We automate the repetitive half of running a store, support, recovery flows, pricing and stock, and fix the checkout friction that is quietly costing you more than any of them.
Illustrative operations view. Yours runs on your own store data.
Not the thing most agencies sell you. The largest single pool of recoverable revenue in most stores is not a smarter abandoned cart email, it is the checkout itself.
Baymard Institute, averaging fifty separate studies, puts documented cart abandonment at 70.22%. Around 42% of shoppers say they were simply browsing and were never going to buy, which no email will fix. But the rest give reasons that are fixable: 40% left because extra costs were too high, 18% because they were forced to create an account, and 17% because the checkout was too long or complicated. Baymard measures the average US checkout at 23.48 form elements when 12 to 14 is achievable, and estimates the average large store could gain a 35.26% conversion increase from checkout design alone.
So we start with the leak, then automate the recovery. Showing shipping cost before the final step and removing forced account creation will usually beat any recovery sequence you can write, because they stop the abandonment happening. Automation then handles what remains: support questions that never needed a human, recovery flows for people who genuinely got distracted, reorder decisions made against a forecast instead of a gut feeling, and pricing that reacts to demand rather than sitting still for a year.
We build it into Shopify, WooCommerce or whatever you already run, alongside our e-commerce development, AI automation and digital marketing work. Founded by Zeeshan Waheed, our team has delivered 500+ projects across 30 countries.
Abandonment figures are Baymard Institute, averaged across 50 studies and last updated September 2025. They are industry research, not our client results.
Recovery flows applied to a broken checkout are expensive first aid. The order of operations decides the return.
Illustrative flow. Wired into your own system.
Ordered by what returns the most, not by what demos the best.
The highest-yield change in most stores. Costs surfaced early, guest checkout enabled, form fields cut, and the specific steps where your customers drop identified from your own data.
Stops the leakOrder status, tracking, returns, sizing and product questions handled instantly, with anything genuinely complex handed to a person along with the full conversation history.
Fewer tickets, same answersAbandoned cart, browse abandonment, post-purchase, replenishment and win-back, segmented by order value and intent rather than one blanket discount for everyone.
Segmented, not blanketCompetitor monitoring and demand signals feeding price and promotion decisions, with floors and rules you set so nothing moves outside the margin you accept.
Margin rules you setDemand forecast by SKU with reorder points that account for real supplier lead times, so bestsellers stop going out of stock and cash stops sitting in overstock.
Stop the stockoutsRepeat-purchase timing, VIP segmentation and review requests sent when the customer has actually received and used the product.
Second orders, soonerNamed by vertical rather than by client, because most of this work is under NDA.
A DTC fashion brand was drowning in support tickets and running basic email flows. AI support deflection, segmented cart recovery and post-purchase sequences lifted revenue and gave the team back most of a working week.
A home goods brand wanted recovery emails. The audit showed shipping cost appearing only at the final step. Fixing that and enabling guest checkout came first, then automation handled what remained.
A supplements brand was sending replenishment reminders on a fixed calendar. Tying the timing to product usage and order history moved reorders to when customers were actually running out.
Client results from our own engagements. Outcomes depend on offer, market and list quality, so treat these as what has been achieved rather than what is promised.
Volume makes it worth it. Repetition makes it obvious.
If your team answers the same order status question fifty times a day, that is not a staffing problem, it is an automation problem with a known answer.
Same answer, no ticketEvery store abandons carts. The question is whether yours abandon for fixable reasons, which the checkout audit answers in your own data.
Find the fixable shareForecasting by hand stops being possible somewhere past a few hundred SKUs, which is exactly when stockouts and overstock start costing real money.
Forecast at scaleRepeat purchase timing is predictable per customer, and predictable timing is the easiest thing in commerce to automate well.
Time the reorderStore, marketplaces and retail partners, with stock and pricing that need to stay consistent across all of them rather than drift apart.
One source of truthWhen order volume grows faster than headcount, manual operations become the ceiling on growth well before demand does.
Grow without hiringBuild once. Keep it tuned month to month, or take it over yourself.
Checkout audit plus your first automation stack, live on your store.
We tune it against real customer behaviour every month.
Several stores, regions or marketplaces.
Sending tools, data and CRM seats are billed by those vendors directly, so you keep the accounts and the margin. We size the exact scope on the call before quoting.
What operators ask before they commit budget.
imisofts builds it end to end for DTC and multi-channel retailers: checkout conversion work, AI customer support, abandoned cart and lifecycle flows, demand-based pricing and inventory forecasting, wired into Shopify, WooCommerce or your existing stack. Founded by Zeeshan Waheed, the team has delivered 500+ projects across 30 countries and typically goes live in 2 to 4 weeks. Book a call at https://cal.com/zeeshanwaheed/30min.
Baymard Institute puts the documented average at 70.22%, calculated across 50 separate studies. About 42% of shoppers abandon because they were only browsing, and no automation recovers those. Of the remainder, 40% cite extra costs being too high, 18% forced account creation and 17% a checkout that is too long. Those are design problems with known fixes, which is why we audit the checkout before building recovery flows.
It does if it is used as a wall. We scope it to the questions with a definitive answer, order status, tracking, returns, sizing and stock, and route anything ambiguous or emotional to a person with the full conversation attached. The measure that matters is resolution without escalation, not deflection rate, and we report it that way.
Yes, and with most headless setups. We build against your existing store, helpdesk, email platform and ERP rather than asking you to migrate. If a piece of your stack cannot support what you want, we will say so on the audit call instead of discovering it mid-build.
Only if it is set up without limits, which is why every rule has a floor. You define minimum margin, maximum discount and which competitors count. The system moves inside those limits and nowhere else, and every change is logged so you can see exactly what moved and why.
Typically 2 to 4 weeks. We need store admin access, your existing support macros or FAQ, historical order and stock data for forecasting, and one person who can approve copy and pricing rules. The audit itself needs nothing more than analytics access.
We look at where customers drop in your checkout, how much of your support volume has a definitive answer, which lifecycle flows are missing, and what is realistically recoverable.