By Sagar Shankaran, Founder of CallSphere
Payouts, oversells, returns aging and chargeback deadlines, prepared between 9pm and 6am. What a DTC ops manager reviews instead of rebuilds each morning.
Key takeaways
You tried this in 2024. Somebody on the team built a chain of Zapier steps that pulled the Shopify payout into a Google Sheet, and it worked for eleven weeks until Amazon changed a column heading in the settlement report and the whole thing silently produced a blank tab every morning for a month. You went back to doing it by hand, and you were right to.
What is different in 2026 is not that the connections got sturdier. It is that the work now happens the way a person would do it — reading the screen, noticing that the column moved, and writing down that it moved — for hours at a stretch, unattended, between the time you close the laptop and the time your head of operations opens hers.
Your head of operations sits down with coffee and opens, in this order: Shopify admin, Amazon Seller Central, the 3PL portal, Stripe, the bank, QuickBooks Online, and the Loop Returns queue. She is not analysing anything. She is answering six questions that have to be answered before anyone can do real work.
Did yesterday's payout hit the bank and does it tie to yesterday's orders. Which orders are sitting unshipped past their promise. Which SKUs went negative overnight because Amazon and Shopify disagree about the same physical pallet. Which returns have been sitting in the inspection queue for more than five days. Which disputes came in and how many days are left to respond. And which of last night's flows failed to send.
That is 70 to 90 minutes every weekday, done by the highest-paid operator in the building, and by the time she finishes it is 9:40 and the day's real decisions have not started. The workaround everyone pretends is fine: she does the first three questions properly and skims the last three. Disputes and returns aging are what get skimmed, and those are the two that cost money.
Long-running autonomous agents are the development, and the phrase that matters to an owner is long-running. A machine that answers a question in four seconds is a search box. A machine that can be handed a goal at 9 p.m. and still be working on it at 5 a.m. — opening systems, pulling reports, comparing, retrying when something is slow, writing down what it could not resolve — is a night shift.
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An overnight agent run is a standing instruction, given once, that is carried out unattended between close of business and open, and that returns not a report but a queue: the things it finished, the things it prepared for you to approve, and the things it could not settle and is escalating. ChatGPT Work, launched 9 July 2026, is built around exactly this shape — hand it a goal, it works across your apps and files for hours and returns finished output. Claude Cowork, which launched in January and reached web and mobile in July, aims the same idea squarely at non-technical staff, which is the relevant audience here: your head of operations, not a developer.
The 2024 version broke because it was a fixed set of steps with no judgment. The 2026 version reads the settlement report the way she does, and when the column heading changes it says so in the morning note instead of producing a blank tab.
flowchart TD
A["9:15pm - overnight run starts"] --> B["Money lane: payouts, fees, bank, QuickBooks"]
A --> C["Stock lane: Shopify vs 3PL vs FBA counts"]
A --> D["Promise lane: unshipped orders, returns aging"]
A --> E["Dispute lane: new chargebacks and days remaining"]
B --> F["6:30am reviewed queue"]
C --> F
D --> F
E --> F
F --> G["Head of ops approves, edits or rejects each item"]
Nothing below requires new software — only read access to what your team already logs into.
Money. It pulls the Shopify Payments payout detail, matches it line by line against the orders it covers, separates out refunds, chargebacks and processing fees, pulls the Amazon settlement for the same period, checks both against the bank deposits, and drafts the journal entries in QuickBooks Online as a batch waiting for approval. Anything that does not tie by more than a stated tolerance goes in the exception list with the order numbers attached.
Stock. It compares on-hand counts across Shopify, the 3PL's system and FBA for every active SKU, flags each disagreement with direction and size, and flags any SKU that went negative — the oversell that becomes a cancellation email and a one-star review three days from now.
Promises. It lists orders past their stated ship-by, grouped by cause: awaiting stock, held for address verification, stuck at the 3PL, flagged by fraud screening. Then the returns inspection queue by age, with the ones about to breach your posted refund window at the top.
