answers. Here that decision is yours to configure, and most of the time the answer is the machine in your own server room.
02 · Why local
Four things a client can measure.
Match the one that matters to you. A CFO usually starts with cost. A security lead starts with data.
Pillar 01
Cost
Cloud AI bills grow with every prompt. A local model turns a variable, growing token bill into a fixed, owned asset. Predictable spend, no per-seat creep, no surprise invoice.
Pillar 02
Security
Routine work stays on your own infrastructure. Only cleanly scoped, non sensitive tasks are eligible to reach the frontier model, and you control that boundary, not us.
Pillar 03
Capability
This is the answer to "local models are weaker." The router sends everyday volume to the local model and the rare hard case to a frontier model. Local economics, frontier ceiling.
Pillar 04
Adoption
Most AI rollouts fail at the people, not the tech. Training isn't an add-on here, it's part of every install. You get a working system and a workforce that uses it.
03 · The install
What actually happens.
Four steps, in this order, on every engagement. William runs the technical side. Terah runs the training.
Step 01
Look at the work
We find where AI is already being used, what it's costing, and which of it can never leave the building. That map decides the shape of the system.
Step 02
Install the local model
The system goes onto infrastructure you own, inside your network, and starts handling daily work. Hardware needs depend on the work, so we scope that together rather than quoting a spec sheet at you.
Step 03
Set the boundary
You decide what may go out to a frontier model and what never can. The router enforces it, and heavy jobs run in the background so people aren't waiting on them.
Step 04
Train the company
Not a lunch and learn for the early adopters. Company-wide training, so the people who were never going to open a chat window use the system too. This is the step most rollouts skip.
L3M is an independent company. Training on every L3M engagement is delivered by Mentra AI, its training partner.
04 · The questions we actually get
Asked and answered.
The objections worth answering before you have to ask them.
Aren't local models weaker?
On the hardest problems, yes. That is why the system is hybrid. Your everyday volume runs locally, where local is plenty, and the rare hard case goes out to a frontier model and comes back.
We already pay for an AI assistant.
Then you already know what per-seat AI costs, and how much of your data leaves to get an answer. L3M isn't a seat licence, it's a system you own. Plenty of companies run both.
What if the internet goes down?
The local model keeps working. Jobs that need the frontier model queue and run when the connection is back, because the system is asynchronous by design. Nobody sits and waits.
Our last rollout died. Why is this different?
Because training isn't an upsell here. Terah runs the adoption side of every engagement, and the whole company gets trained, not just the volunteers.
What hardware do we need?
It depends on how much work you're moving in house, and how fast you want it back. We size it with you in the first conversation, before anyone talks about buying anything.
Who else is using this?
We're early, and we would rather show you measured results than logos. When we publish a case study it will be with the client's written permission, which is exactly how we would treat your name.
05 · Who installs it
You're moving AI inside your walls. Know who's doing it.
Two people lead every engagement, one on the build and one on the rollout.
William Heath
Chief AI Officer (CAIO), L3M
Leads the technical implementation on every engagement. Formerly Senior Software Engineer at MIT Lincoln Laboratory, and founder of the Laboratory's first Applied AI cohort, more than 200 members.
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Headshot and a longer bio still to come. The credential sentence above is written
to the brand guide guardrail and should not be reworded
without sign off.
Terah Bromley
CEO, L3M
Leads the company, and the training and adoption side of every engagement. More than 25 years of corporate training across more than ten countries. M.A. in Communication Studies, Arizona State University.
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Headshot and a longer bio still to come. No founder quotes appear anywhere on this
page, by design: a quote is a check first item.
06 · Talk to us
Start with the boring question.
What work would you move in house first, and what could never leave the building? Tell us that and we can tell you whether L3M is worth your time. One conversation, no deck required.
Prefer your own email client? hello@l3m.ai
Specs, availability and pricing are not published yet. They're a check first item under the brand guide's approval gates, so they'll land here once they're signed off.