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AI for Retail & E-commerce

The sell-through is in the plan you make before the season ships.

We engineer AI for retail and e-commerce brands: demand forecasting across DTC, marketplaces, and wholesale; catalogue hygiene and merchandising; on-brand personalisation; cart recovery and lifecycle; and returns that stop eating margin.

Built on your real catalogue, your real orders, and the systems your merchants already use.

Discuss your merchandising workflowSee how the system works

The operational reality

You plan on sixty to a hundred and twenty days of lead time. You ship into three or four channels.

One missed signal shows up as a stockout. Or an overbuy. Both cost margin.

The forecast beats the gut. Every time it reads the signal first.

Felt by heads of merchandising, heads of e-commerce, CMOs, and the supply and ops leaders who absorb what a weak demand plan leaves behind.

  1. 01

    Plan and forecast

    Merchants build the season plan: unit forecasts by SKU and channel, open-to-buy, promo calendar, and the inventory position needed to hit it.

    Friction · Forecasts live in a spreadsheet that nobody rebuilds mid-season; a two-point miss becomes a markdown problem by week eight.

  2. 02

    Catalogue and merchandising

    New SKUs are set up in the PIM and the storefront: attributes, images, copy, categories, cross-sells, and marketplace mappings for Amazon, eBay, Walmart, and TikTok Shop.

    Friction · Missing attributes and weak copy sink on-site search and marketplace rankings before the product has a chance.

  3. 03

    Acquisition and personalisation

    Paid and lifecycle teams drive traffic from Meta, Google, and TikTok. On-site, personalisation decides what each visitor sees and which offers surface.

    Friction · Personalisation is a vendor box that treats every visitor like the top of the funnel; the brand voice gets lost in templated recommendations.

  4. 04

    Cart, checkout, and lifecycle

    Carts fill and empty. Abandoned-cart, browse-abandon, and win-back flows run in Klaviyo. Shopify or the custom checkout handles the transaction.

    Friction · Flows are generic; the sender name is the brand but the message is not. Recovery is measurable but under what it could be.

  5. 05

    Fulfilment, reorder, and returns

    Orders are routed to 3PLs or stores for fulfilment. Reorder decisions are made in a weekly review. Returns are received, graded, and resold or written down.

    Friction · Reorder decisions sit on a spreadsheet that is a week behind the signal; returns are a profit leak nobody owns end-to-end.

Swipe the workflow

Where the work breaks

The friction is in the places nobody budgets for.

  • 01

    Demand plans go stale the moment the season starts selling.

    How we fix it · A system we build catches this before it costs you.

    Impact · Air freight to cover a hot item, markdowns to clear the one that missed, and a GMROI that reads worse than the brand actually is.

  • 02

    Catalogue copy and attributes are the quiet drag on sell-through.

    How we fix it · A system we build catches this before it costs you.

    Impact · On-site search misses the match; marketplace listings sit under the fold; the product that should sell is invisible to the person already looking for it.

  • 03

    Personalisation reads nothing like the brand.

    How we fix it · A system we build catches this before it costs you.

    Impact · The site feels interchangeable with every other DTC store running the same vendor; the voice the founder spent years earning goes templated at checkout.

  • 04

    Cart abandonment and lifecycle flows plateau under what they could recover.

    How we fix it · A system we build catches this before it costs you.

    Impact · Known revenue leaves the funnel every day; the recovery copy was written once and has not been refreshed in a year.

  • 05

    Returns and reverse logistics eat margin without being planned for.

    How we fix it · A system we build catches this before it costs you.

    Impact · The accepted return rate was baked into the margin a year ago; the actual rate is higher by category and nobody owns the fix.

What we would build

Systems, not features. Each with its own job.

  • 01

    Demand forecasting across channels

    The season plan is right in August and wrong by October; nobody has time to rebuild it mid-season.

    Who benefits · Heads of merchandising, planners, and supply ops.

    Inputs
    Order history by SKU, channel, and location; current on-hand and in-transit; promo calendar; lead times by vendor; weather and seasonality; marketplace signals.
    What the system does
    A hierarchical forecast scores unit demand by SKU, channel, and week with a confidence interval. It reconciles to the merchant's open-to-buy and surfaces the SKUs where the plan is drifting against actuals.
    Output
    A living forecast, a reorder queue flagged by stock-out and overstock risk, and the specific decisions the planner should take this week.
    Benefit
    Fewer stock-outs on the SKUs that drive the season, fewer markdowns on the ones that missed. Days of supply trend to where the merchant wants it.
    Human stays here
    The planner owns the final buy. The system shows its interval and the signals behind the move, and never auto-places a purchase order.
  • 02

    Catalogue hygiene and merchandising

    New SKUs land in the PIM with missing attributes, weak copy, and inconsistent category tags; the storefront loses the sale before it starts.

