We find one expensive job your team keeps delaying, repeating, or routing through the founder. Then we install a custom AI employee to handle it inside Slack or Teams within 30 days.
Built in your accounts. Measured against real operating costs. Sensitive actions stay under human control.
Morning brief ready — 3 call notes assembled, 2 decisions queued, sources attached.
5 follow-ups drafted and ranked by signal. Nothing sends without your sign-off.
Brief approved — ship the Harbor follow-up ✓
Risk Scout is running the delivery audit…
We calculate what it costs today, connect the right systems, define the finished output, and make the work repeatable.
Problem: Replies sit in inboxes, Slack threads, and CRM notes until someone remembers.
What the AI employee does: Finds stale opportunities, checks the last conversation, drafts the next reply, and queues it for approval.
What you see: “3 leads need follow-up. 1 has buying intent. Draft ready. Source trail attached.”
Problem: Useful context is scattered across notes, Slack, docs, tickets, and founder memory.
What the AI employee does: Prepares the brief before the call with open risks, prior commitments, useful context, and suggested next moves.
What you see: “Call with Maya at 10:00. Renewal risk is procurement. Security owner missing. Suggested opener attached.”
Problem: Client problems surface late because nobody owns the small warning signs.
What the AI employee does: Scans handoffs, deadlines, open asks, and missing owners. Flags risk before the client asks.
What you see: “Atlanta pilot has no assigned owner for security answers. Due Friday. Assign owner or update client.”
Problem: The day starts with searching instead of deciding.
What the AI employee does: Pulls live context into one Slack brief: what moved, what is blocked, and what needs approval.
What you see: “5 items need attention. 2 drafts ready. 1 delivery risk. 1 claim needs proof before it can be used.”
Follow-ups recovered before they go cold. Delivery risks surfaced before the client asks. Call briefs delivered before the founder starts searching. No new dashboard to babysit.
Morning — what needs me before my 9:00? Pipeline and delivery first.
Three things are waiting on you. Receipts attached.
Approve drafts 1 and 2. Hold Atlas — I'll call them myself. Why was the Harbor proof missing?
Done — drafts 1–2 queued for send, Atlas held. Harbor: the proof-upload step was skipped at handoff (intake SOP, step 4). Flagged to the PM with the evidence and a suggested SOP edit. Receipt: 6 sources · full audit log
Flag: the Harbor handoff is Friday and the proof pack still isn't in the folder. Cause traced — the upload step was skipped at intake. Fix drafted, held for your call.
If the proof lands Thursday, do we still make Friday?
Yes — Thursday noon keeps the handoff green. The nudge is staged, and I added a blocking checklist item so this step can't slip again. Receipt: scope doc · intake SOP step 4
Two new wins came through overnight. Claims normalized against the source threads — both held at the compliance gate for sign-off.
Approve Vantage. For Northwind, use their exact wording from the email — no paraphrase.
Done. Vantage is live in the proof library. Northwind re-drafted verbatim and re-queued — the diff is highlighted for your next pass. Receipt: proof library + email thread
Prep for the Meridian call at 10:00. Three things worth knowing — sources attached, and the one claim I couldn't verify is marked, not buried.
Good catch on the Epic question — make it a direct ask, first item on the agenda.
Done — agenda item one, with the context paragraph attached. The brief is locked and synced to the call doc. Receipt: call doc · 5 sources
Weekly drift report. One pattern crossed the line: intake SOP step 4 — proof upload — skipped three times this week by two different people. That's structural, not a slip.
Is that the same step that bit us on Harbor last week?
Same step. I cross-linked this report to the Harbor flag in #delivery — one fix closes both. Receipt: drift report + Harbor flag
Escalation: second ping from Northwind in 24 hours — tone reads frustrated, and the response window closes at 2:00. Reply drafted from live order state, held for review.
Soften the second line — we own the miss. Then send it today.
Softened and re-queued. If their reply reads negative, this thread escalates straight to you with the history attached. Receipt: helpdesk + tone check
Illustrative example using sample data. The installed system uses your tools, names, permissions, and approval rules.
Sales history lives in the CRM. Client context is buried in Slack. The latest process is in a document nobody remembers updating. Important decisions sit in meeting recordings and someone's head.
That makes every question expensive. A report needs three people. A client call starts with twenty minutes of searching. A missed follow-up stays missed until the deal is cold.
The founder or technical lead becomes the human API for the entire company. We give your team one place to ask, act, and get the supporting records back.
The custom AI employee works inside Slack or Teams, uses only approved systems, follows your real standards, and stops for a human before any sensitive action.
Reports, briefs, follow-up drafts, research, task updates, QA checks, and internal tools. Not another list of suggestions.
One request can pull approved context from Slack, Drive, your CRM, inbox, calendar, project tools, and billing systems.
Daily briefs, weekly reports, pipeline checks, risk scans, and recurring research arrive on schedule.
Your edits become reusable rules instead of disappearing when the chat closes.
Public messages, money, production changes, and sensitive work wait for the named human owner.
The system runs in accounts you control. The knowledge, instructions, integrations, and operating guides stay when a vendor, model, or consultant changes.
