01
Content repurposing
An essay or video becomes channel-native drafts for Substack Notes, X, LinkedIn, and clips. Every asset lands in a review queue before anything goes public.
AI operations for high-output teams
You do the thinking. The system handles the rest.
I build an AI operations layer that runs your content, research, and intelligence pipeline unattended. It turns the work you already do into scheduled deliverables—without handing authorship or final approval to the machine.
Managed systems · $5–15K/month · Built and operated by Griffin Long
The operating problem
Your best work should not create a second job.
Writing the essay, recording the video, or making the research call is the high-value part. Then comes the operational wake: reformatting it for every channel, monitoring sources, assembling briefs, checking claims, reading analytics, and making sure the whole machine ran.
Most people solve that with more tabs, a loose pile of prompts, or another person to coordinate. I build the layer underneath it: a system that knows the workflow, runs it on schedule, and brings you the decisions that still require judgment.
Not a prompt pack
The workflow is encoded, scheduled, tested, and observable.
Not staff augmentation
You are buying a running capability, not a block of developer hours.
Not autopublish
Public output lands in a review queue. You keep the final word.
What the system runs
One operating layer. Six recurring jobs off your plate.
Each build is scoped around the work already surrounding your ideas. The modules change by engagement; the operating pattern stays the same.
02
Research to brief
The system ingests the sources you trust, clusters them by theme, removes duplicates, and drafts a morning brief around the signals worth your attention.
03
Fabrication control
A deterministic guard checks generated copy against approved source material. If the draft introduces a fact, number, or named entity the source does not support, the output is rejected.
04
Scheduled operations
Cron jobs run the pipeline every day without waiting for someone to open a laptop, paste context into a chat, or remember the next step.
05
Telegram supervision
Check status, approve work, trigger a run, or investigate a failure from your phone. The system works unattended without becoming invisible.
06
Subscriber intelligence
Publication and audience data becomes a recurring operating memo: what is growing, what converts, where attention compounds, and what deserves a closer look.
The operating loop
Unattended does not mean uncontrolled.
A useful system knows when to run, what evidence it can use, where it must stop, and how to report what happened.
Scheduled
The workflow starts without you.
A cron job or source event starts the run on the agreed cadence. No manual prompt is required.
Grounded
It works from approved inputs.
The system fetches the essay, transcript, feed, report, or dataset and records the source material for the run.
Produced
It creates the deliverables.
Specialized agents transform the source into the formats the workflow requires, with channel and audience rules already built in.
Checked
Guards reject unsupported output.
Deterministic checks catch introduced claims and malformed deliverables before they can enter the review queue.
Supervised
You get the decision, not the busywork.
The finished work, failure report, or approval request reaches you with a readable trace of what ran and why.
Running in production
One person publishes like a team.
An anonymized look at the AI operations layer behind a high-output finance publication.
- subscribers
- 22K
- published each week
- 5×
- author and operator
- 1
- unattended cron jobs
- 5
22K subscribers, 5×/week, one person. Five cron jobs run unattended. A fabrication guard blocks anything the author didn't actually say. He writes. Everything else handles itself.
The system repurposes each essay, monitors recurring workflows, prepares research and analytics, and reports through Telegram. Anything public waits for review. The author still owns the thesis, the judgment, and the final line.
That is the product: more operating capacity without turning the publication into a content factory.
Built for trust
Autonomy where it is safe. Approval where it matters.
01
Sources before generation
Every run begins with named inputs. The system records what it read so the output can be checked against the material that produced it.
02
Deterministic fabrication guards
The model does not grade its own honesty. Code checks facts, numbers, and named entities against the approved source and fails the deliverable closed.
03
Human approval for public work
Drafting can run unattended. Publishing cannot. Anything carrying your name waits in a review queue until you approve it.
04
A trace for every run
Successes, failures, inputs, and actions are recorded. When a source or integration changes, there is evidence to inspect instead of a mystery to debug.