    Who benefits · Merchandisers, e-commerce leads, and marketplace managers running Amazon, eBay, Walmart, and TikTok Shop.

    Inputs
    The product in the PIM, supplier spec sheets, images, past copy that worked, and marketplace category requirements.
    What the system does
    The system drafts missing attributes, generates on-brand product copy, suggests category and cross-sell mappings, and flags listings that fail marketplace rules before they go live.
    Output
    A review queue of PIM entries ready for merchant sign-off, with the mapping back to each marketplace format.
    Benefit
    On-site search matches the shopper's query, marketplace listings earn their spot, and the merchandiser spends their time on the SKUs that need judgement.
    Human stays here
    Nothing publishes to a storefront or marketplace until a merchant approves it. Brand-voice drift is caught against the brief, not after launch.
  • 03

    Personalisation that reads like the brand

    The on-site experience is powered by a vendor box that treats every visitor like a new one and makes the brand sound like every other DTC.

    Who benefits · Heads of e-commerce, CRO leads, and content.

    Inputs
    Session behaviour, order history, email engagement, the brand voice brief, and the current merchandising priorities.
    What the system does
    A segmentation and ranking layer picks what each visitor sees. On-site copy, PDP recommendations, and lifecycle emails are drafted in the brand voice from the brief, not from a vendor template.
    Output
    Personalised PDPs, collection pages, and lifecycle emails that the merchant can edit, approve, and schedule.
    Benefit
    Personalisation lift that compounds instead of plateauing, and a site that still sounds like the brand two years in.
    Human stays here
    Content stays editable and approvable. The system refuses to publish copy that scores poorly against the brand brief.
  • 04

    Cart recovery and lifecycle

    Abandoned-cart and browse-abandon flows run in Klaviyo on copy written a year ago; the lifecycle plateau is a copy and timing problem.

    Who benefits · Lifecycle leads, email marketers, and the e-commerce manager.

    Inputs
    Klaviyo events, Shopify order history, product inventory and merchandising priorities, past campaign performance.
    What the system does
    The system drafts flow variants per segment, times sends around browse and purchase behaviour, and runs structured A/B tests on subject lines and offers against a holdout.
    Output
    A continuously refreshed set of flows and campaigns, with results attributed back to the holdout rather than to vendor dashboards.
    Benefit
    Abandoned-cart recovery climbs; email revenue stops being a flat line against last year.
    Human stays here
    Every send is reviewed by the lifecycle lead. Discount floors and brand guardrails are enforced in the brief, not left to the model.
  • 05

    Returns and reverse-logistics triage

    Returns are an accepted cost nobody owns end-to-end; the rate is highest in categories merchandisers do not see.

    Who benefits · Ops leads, customer experience, and merchandising.

    Inputs
    Return reasons, order and customer history, product variants and sizing, warehouse inspection notes.
    What the system does
    The system clusters return reasons by SKU and variant, surfaces the handful of items driving the long tail, and drafts the fix: updated size guidance, a photography retake, or a product-page edit.
    Output
    A weekly return triage with the three SKUs most worth fixing, each with a specific suggested change and the expected impact.
    Benefit
    The return rate on the worst SKUs comes down; the margin written off to reverse logistics shrinks against plan.
    Human stays here
    Merchandisers approve every content and sizing change. Return accept/reject stays human for anything outside policy.

Show the system

What it could look like in your hands.

A design concept, not a shipped product. Interfaces like this get drawn out before any code lands, so the system reads back the way the people using it work.

merchandising / demand and reorder
As of Mon 09:14

SKUs under watch

  • SKU-10421 · Apparel / tops

    Linen oversized shirt, ivory

    148 on hand

  • SKU-10488 · Apparel / tops

    Terry crew sweatshirt, dusk

    612 on hand

  • SKU-10512 · Apparel / bottoms

    Wide-leg denim, washed

    220 on hand

  • SKU-10607 · Footwear

    Suede loafer, cognac

    92 on hand

  • SKU-10729 · Accessories

    Everyday tote, olive

    410 on hand

Linen oversized shirt, ivory

SKU-10421 · 12-week forecast · units per week

  • Forecast
  • 90% band
  • Actual
0 u/wk100 u/wk199 u/wkW28W30W32W34W36W38Reorder at 95

Shaded band is 90% prediction interval. Dashed line is the reorder threshold agreed with the merchant.

