Audit
Find the operational leak.
A paid diagnosis. We review your workflows and tool stack, find where time leaks, and report what should move first.
Managed AI agents
Custom AI agents for
founder-run businesses.
We design and build the agent, wire it into the systems you already use, test it against your own cases, and keep running it after launch. You approve outcomes. We own the machinery.
Definition
What is a managed AI operator?
A Phronimos operator is a custom AI agent built for one job inside your business: connected to your systems, bounded by explicit guardrails, and monitored by us. Not a subscription you configure and forget. A working system somebody is accountable for.
Most of the market calls these AI agents. We call ours operators because the agent is only half of what you buy. The other half is the monitoring, versioning, and escalation that keeps it working in month six, when the tool it depends on changes and nobody on your team notices.
Your stack is full. The work is still manual.
A CRM, a project tracker, a shared inbox, a meeting recorder, two automation platforms, and an AI subscription someone bought after a conference. And the owner still clears email on Sunday night.
Software waits to be operated. The workflows that hurt are the ones nobody has time to operate. Phronimos gives those workflows an owner that does not get busy.
How a Phronimos engagement works
Audit first. Build second. Manage ongoing.
Sprint
Build the first operator.
One painful workflow built, tested with live examples, and handed off with guardrails and a clear SOP.
Manage
Keep the system running.
Monitoring, maintenance, escalation, and new rollouts under one retainer. The layer keeps earning its keep.
The managed layer
What does a managed AI agent service include?
Every operator Phronimos builds is monitored, versioned, and covered by a service standard. When something breaks, we find out first, contain it, and tell you what happened with the trace attached.
Monitoring · live
You hear it from us first.
Failure alerts route to us, never to your team. Recovery paths run automatically where they can, and escalation goes to a named person rather than a ticket queue.
Under 48h
New request turnaround
Target on retainer work. Incidents are handled on their own clock, not this one.
Versioned, and reversible.
Prompts, models, and guardrails are versioned. Every change is testable, and every action an operator takes leaves a trace you can read.
One controlled path.
The audit finds the leak. The sprint proves the fix. The retainer keeps the operator layer running, and growing, without your attention.
Which workflows should become AI agents first?
The first build is almost always a workflow every owner-led team drowns in: high volume, low judgment, easy to measure. The retainer is where operators get specific to your business.
The machinery stays with us.
You never choose a model, manage a token budget, or debug an integration. Phronimos owns the runtime, the auth layer, the monitoring, and the version history for everything it builds.
Where most teams start
Inbox triage
Priority surfaced, noise filed, drafts staged for review.
Meeting follow-up
Summaries, next steps, and owners captured every time.
Lead intake and routing
Every inquiry qualified and put in front of the right person.
Document chase-ups
The polite persistence nobody on your team has time for.
Repeated internal questions
Answers pulled from your own knowledge, on demand.
Open-loop tracking
Commitments tracked until they close.
Where a custom operator earns its keep
Matter intake and conflict screening
Inbound inquiry parsed, conflicts checked against the practice-management system, engagement letter drafted, and non-viable matters declined with the reason on file.
Renewal chase across a book of business
Policy data reconciled from the agency management system, exposure changes flagged, and client outreach sequenced and escalated by expiry date.
Candidate screening against a live requisition
Applicants scored against the actual scorecard, notes written back to the ATS, and borderline cases escalated with the reasoning attached.
Who is a managed AI operator for?
You lead a team of 5 to 50 and operations still route through you.
Repeated admin and follow-up work eats hours every week.
The SaaS stack is full, yet the work is still manual.
You want someone accountable for the system after it goes live.
Common fits: agencies, law firms, insurance teams, real estate teams, recruiting firms, and service businesses with an office to run.
What does it cost?
An Agent Reliability Review costs $999. An Agent Implementation Sprint starts at $3,500, depending on integration complexity. The Fractional AI Officer retainer is $5,000 per month. Scope changes are re-priced in writing before work continues.
The review fee is credited in full toward an Agent Implementation Sprint or the first month of a Fractional AI Officer retainer.
$999Agent Reliability ReviewRecorded discovery, workflow and tool-stack review, findings report, and a scoped first sprint.From $3,500Agent Implementation SprintOne workflow built into a production agent, tested on live examples, handed over with guardrails and an SOP.$5,000 / monthFractional AI Officer retainerMonitoring, maintenance, new rollout, and a queue you can see into.
Start with the audit.
If work keeps piling up around you, the first move is finding which of it should become an operator, and getting that one live.
Before you book: what the audit includes ·how we handle access to your systems