The weekly briefing was assembled by hand from a wall of noise.
The firm sells a weekly briefing that tells C-level subscribers what actually matters in their sector, and how urgently to act on it. Producing it was an all-week chore: analysts scanned feeds, newsletters, and videos by hand, filed the same story over and over as it surfaced in different places, and raced a Friday deadline to write the summary from memory.
- A few hours a day of scanning feeds and newsletters, every day
- The same announcement re-filed in five sources before anyone noticed
- Urgency decided by whichever analyst happened to be reading that week
- Weekly summary written from memory against a Friday deadline
- Sources ingested automatically, deduplicated, and clustered before a person sees them
- Urgency scored structurally: act, track, scan, or ignore, on one radar
- Briefing and narrated audio drafted in days, held to a review gate before publish
- The analysts steer and approve instead of starting from a blank page
What it returns.
Two analysts spent roughly twenty hours a week keeping subscribers current, about $5.2k a month of paid time. It now runs on a Medium lease, $2.5k a month flat, and the analysts spend about four hours a week reviewing instead of twenty producing.
cost figures estimated from the client's disclosed analyst hours; review time at 20% of the manual hours; the twice-the-volume figure is a projection. model usage runs on the client's own AI account
The firm ran itself on email threads and good memory.
Every client call left a transcript nobody re-read. Deals, contacts, and decisions sat in a generic CRM that someone updated by hand, when there was time. The question "what is stuck in proposal?" cost an afternoon of reconstruction, and the answer was out of date the next day.
- Records split across inbox, call transcripts, a generic CRM, and a notebook
- Pipeline and next actions updated by hand, weekly at best
- A pipeline question cost an afternoon of reconstructing
- Dashboards were snapshots: accurate when made, stale the next morning
- Every call, email, and signal lands on the right account and opportunity, automatically
- Golden records for accounts, contacts, and deals stay in sync by rule, so nobody has to remember to update them
- Any pipeline question answers in one line; the weekly review assembles on demand
- A live board flashes every change the moment it happens
What it returns.
Keeping the records and pipeline current ate roughly fifteen hours a week of principal time, around $5.5k a month loaded. It now runs on a Medium lease, $2.5k a month flat, with about three hours a week of review.
cost figures estimated from the client's disclosed admin time; review time at 20% of the manual hours; the twice-the-clients figure is a projection. model usage runs on the client's own AI account
Fourteen locations reconciled to a ledger by hand.
Weekly inventory reconciliation between the Shopify point of sale and QuickBooks, done by hand: six people, five hours each, every week. Roughly thirty hours of manual ledger work each week, plus whatever shrinkage went unnoticed between reconciliations.
- Six people, five hours each, every week: ~30 manual hours reconciling two systems
- Supplier invoices matched to purchases by eye
- Shrinkage surfaced weeks later, if at all
- Reconciliation ate a whole shift every week
- The autopilot maps SKUs across both systems and reconciles every night
- Supplier invoices matched semantically, with deterministic math on every flag
- Human review queue: no automatic adjustment, every discrepancy seen by a person
- Review is an hour a day, and shrinkage is caught weekly
What it returns.
This client was paying roughly $7.9k a month for the manual process: 30 hours a week at ~$30/hr, about $3.9k, plus roughly $4k of shrinkage and errors that went unnoticed. It now runs on a Large lease, $4.0k a month flat, with about an hour a day of review.
cost figures estimated from the client's stated hours; review time at 20% of the manual hours; the twice-the-locations figure is a projection. model usage runs on the client's own AI account