Claude Code, Codex, and Copilot are excellent workers. We give them your company context, a real job, approved access, human controls, memory, and somewhere to work with the rest of your team.
You prompt it, supervise the session, review the output, and decide how to deploy it. When the task ends, the business process is still yours to operate.
Applied Leverage
The system reads the approved company context, coordinates the right coding tools, runs the tests, prepares the handoff, and posts the finished work in Slack with the evidence attached.
You are not choosing us instead of the best models. You are getting the operating layer that can use them without rebuilding your company around whichever vendor is winning this month.
We calculate what the job costs today, install the custom AI employee that can perform it, run it on real work under human review, and measure the saving before you accept the build.
We document the selected job, who performs it, how often it happens, how long it takes, and what that work currently costs.
We install one custom AI employee inside the tools where the job already happens. No template pack, prompt library, or new dashboard to babysit.
The AI employee runs alongside your team first. We measure completed work, hours removed, corrections required, and annualized savings against the signed baseline.
We train the human owner, hand over the operating guides, and transfer the system into accounts you control. Then we rank the next highest-value job.
Useful information was spread across Slack, dashboards, coaching calls, documents, and internal tools. We connected the approved sources and installed an AI employee in Slack that can prepare briefs, find company wins, answer internal questions, build tools, and coordinate technical work.
One private dashboard for revenue, students, coaches, proof, and Slack activity — built around exceptions: what broke, what changed, who owns it.
A prioritized queue that tells coaches who needs action now, why, and the evidence — risk-scored across engagement, goals, progress, and touchpoints.
The Slack archive, SOPs, and the coaching-call corpus made searchable. Agents pull focused context; a nightly sync keeps it fresh.
Client wins move from scattered Slack messages through a compliance gate into approved proof. No claim inflation, no invented results.
Live integration credentials stay out of prompts and repos — loaded through a locked env drop-in, not pasted keys.
Durable context on people, systems, ownership, and dependencies — operator knowledge that compounds instead of evaporating.
You are buying one recurring job removed. Underneath it sits the permission, evidence, approval, and ownership layer that keeps the work reliable.
Every source and permission is agreed before the AI employee can read or write anything.
Customer-facing messages, money, production changes, and high-risk work wait for named approval.
Sources, exceptions, approvals, and changes stay visible instead of disappearing into a chat transcript.
Your company owns the context, integrations, instructions, runbook, and operating history after handoff.
Before we build, we document who performs the selected work, how long it takes, how often it happens, and what that time costs. We then measure the installed system against that baseline. If it does not produce at least $50,000 in verified annualized savings, you do not pay the balance.
If the verified annualized saving is below $50,000, you do not pay the balance. Theoretical opportunity does not count. The installed system has to produce the saving on real work.
Start with a recurring job your team understands, real examples, and one person who can review the work during the installation.
Recognize the right column? Find your $50K bottleneck and pressure-test the first job. You leave knowing whether the work is ready for a custom AI employee.
Bring the recurring work your team keeps delaying, repeating, or routing through the same overloaded person. We will calculate its current cost, identify the first job an AI employee should take over, and show you what should be built first.
Lucas reviews every application. If the job is ready, you get the mapping-call link. If the process needs cleanup first, you get the direct no and the reason.
Still have a question about the AI employees, the install, or how the work gets done?
ChatGPT waits for prompts. Our AI employees have job ownership, company memory, source rules, review paths, and recurring responsibilities. The output is completed work, not chat history.
We price one recurring job, name the inputs and finished output, identify approval gates, and decide whether it is ready for a custom AI employee.
No. We build, integrate, harden, and calibrate the system. You bring business context and access to the tools that matter. We work with your stack, not against it.
Yes. That is why the first phase runs in shadow mode with human review. The point is controlled leverage, not unsupervised chaos.
No. The operating layer runs on your infrastructure and stays portable across providers. You own the operators, the data path, and the receipts.
One working custom AI employee and one live workflow in 30 days: mapped work, a signed role brief, connected sources, a review queue, receipts on every output, and the runbook. Delivered means every acceptance criterion is checked off.
On the mapping call, we pick one recurring workflow, name the systems involved, assign the human review owner, and write the acceptance criteria before anything gets built. The day-30 balance is tied to those criteria being checked off.
The operating layer runs in accounts you control, with source trails, logs, and review gates visible to your team.
Stage 02 of the blueprint maps exactly what each operator may read and write, field by field. PII gets redaction rules, credentials live in a vault and never in prompts, every call is logged, and the whole system runs in accounts you control. You can revoke access — or hit the kill switch — at any time.
Hire one anyway when you're ready — they're different tools. A hire scales by hours and leaves with the playbook. The fleet turns recurring work into reviewed drafts around the clock, keeps its memory in your repo, and gives your people back the work that actually needs human judgment.
Week one: a few hours of interviews plus access setup. After that, a one-hour review at each gate, and about fifteen minutes a day clearing the queue during shadow week.
Your team runs it. The runbook, training, monitoring board, and 90-day roadmap are part of the install. If you want us to build the next AI employees on the same foundation, we scope that after the first workflow is working.
We find one expensive recurring job, install the custom AI employee to handle it, and prove the saving against your real operating costs.
