How Automoat works.
Three movements behind every engagement — sweep, install, retainer. Five layers in every install. Same shape across every build; the depth scales with what you hire us to do.
A unit. Not a deck-and-handoff.
Most firms send strategists to scope, then hand the build to an offshore team you've never met and a junior PM who's on three other accounts. We don't do that. The same operators who scope the work ship the work — across the Sweep, the Install, and the Retainer.
That's what makes a named outcome credible. There's no one to hand off to. The names on the SOW are the names on the dashboards.
An elite operator unit that sweeps your operation end-to-end, builds the AI layer, deploys it, and stays on retainer to run it. Same people, every phase.
Sweep. Install. Run on retainer.
The Sweep
An end-to-end audit of the operation. AI spend reconciliation. A 90-day install plan with the ROI math behind every workflow. You keep the deliverable whether you hire us or not.
- →Operations map
- →AI spend reconciliation
- →Workflow-by-workflow ROI model
- →AI readiness audit
- →90-day install plan
The Install
The custom AI layer built around your workflows. Deployed into your operation. Instrumented so you can see what it's lifting and where.
- →Models picked per workflow
- →Firm knowledge embedded
- →Agents around named workflows
- →Workflows rebuilt around the agents
- →Dashboards instrumented
- →Team training
The Retainer
The build, running and tuned. Monthly KPI review. Quarterly business review. New builds added as the operation evolves.
- →Monthly KPI review
- →Quarterly business review
- →Vendor management
- →Governance kept current
- →Incremental builds
What an AI-native operating system actually is.
Every install ships all five layers. Skipping any of them is the difference between AI that lifts the P&L and AI that sits in a license agreement.
What: Frontier and self-hosted models, picked per workflow against the operation's economics.
Why: The compute substrate. Wrong choice here and everything above it suffers — cost, latency, accuracy, risk.
What: Proprietary knowledge embedded into a vector store, with retrieval tuned to your domain and your data.
Why: Without your knowledge in retrieval, the model is a generic chatbot writing in your firm's name.
What: Autonomous routines that execute named workflows end-to-end, with eval harnesses and human checkpoints.
Why: Without agents, the model talks. With agents, the model works.
What: The business processes themselves, rebuilt around the agent layer — not bolted onto the side of it.
Why: Layers 1–3 don't matter if the workflow underneath is broken. The workflow is the work.
What: Instrumentation on every workflow. Audit-grade lineage on every model call. KPI dashboards the board can read. Governance signed by IT and legal.
Why: Productivity that isn't measured isn't productivity. Compliance that isn't logged isn't compliance.
Human-directed. Measured. Owner-optional.
Human-directed
Humans set the standard. Humans review the work. The AI executes inside the lines you've drawn — never outside them.
Measured
Every workflow has a KPI. Every KPI has a baseline and a target. If we can't measure the lift, we don't claim it.
Owner-optional
The operation runs without the owner in the room. Owner stays in charge — by choice, not because the system can't run without them.
The work we've chosen not to do.
A roadmap is a starting condition for an Install, not the work itself. The Sweep ends with a plan; the engagement doesn't end there.
If the workflow underneath is broken, we rebuild it. A chatbot on top of a broken workflow is a broken workflow with a chatbot.
One operation gets fully shipped before we start the next conversation. The tiger team is yours during the engagement.
We make them faster on the work that matters and free them from the work that doesn't. The relationships and the judgment stay with them.
Book an Ops Call.
30 minutes. Operator-to-operator. No deck. No follow-up nurture sequence designed to wear you down.
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