Synthetic data. This is a design concept of how a demand forecast, prediction interval, reorder threshold, and the SKUs flagged for action could look in a merchant's hands.

End-to-end workflow

From the trigger to the outcome.

A representative season path, from the sales signal to a merchant decision. Automated stages run on the data as it lands; human stages are explicit and never skipped.

  1. 01

    Signals land

    Order history, on-hand, in-transit, marketplace sell-through, promo calendar, and vendor lead times are pulled from Shopify, Netsuite or SAP, and the marketplaces each night.

    Integration
  2. 02

    Forecast refreshes

    A hierarchical forecast scores every SKU and channel for the next twelve weeks with a 90% prediction interval and reconciles against the open-to-buy.

    Automated
  3. 03

    Drift and risk flagged

    SKUs trending outside their interval or crossing a reorder threshold are surfaced before the Monday merchant stand-up, with the signals behind the flag.

    Guardrail
  4. 04

    Catalogue and content checks

    New and refreshed SKUs are drafted into PIM entries and listings; brand-voice and marketplace-rule checks run before anything goes to a review queue.

    Automated
  5. 05

    Merchant review

    Merchandisers review the reorder queue, approve catalogue edits, and sign off on the week's priorities. Nothing publishes or orders without them.

    Human in loop
  6. 06

    Lifecycle acts on the plan

    Approved priorities push to Klaviyo as segment, offer, and send guidance. On-site personalisation surfaces the hero SKUs where attention actually lands.

    Integration
  7. 07

    Weekly readout

    A weekly brief lands with sell-through vs plan, forecast error by category, and the three SKUs the merchant should look at hardest next.

    Automated

How exceptions are handled

  • If a vendor lead time changes, the reorder queue refuses to recommend a buy until the new lead time is confirmed with ops.
  • If a marketplace rule changes overnight, affected listings are held and surfaced for a merchant to re-approve.
  • If a personalisation copy variant scores below the brand brief, the system refuses to send it, even if the test shows a lift.
  • If order data is missing for a channel, the forecast flags the gap rather than filling it in with an assumption.

How it is engineered

Built on your stack, not ours.

Design choices that make this run in a real merchandising week, not a demo. We build to your commerce stack and your merchandising calendar, not to our own.

01

Commerce and ERP integration

Shopify, Netsuite, SAP, Oracle Retail, and the marketplaces each ingest through their native APIs. We write back only through the surfaces each system approves. No scraping, no fragile middleware.

02

Catalogue and PIM boundary

Products live in your PIM. The system reads, drafts, and stages changes; the merchant publishes. Marketplace mappings are versioned so a rollback is one click.

03

Forecasting layer

A hierarchical model stack picks the best approach per SKU class: fast-moving essentials get a different family than long-tail or launch SKUs. Confidence intervals are reported alongside point forecasts.

04

Lifecycle and personalisation

Klaviyo for lifecycle, your CDP for segmentation, and an on-site layer we build to the storefront. Content runs through a brand-voice brief with versioned prompts.

05

Data boundary and ownership

Your order and customer data stays in your tenant. Model inference runs with no retention and no training on your data; fine-tuning, where it helps, happens on your copies of your copy.

06

Observability and evaluation

Every forecast, every content draft, and every lifecycle send is logged with model version, inputs, and the merchant's final edit. An evaluation harness watches forecast error and brand-voice drift.

What the business will measure

Numbers the operator already watches.

We agree on metrics and baselines before building. These are the numbers a retail or e-commerce operator usually watches when deciding whether a system is actually working.

  • 01

    GMROI by category

    Baseline today

    Measured today in the ERP: gross margin dollars divided by average inventory investment, by category and season.

    How it moves

    Fewer stock-outs on hero SKUs, fewer markdowns on the ones that missed, as the plan refreshes against live sell-through.

  • 02

    Sell-through rate at week six and week twelve

    Baseline today

    Units sold vs units received at six and twelve weeks, by SKU class and channel.

    How it moves

    The reorder queue acts on drift inside the season rather than at season close; hero SKUs stay in stock, slow movers do not get re-ordered.

  • 03

    Days of supply vs plan

    Baseline today

    Average days of supply by SKU class vs the merchant's stated target band.

    How it moves

    Forecast and reorder decisions keep cover inside the band rather than oscillating across it.

