What the reporting job used to cost
Sunday evening or Monday morning, depending on how disciplined I was being that week. Open Triple Whale, pull the week. Open Klaviyo, pull flow and campaign revenue. Open Meta, sort the ad sets, work out which creatives were actually new this week and which were carryover. Open Shopify for the order and AOV numbers. Then start typing all of it into a deck that already existed, because the structure never changes, only the numbers inside it.
The published figures put manual client reporting at 5 to 10 hours per account per month, with 2 to 3 of those hours going purely on pulling platform data into a deck. On a weekly cadence it is worse. And the cruel part is that by the time the deck is finished, the numbers in the first section are two days older than the numbers in the last. You are presenting a picture that was never true at any single moment.
Reporting is the highest-volume, lowest-judgement work in a growth operation. It is also the work that gets done last, which is why the decisions it should be informing get made on memory instead.
What the agent actually does
It runs on a schedule, unattended, and rebuilds the whole deck rather than appending to it. Five stages, in order.
Stage 1
Pull and reconcile
It hits Triple Whale for blended performance, Klaviyo for flow and campaign revenue, and the ad platforms for spend and delivery. Every figure lands with the window it came from attached, so a partial week is labelled as a partial week rather than silently compared against a full one. Where two sources disagree, the deck shows both and names the gap instead of picking a winner.
Stage 2
Rebuild the KPI tiers
The deck runs metrics in tiers: headline revenue and spend at the top, then efficiency (ROAS, CAC, contribution margin), then retention (repeat rate, cohort behaviour, email share of revenue). Each tier gets a week-on-week comparison and a full-month comparison, because a bad week inside a good month is a different conversation to a bad week inside a bad month.
Stage 3
Cohort the ad performance
It separates creative launched this period from creative carried over, so creative fatigue shows up as a trend rather than a surprise. New launches get their own report with spend, early performance, and how they compare to the account average at the same age.
Stage 4
Draft the next creative concepts
Off the back of what performed, it drafts four new ad concepts: angle, hook, format, and the customer language it is built on. These are starting points for the creative team, not finished ads. Their job is to stop the week beginning with a blank page.
Stage 5
Sync and flag
Everything writes back to Notion so the deck, the tasks, and the campaign tracker stay in one place. Anything that breaches a threshold gets flagged at the top rather than buried on slide nine.
The part that makes it feel like an employee
A script that pulls numbers is not an agent. What separates the two is memory. This one carries a persistent context file for each brand: which metrics that brand actually steers by, the thresholds that count as a problem for them specifically, the naming conventions their ad account uses, the seasonality that explains a dip nobody needs to panic about, and what was decided last week.
That memory is why the deck reads like it was written by someone who works there. A 12 percent drop in blended ROAS is a flag for one brand and a normal Tuesday for another, and the agent knows which is which because the brand context says so. It also means the deck is written in the brand's own vocabulary rather than generic marketing language, which is the difference between a report people read and a report people file.
What lands on Monday morning
A rebuilt deck with the KPI tiers populated and both comparison windows filled in. A recent-launches report listing every creative that went live in the period with its spend and early read. Four drafted ad concepts with hooks and angles attached. A short exceptions list at the top naming anything that breached a threshold, with the number, the window, and what it was last week.
What it does not contain is commentary pretending to be strategy. The agent reports and flags. The decisions are mine, and I make them at nine on a Monday having read the deck for twelve minutes rather than having assembled it for four hours.
Where it still gets things wrong
Late-attributed conversions are the main one. If a platform backfills after the pull, the deck understates the week and has no way to know it. The fix is a restated figure in the following week, clearly marked, rather than pretending the first number was right.
Partial weeks needed explicit rules after the agent once compared two days against seven and reported a collapse that did not exist. And it has no instinct for context outside the data. If a delivery went out late or a hero SKU was out of stock on Thursday, that explanation comes from a human. The agent will show you the dip. It will not tell you why.
Inside the system
How we build this for brands
The reporting agent sits on top of a profit and cash-flow layer built from live Shopify and ad data, so the deck is reading contribution margin rather than platform-reported ROAS. Alongside it, the VOC engine mines reviews and support messages into the positioning that feeds the creative concepts, which is why the drafted hooks use customer language rather than invented copy.
Downstream, the same context layer feeds the Klaviyo lifecycle flows and the creative pipeline, so the thing that measures performance and the things that produce it are working from one shared memory of the brand. Part of this runs live for portfolio brands today; the full system is what we deploy when we take a brand on.
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Book A DemoFrequently asked questions
How long does manual weekly reporting take for a DTC brand?
Industry figures put manual client reporting at roughly 5 to 10 hours per account per month, with 2 to 3 of those hours going purely on pulling platform data into a deck. For a brand running a genuine weekly cadence rather than monthly, that number roughly doubles. Most of it is not analysis, it is copying numbers between tabs.
Can I build a reporting agent like this myself?
The mechanics are buildable. You need API access to your data sources, a runtime that can call them on a schedule, and a fixed deck structure the agent writes into. What people underestimate is the brand memory layer: the agent has to know which metrics matter for this brand, what its thresholds are, and what happened last week. Without that you get a data dump, not a report. Expect the first version to take a few weekends and the useful version to take a few months of correction.
How long does it take to set up a reporting agent for a brand?
Connecting the data sources and getting a first deck out takes a few days. Getting to the point where nobody edits the deck before it goes out takes about four to six weeks, because that is how long it takes to encode the thresholds, naming conventions, and judgement calls a human was making without writing them down.
Does an AI reporting agent replace an analyst?
No. It replaces the assembly work, which is most of the hours and almost none of the value. The agent pulls, reconciles, populates and flags. Deciding what to do about a rising CAC or a cohort that has stopped repeating is still a human call, and it is a better call when the person making it has not just spent three hours in a spreadsheet.
What does a reporting agent get wrong?
Attribution windows and partial weeks. If a platform backfills conversions after the pull, the deck understates performance and the agent cannot know. Partial weeks need explicit handling or it compares four days against seven and reports a collapse that is not real. Both are solvable with rules, but you tend to write those rules after being burned once.
What data sources does the deck need?
At minimum, an attribution or blended reporting source, the email and SMS platform, the ad platforms, and the store itself. The order matters less than consistency: the same sources pulled the same way every week is what makes trends readable. Adding a source mid-quarter creates a step change in the data that looks like performance and is not.
About the author
Caner Veli built Liquiproof to global distribution across 3,000+ retailers, then exited. He now runs Purposeful Profits using a combination of operator strategy and AI-powered systems he has built and uses daily, having 10x'd monthly revenue in his own business in the last 90 days.