What is a fractional AI department?

What the department consists of

Underneath, a fractional AI department is an AI operating system with an operator attached. Four parts do the work:

• A memory layer: one AI-readable record of the business's offers, tone, policies, and decisions, so no agent works from generic context. This is the second brain, and it's built first.
• Agents, one job each: a follow-up agent, a support agent, a reporting agent, each scoped narrowly enough to be measured and shut off independently.
• Approval gates: every outbound action starts as a draft a human approves; an action type earns autonomy only after a sustained low override rate.
• An operator: a human accountable for scoping, watching, and correcting the agents. This is the part the $297/mo tools don't sell.

Fractional AI department vs fractional CAIO

The names sound alike; the deliverables don't. A fractional Chief AI Officer advises: several hours a month of strategy, vendor evaluation, and a roadmap. Valuable if your problem is not knowing what to do. But most founder-run businesses know exactly what the repetitive work is; their problem is that nobody has built the thing that does it. A fractional AI department ships and operates. The roadmap is the first two weeks, not the deliverable.

Vs hiring in-house: the honest math

A competent US ops or automation hire runs roughly $90–120K a year in salary before benefits and tools. They work one timezone, take two to four months to ramp, and when they leave, the context leaves with them. An in-house AI engineer costs meaningfully more, and still needs someone to supply the operational context.

A fractional AI department is scoped to the work you actually want off your plate and priced in the readout, typically landing under the loaded cost of that hire, with coverage from the first month, no ramp on nights and weekends, and a memory layer that survives any staff change. But the comparison isn't one-sided, and pretending otherwise would be selling. Hiring wins when the work is judgment-heavy or relationship-heavy, when you need deep custom product engineering, when operations are complex enough to need a full-time owner (typically well north of $20M) or when what you actually need is a manager for humans. The longer version of this comparison, including where DIY tools fit, is at AI agency vs hiring.

What it costs, and what's included

Helix publishes its entry prices: the 72-hour diagnostic at $2,500, credited in full to the build, which prices the AI Second Brain and everything after it. The monthly department is scoped and priced in that readout, in writing, before the build starts, because the honest drivers vary: how many modules run, how many systems the agents touch, how complex the approval flows are, and how much of your knowledge already exists in writing. Whatever the number, the same things are always included: every action validated against its real side-effect, approval gates until an action type earns autonomy, measurement on your own metrics, and your data, dashboards, and runbooks staying yours. Details and what drives cost up or down are on the pricing page.

How the engagement runs

It starts by mapping the highest-ROI work and what it's worth from your own numbers, not from a benchmark deck. Then the memory layer, then agents module by module, each one running behind approval until it earns autonomy. Results are measured on your metrics, in your systems. And the exit is designed to be boring: month-to-month after month 6, everything documented, every token revocable by you in one click.