Disputes. This is the money line. For every new chargeback it identifies the reason code, calculates days remaining in the response window, and assembles the evidence packet — the delivery scan, the address verification result, the customer's prior order history, the tracking, the terms they accepted at checkout — into a draft response. Your head of operations reads it, corrects it, and submits. She does not go hunting for a delivery scan at 8:40 a.m.
Reconciliation hours are the visible benefit and the smaller one. Here is the illustrative arithmetic for a brand doing 4,800 orders a month at $92 average order value.
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| Disputes | Today | With overnight prep |
| Dispute rate | 0.45% | 0.45% |
| Disputes per month | 22 | 22 |
| Responded to inside the window | 9 (41%) | 22 (100%) |
| Win rate on those responded to | 24% | 38% (fuller evidence) |
| Disputes recovered per month | 2.2 | 8.4 |
| Value recovered at $92 | $202 | $773 |
| Ops time on disputes | 45 min × 9 = 6.8 hrs | 10 min × 22 = 3.7 hrs |
That is roughly $571 a month, or $6,850 a year, in money that was already yours and was being written off because nobody got to it in time. Add the morning routine: 75 minutes a day down to about 20 minutes of reviewing a prepared queue, which is 55 minutes × 21 working days = 19 hours a month back from a person costing you around $52 an hour loaded — call it $12,000 a year. The two together fund the tooling several times over, and the second number is the one your head of operations will feel.
How you prove it: log the date and time every dispute was responded to for the three months before and after. That single column is unarguable, and it does not depend on anyone's opinion about AI.
An overnight run should prepare work, not commit it. Draw the line hard, in writing, before the first night.
The honest limit underneath all four: the agent is good at "these numbers do not agree" and bad at "here is what we should therefore do about the Portland account." Judgment about relationships, disputes with your 3PL, and anything a customer will read stays with people. Keep the finance reviewer too — a controller who signs off on the batch of journal entries is not an optional step just because the entries arrived pre-drafted.
No, and do not. Use read-only access wherever it exists, and feed banking through the connection QuickBooks already has rather than handing over credentials. The run needs to see transactions; it does not need the ability to move them. Where a system has no read-only role, create a separate limited user so you can see exactly what it touched and switch it off in one click.
You want the failure loud and harmless. Set the rule that anything it cannot resolve goes in the escalation list with the raw numbers attached, and that a run which cannot finish leaves a note saying where it stopped. The first month, have your head of operations do her old 8:15 routine as well — if the queue caught everything she caught for four straight weeks, retire the manual pass.
Not for reconciliation. At that volume the founder does the whole check in fifteen minutes. It becomes worth it when the count of systems that have to agree goes past three — the day you add Amazon, or a second 3PL location, or wholesale orders through Faire — because the work scales with the number of disagreeing systems, not with order count.
Both ChatGPT Work and Claude Cowork accept a written goal and work across your apps and files unattended, and either will do this job. Pick on two grounds: which one already connects to the systems you use most, and which one your head of operations finds easier to correct when it gets something wrong. She lives with it at 6:30 a.m., so let her choose after a two-week trial on reconciliation alone.
One thing the overnight queue will surface within a fortnight: how many of yesterday's exceptions started as a phone call or a chat that nobody answered — the customer who called at 7 p.m. to change an address before the parcel shipped, the wholesale buyer who left a voicemail about a short shipment. CallSphere builds AI voice and chat agents that answer the line and web chat around the clock, capture what the caller needs and book the follow-up, so those turn into a logged item on the morning queue instead of a $92 dispute you find out about three weeks later.
Written by
Sagar Shankaran· Founder, CallSphere
LinkedInSagar Shankaran is the founder of CallSphere, where he builds production AI voice and chat agents deployed across healthcare, hospitality, real estate, and home services. He writes about agentic AI, LLM engineering, and shipping voice agents that handle real calls in production.
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