Forward Deployed Agent · Built for MSPs
our forward deployed agent turns
invisible work into AI teammates
01
observe
[fda] watches, quantifies
02
diagnose
assess insights; rank by hours back and business impact
03
plan
[fda] specialist agents queue the implementation plan
04
execute
press go, deploy AI 19.5x faster
Free on every endpoint · Metadata only, never your content · MDM or RMM push
v0.2.1 · 3.5 MB Windows 10+ · 10.7 MB macOS 13+
We put fda on 4 of our own machines.
Nobody changed how they worked. Three weeks later, here is what it found.
The same things, over and over
Add it up
168 hrs / month
One job's worth of busywork, wasted. Every week.
How the forward deployed agent works
01
Install the agent
Pushed to every tech in an afternoon, under 1% CPU. Nobody changes how they work.
02
Work normally, two weeks
It learns the real job — RMM, PSA, documentation, and every switch between them.
03
Get your report
Where your techs' hours actually go, and what a teammate could take over. Free, yours to keep.
04 — We build it with you
Side by side with your team
We build it with your team, not for them — sitting alongside the techs whose work it learned. It ships after the two-week scoping period, trained on your stack and your SOPs, not a generic model.
What it takes over
Trained on 530 hours of your techs' actual work
fda learns the work. Your AI teammate does it.
— 02 / Learn · Map · Take over
Remove monotony from the ticket queue.
01 — Learn
Install once. Forget it exists.
Deploy across every tech in an afternoon. The agent runs under 1% CPU, changes no workflows, and nobody has to remember to start a timer. It learns; your techs work.
Capture stream · local buffer
02 — Map
fda finds what no one would report.
The same ticket re-keyed from RMM into PSA. Client data retyped into documentation. Patterns spread across weeks and thousands of actions — invisible from inside the queue.
03 — Take over
Every piece of monotony becomes work your AI teammate can own.
What it takes over, what that gives back, how long it takes to build — ranked by value. A hiring plan for software, not another dashboard.
— 03 / The finding
Our internal case study.
4 people, 3 weeks, 530 hours. Nothing redacted, nothing illustrative. Click through all 5 pages.
We measured ourselves first.
It was worse than we thought.
We put our forward deployed agent on 4 of our own machines — recruiting, client success and the founder's desk — and recorded 3 weeks of real work. Every application, every browser tab, every copy and paste. No surveys.
168 hrs / mo
= one full-time employee of repetitive work, spread across 4 people. You already employ the 5th person. You just can't see them.
~2,300
copy-pastes into AI tools across the team — context shuttled by hand between Slack, Gmail, the ATS and the models
~25,500
app switches measured — one every 75 seconds of work
2,400+
AI sessions already running — every one of them fed by hand
attain internal case study — july 2026
This is the same report you get after your two weeks.
— 04 / Scale
More techs = better AI teammate.
Every tech you add sharpens the picture. What looks like one person's workaround turns out to be how the whole shop runs.
One tech
A habit
How one tech works. Easy to dismiss as personal preference.
One team
A pattern
The same workaround on 4 techs is not a quirk. It is a process nobody designed.
The whole shop
A system
fda learns how the shop actually runs.
Free on every machine, so don't be shy.
Unlimited seats · Deploys in an afternoon
— 05 / Deploy across the fleet
Built for hundreds of endpoints, not one laptop.
- Push via MDM or RMM — Intune, Jamf, NinjaOne, Datto RMM
- Silent enterprise install with an enrollment token
- Per-machine or per-team grouping
- Central console for enrollment, policy, and export
Silent install is for org-managed machines under an announced policy. On a personal machine, install is always visible and consented.
15 minutes with Chris — enrollment, policy, and what your techs will see.
2 weeks of work.
1 new teammate.
Free on every endpoint · Metadata only, never your content