An ecommerce development agency. Judged on what the store converts.
Most storefronts are not slow because nobody noticed. They are slow because speed sits with one vendor, checkout with another and merchandising with a third, and not one of them is measured on the conversion rate. We are a Shopify Partner and an engineering firm: we build and rebuild commerce storefronts, instrument what they actually do under real customers, and work against a number you can read in your own analytics.
Retailers and direct-to-consumer brands whose store works, sells, and is quietly losing orders somewhere between the ad click and the confirmation page.
Retail margins are thin enough that a build has to pay for itself in the only currency that counts. So this page is about the store as a system under load — how fast it renders for the slowest customers you have, how much friction sits between a product page and a completed order, and whether anything in your stack is measuring either.
Conversion rarely fails loudly.
It leaks in four familiar places, and every one of them is an engineering problem before it is a marketing one.
It is slow on the device that actually pays.
The desktop demo is fine. The phone on a mid-range Android on a suburban connection is not, and that is where the orders come from. Usually it is not one big thing — it is a hero image nobody compressed, a theme shipping code for sections the store no longer uses, and six third-party tags loaded before anything renders.
Checkout asks for things it does not need.
Every extra field, forced account, surprise fee and re-entered address is a place to stop. Abandonment is not one dramatic failure; it is a dozen small frictions, each of which looked reasonable to the person who added it.
Search returns nothing for the words customers use.
The catalog calls it a 'thermal insulated tumbler'. The customer types 'coffee cup that keeps it hot'. A keyword index returns zero results, and a zero-result search is a lost order with a receipt — it is sitting in your analytics right now, already counted.
Nobody owns the number.
The agency owns the design, the platform owns the checkout, an app owns search, and the merchant owns the conversion rate alone. So every party can be doing their job while the number goes sideways, and no single conversation can fix it.
The store, and everything behind it.
Storefront engineering, the integrations it depends on, and the AI layer only where it earns its place.
- 01Storefront builds and rebuilds on Shopify, including theme work written as code rather than assembled from apps.
- 02Replatforming off a store that has outgrown its theme, its app stack, or its hosting — with the catalog, URLs and redirects planned before anything moves.
- 03Headless and custom commerce where the storefront genuinely needs it, and an honest argument against it where it does not.
- 04Performance engineering against field data from real customers: Core Web Vitals, third-party script budgets, image and font delivery, and the render path.
- 05Checkout and funnel work — fields, steps, payment and shipping presentation, and the instrumentation to see where people actually stop.
- 06Catalog, PIM and product-data pipelines, so search, merchandising and SEO pages all read the catalog you actually have.
- 07Integration work behind the storefront: order management, 3PL and fulfilment, ERP, subscriptions, support platforms and analytics.
- 08The AI layer on top when it earns its place — support agents, catalog enrichment, semantic search — which is the /industries/ecommerce-retail side of this.
Measure, then change one thing at a time.
A redesign shipped on taste with no instrumentation behind it is the single most reliable way to make a conversion rate worse. This is the order we work in instead.
Measure the store you actually have
Field data from real sessions first — not a lab score from one run on a fast laptop. What the slowest quarter of your customers experience, on the devices and connections they really use, plus where sessions end in your own analytics. Nothing gets changed in this step.
Rank the findings by money, not by score
A long list of audit items is not a plan. Each finding gets sized by how much of your traffic and revenue it actually sits in front of, so the work starts where the loss is — which is frequently not the thing that scores worst.
Fix the platform layer
Render path, image and font delivery, the third-party script budget, and code the store no longer uses. This is the part with no taste argument attached: it is the same page, arriving sooner.
Work the path to purchase
Collection and product pages, search and merchandising, then the checkout path — fields, steps, and how payment and shipping are presented. Changes go in one at a time so an outcome can be attributed to a cause.
Prove it with a test, not an anecdote
Where traffic supports a split test, the change is tested against the unchanged version and read at the end, not on day two. Where it does not, we say so and use a before-and-after window with the seasonality called out, rather than dressing it up as a trial.
Hand over the instrumentation
The dashboards, the budgets, the alerting and the test setup stay with you, in your accounts, along with the code. The point is that you can run the next round without us — including with a different agency.
What the measured research says this is worth.
We have no published conversion result of our own to show you, so here is the next best thing: figures other people measured, published, and can be checked against their own bases.
The two performance figures come from a single Rakuten 24 experiment published by Google on web.dev: one landing page, a one-month 50/50 split, version A optimized for Core Web Vitals and version B left alone, with no other functional or visual differences between them. Version A produced a 33.13% increase in conversion rate and a 53.37% increase in revenue per visitor. One store, one page, one month — not an industry average, and not a promise about yours.
The two checkout figures are Baymard Institute’s. 70.22% is their average documented cart abandonment rate, calculated across 50 different studies rather than measured once. 35.26% is the conversion-rate increase they estimate the average large-sized ecommerce site can gain through better checkout design, from ten years of large-scale checkout testing on sites including Walmart, Amazon, Wayfair, Crate & Barrel and ASOS.
web.dev/case-studies/rakutenbaymard.com/lists/cart-abandonment-rate
A Shopify Partner, stated at its actual size.
Beyond Technologies is enrolled in Shopify's partner programme, alongside the three cloud and model partnerships the rest of our work runs on.
Worth stating because it is checkable and because it tells you which ecosystem we work in every day. Worth keeping in proportion for the same reason: a partner programme is not a certification, not a performance tier and not an award, and it has never made a store convert on its own. All four are published in this site’s Organization structured data as programme memberships, so a machine reading the page gets the same claim a person does — no more, no less.