Who this is for
High output. Small team. Too much operational wake.
Newsletter authors
You have 10K+ subscribers, publish three or more times a week, and the work around each issue is consuming the time you need to think.
YouTube creators
The video is the source of truth. You want each episode to become useful written distribution without rebuilding it by hand for every channel.
Research analysts
Your morning starts with the same feeds, reports, and filtering work. You need a grounded brief before the real analysis begins.
Solo founders
You run the company and the content engine. The recurring coordination work has become a tax on both.
Family offices
Your team filters news, reports, and deal flow across too many sources. You need signal organized around your mandate, not another generic feed.
The common denominator is not industry. It is a valuable point of view trapped inside a workflow that repeats often enough to become infrastructure.
The engagement
A running system, not a block of hours.
$5–15K/month
The monthly rate depends on the number of workflows, source systems, delivery channels, and safeguards the operation requires. You are paying for a maintained AI operations layer that produces agreed deliverables on schedule—not for time sheets or prompt sessions.
Book a callBest fit: a recurring workflow with clear inputs, a concrete deliverable, and enough weekly volume to justify infrastructure.
01
Map the operating loop
We identify the recurring work, the source of truth, the deliverable, the schedule, and every point where a human decision must remain.
02
Build the first live workflow
I connect the real inputs and tools, encode the checks, and get one complete workflow producing reviewable output before expanding the system.
03
Run, observe, and compound
The system operates on schedule. I maintain the workflows, investigate failures, and add the next highest-leverage loop as the operation proves itself.
Why trust the build
I build the runtime and the operation.
I built Agent AFK, an open-source runtime for agents that keep working after you close the laptop. It is distributed on npm and handles roughly 35K downloads a month.
The same production concerns show up in every client system: scheduling, tool access, durable state, approval gates, readable traces, failure recovery, and a clean way to supervise the work from anywhere.
This service is that engineering applied to your operation. Not a demo. Not a prompt library. A system your business can depend on next week, and improve over time.
Questions
Straight answers.
What exactly am I buying?
A maintained AI operations layer built around a recurring part of your business. It connects to your real sources and tools, runs on an agreed schedule, produces defined deliverables, and stops for approval where judgment or public action is required. This is not hourly consulting or a folder of prompts.
Does the system publish content automatically?
No. It can ingest, draft, format, check, and route content without supervision, but anything public or carrying your name lands in a review queue. You approve the final output before it is published.
How does the fabrication guard work?
The generated draft is checked against approved source material. Deterministic code extracts facts, numbers, and named entities and rejects output that the source does not support. The model is not asked to grade its own work.
What can I control from Telegram?
The exact controls depend on the workflow, but the operating pattern includes run status, completion and failure notifications, approval requests, manual triggers, and a path to inspect what happened without opening a laptop.
How long does it take to get the first workflow running?
The first goal is one complete workflow against real inputs—not a strategy deck. Timing depends on the systems involved, but the engagement is structured to get a live, reviewable loop running first and expand from evidence after that.
Why is this priced monthly?
Because the deliverable is a running operation. Sources change, integrations fail, workflows evolve, and the next useful loop becomes obvious only after the first one is live. The monthly rate covers the system, its scheduled output, maintenance, supervision, and continued improvement.
What does it cost?
Most engagements are $5–15K per month depending on the number of workflows, integrations, delivery channels, and safeguards required. Book a call with the recurring work you want off your plate, and I will tell you whether the economics make sense.
Who is not a fit?
This is probably too early if you are still deciding what to publish, have no repeatable source material, or only need a one-off automation. It works best when the judgment is valuable, the workflow repeats every week, and the operational load is already visible.
Get started
Show me the work that keeps coming back.
Bring the recurring content, research, or intelligence workflow that is consuming your week. I'll tell you what should run unattended, where approval should stay human, and whether it is worth building.