  • 04

    Abandoned-cart recovery rate

    Baseline today

    Recovered revenue as a share of abandoned-cart revenue, measured against a holdout, by segment.

    How it moves

    Flows refresh on real behaviour, timed to the signal rather than to a static schedule; subject lines and offers test against each other.

  • 05

    Return rate on worst-performing SKUs

    Baseline today

    Return rate by SKU and variant, measured against category average.

    How it moves

    Weekly triage surfaces the three SKUs most worth fixing; sizing, photography, and copy edits land before the next buy.

Human in the loop

Oversight, brand control, and the data boundary

Where the humans stay in control.

The system is designed around what merchants and brand owners already expect. Nothing is autonomous that should not be.

  • 01

    Merchant sign-off on every buy

    No purchase order is placed automatically. The system recommends; the planner decides, with the forecast interval and the signals behind the flag in view.

  • 02

    Brand-voice brief as the ground truth

    All drafted copy is scored against a versioned brand brief. Drafts that fall below threshold are routed back for a rewrite rather than published.

  • 03

    No marketplace rule-breaks

    Listings and content are validated against each marketplace's current rules before they are staged. Rule changes pause the affected listings.

  • 04

    Discount and offer guardrails

    Discount floors, offer stacking rules, and segment-level offer caps are enforced in the brief. The system refuses to send offers that cross them.

  • 05

    Privacy and consent

    Lifecycle and personalisation honour consent at the customer level. Opt-outs flow back to Klaviyo and the storefront on the same cycle.

  • 06

    No training on your data

    Your order, catalogue, and customer data is not used to train base models. Any fine-tuning happens in your tenant, on your data only, with your approval.

Proof

Work we have shipped, and the shape of what we build.

Real engagements where we can speak to them, and representative use cases where we cannot.

Case studies

Shipped in Retail & E-commerce.

How the build runs

Scoped tightly, shipped in phases.

Fixed price per phase, approved before the phase starts. You own the code, the models, the data pipelines, and the deployment.

  1. 01

    Discovery

    Two weeks. We sit in a planning cycle and a merchant stand-up, map the real workflow against your commerce and ERP stack, and agree on the metrics we will move and how we will baseline them.

  2. 02

    Prototype on real orders

    Four to six weeks. One category, one channel, one proof of mechanism. Merchants use it alongside the current week; we measure forecast error and sell-through against the baseline.

  3. 03

    Production rollout

    Eight to twelve weeks per workflow. Commerce and ERP integrations, PIM and marketplace wiring, lifecycle flows, SSO, audit trails, and the evaluation harness.

  4. 04

    Handover

    You get the full source, the prompts, the forecast models, the runbooks, and a two-week walkthrough with your merchandising, e-commerce, and engineering teams.

  5. 05

    Optional ongoing support

    A fixed-term support agreement for model drift, marketplace rule changes, and seasonal recalibration. Never a retainer dependency.

Common questions

Answers before the first call.

01Do you work with Shopify, Netsuite, SAP, and the marketplaces?
Yes. We build against the native APIs for Shopify, Netsuite, SAP, Oracle Retail, Klaviyo, and the major marketplaces. We are not a partner of any of them; we are compatible with the stack you already run, and we write back only through surfaces each platform approves.
02What catalogue size does the forecasting layer work on?
We have modelled from a few thousand SKUs to catalogues in the hundreds of thousands. The hierarchical approach picks a different model family for fast-movers, slow-movers, and launch SKUs, so a long tail does not drag down accuracy on the hero items.
03Will the personalisation copy still sound like our brand?
Brand voice is the ground truth, not an afterthought. All drafts are scored against a versioned brand brief; drafts that fall below threshold are routed back rather than published. The system is only useful to a brand if the brand still sounds like itself a year in.
04Do we have to swap out Klaviyo or our CDP?
No. We build to the lifecycle and segmentation tools you already run. The system drafts flows and segments; your lifecycle lead approves and sends. If you want to migrate later, your flows and segments are yours in portable form.
05How does this handle marketplace rule changes?
Marketplace rules are enforced in the staging layer. When a rule changes, the affected listings pause for a merchant to re-approve rather than silently publishing something that will get rejected.
See the other details on the FAQ page

Next step

A thirty-minute conversation. Bring one category where the plan is wrong by week six today, and we will walk through what the first phase would look like, where the measurements come from, and what has to be true for your commerce and ops leads to approve it.

Discuss your merchandising workflow

Helpful to have on the call

Helpful to have on the call: the commerce and ERP stack you run, one category with a planning or catalogue pain point, and the person who owns the number you would like to move.

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