Scope, not a scoreboard.
Three published engagements that are genuinely commerce work. We are showing what was built rather than what it returned, because the outcome numbers on these are either under NDA or not yet confirmed with the client — and an unconfirmed number is worth less than an honest sentence.
AI Support Agent for a DTC Ecommerce Brand
A production support agent handling order status, returns, and product questions across email and chat — integrated with Shopify and the brand's 3PL, with human escalation built in.
Read the caseMeditizer — Unified Commerce & Agent Ecosystem
A unified digital ecosystem: eCommerce storefront, field-agent portal, customer mobile app, and a multi-level referral engine — all orchestrated from one central admin CMS.
Read the casePassGlobe — Lifestyle Subscription Platform
A subscription-driven offers ecosystem built around three dashboards — user, vendor, admin — plus a conversion-focused marketing site. Weekly Stripe subscriptions, QR-based one-time redemptions, launched in Azerbaijan and built to expand globally.
Read the caseWhat is deliberately not on this list: a storefront rebuild or a conversion-optimisation engagement. We have not published one, so we are not going to imply one. If that is the only evidence that would satisfy you, that is a reasonable position and you should say so on the call.
Once the store itself is sound, the layer above it is a separate piece of work: support agents that resolve rather than deflect, generative catalog enrichment, semantic discovery, and demand and pricing analytics.
AI operating system for ecommerceFour reasons to hire someone else.
Pre-qualifying costs us inbound and saves both sides a month.
- 01You want a rebrand and a new look. That is a design studio's job, and a redesign shipped with no measurement behind it is one of the more reliable ways to make a conversion rate worse.
- 02You want a conversion-rate number guaranteed before anyone has seen your field data. Nobody honest can give you one, and a firm that does is selling you the guarantee, not the outcome.
- 03You want the cheapest possible theme install. We write storefront code and own what it does under load; that is not the same purchase.
- 04You want more traffic. This is about what happens after the click — paid and organic acquisition is not work we take.
The questions worth asking first.
Not a storefront one, no — and we would rather say that plainly than point at something adjacent and let you assume. There is no published storefront build or conversion-optimisation engagement on this site, because we have not shipped one we are able to show. What is published and real: we are a Shopify Partner; we built a production support agent for a US direct-to-consumer brand that integrates with Shopify's APIs and the brand's third-party logistics provider, with human escalation built in, which is under NDA; and we have built full commerce platforms end to end — Meditizer's storefront, field-agent portal, mobile app and admin, and PassGlobe's subscription commerce with Stripe payments and vendor and admin operations. That is commerce engineering with real scope behind it. It is not a conversion case study, and we are not going to call it one.
It means the company is enrolled in Shopify's partner programme and builds on Shopify's platform and APIs. It is worth stating because it is checkable and because it tells you which ecosystem we work in day to day. It is not a certification, not a performance tier, not an award, and it does not by itself make a store convert. Treat it as context about the platform, then judge the engineering on the engineering.
Agreed with you before anything is built, and read from your analytics rather than ours. Usually it is the conversion rate on the sessions the work actually touches, alongside revenue per session so a rate improvement bought by killing average order value cannot pass as a win. Add-to-cart and checkout-completion rates separate a product-page problem from a checkout problem, and field Core Web Vitals sit underneath as the leading indicator. All of it segmented by device, because the mobile number is the one that moves.
No, and you should be wary of anyone who does. What is under our control is the work: the store gets measurably faster on real devices, the friction we agreed to remove is removed, and every change is instrumented so the result is attributable instead of argued about. What the market does with your prices, your products and your competitors in the same quarter is not under anyone's control. We will tell you before we start which findings we think are worth the money and which are not.
Shopify unless there is a specific reason it cannot do the job — an integration it genuinely cannot carry, a catalog or merchandising model it cannot express, or a storefront that is really an application. Headless buys you control and costs you a platform's worth of things you now maintain yourself: previews, checkout, SEO plumbing, the build pipeline, and every upgrade forever. Plenty of stores are on headless architectures they did not need and are slower for it. We will make the argument against it if the argument is there.
No. The engineering underneath — the render path, the third-party script budget, catalog and product data, checkout instrumentation, and the order, fulfilment and support integrations — is largely platform-independent, and our commerce work already spans Shopify, WooCommerce and Magento APIs alongside custom builds. Replatforming is a decision we would rather reach with you on evidence than assume on day one.
Yes, and it is written up separately at /industries/ecommerce-retail — support agents that resolve rather than deflect, generative catalog enrichment, semantic product discovery, and demand and pricing analytics. It is a separate page on purpose: a store that is slow and leaks at checkout does not need an AI layer first, it needs the store fixed first. If you arrive wanting the AI work, start there instead.
There is no rate card on this site and there is not one here either. Commerce work ranges from a scoped performance and checkout engagement on an existing store to a full rebuild, and quoting either from a page rather than from your store would be a made-up number. Tell us the platform, the catalog size and what you are seeing in analytics, and you will get a real figure and a scope in writing before anything starts.
We are headquartered in Glen Allen, Virginia, with engineering centers in Lahore and Dubai — US contracts and US business-hours coverage, with follow-the-sun progress overnight. You meet the engineers who will do the work before you commit, and the code lives in your repositories from the first commit.
Send us the store URL and what your analytics is telling you.
Let's put AI to work in your business.
A 30-minute call. You bring the workflow or the roadmap — we'll tell you what's feasible, what it costs, and what we'd build first.