Retail AI specialists · D2C · Ecommerce · Wholesale · Retail
Hire Team that builds AI for Retail.
A senior team — 60+ combined years — that built the demand, pricing, and recommendation systems inside Kohl's and Sears. Now building them for growing retail and e-commerce businesses. First production result in 4 weeks.
D2C Brands · Ecommerce & Marketplace Sellers · Wholesale & Distribution · Multi-store Retail
Ship-or-don't-bill · You own the code · Month-to-month after milestone one
Demand forecasting, Recommendation engines, Reconciliation automation, Returns prediction, Dynamic pricing, Inventory prediction, Customer segmentation, Marketplace settlements
Proof in numbers
- Sellers on HappySellers
- 250+
- Years in retail AI
- 15+
- To first production result
- 4 wks
- Revenue sweet spot
- $5M–$50M
Sellers on HappySellers
our live AI lab, real GMV
Years in retail AI
inside Kohl's and Sears
To first production result
or the milestone is free
Revenue sweet spot
the brands we serve best
Where we've shipped
Kohl's
Team shipped here
Sears
Team shipped here
WORLDEF
Exhibited here
HappySellers
We built this
The problems
If any of these sound like your business, our team has fixed it before.
Sales are up but margins are shrinking
You don't know true contribution margin per SKU after ads, fees, shipping, and returns. So you discount to move stock instead of pricing on purpose.
Inventory is wrong in both directions at once
Cash locked in slow movers while the bestseller stocks out again. Reorder quantities from a spreadsheet built on last year's numbers.
There's no single version of the truth
Shopify says one number, Amazon another, the 3PL a third, the books a fourth. Weekly reporting eats two days of someone's week.
Every tool you bought added a dashboard, not an answer
Analytics tells you what happened. It never tells you what to do on Monday.
Customers buy once and disappear
No segmentation, no repeat-purchase signal, CAC climbing, attribution nobody trusts.
Every decision bottlenecks on one person's judgment
And that person is usually you.
If three or more are true, that's a 30-minute conversation.
Book a callWhat we know
Before we build anything, here's what we understand.
Most AI teams learn your business on your budget. We've already spent 15 years in it.
Inventory & replenishment
Reorder points, safety stock, lead-time variance, multi-warehouse allocation.
Demand & seasonality
New-product cold start, promotional lift, size and colour curves.
Pricing & margin
Elasticity, markdown timing, true per-SKU contribution.
Customer behaviour
Segmentation, repeat purchase, LTV, churn signals.
Merchandising & recommendations
Basket analysis, cross-sell, catalogue ranking.
Returns
Return risk at checkout, size-driven returns, cost allocation per SKU.
Marketplace & wholesale ops
Settlements, deductions, chargebacks, sell-through after shipment.
Retail data engineering
Shopify, Amazon, ERP, POS, 3PL, ad platforms into one trusted model.
The team
Who you're actually hiring.
Not a sales team with engineers behind a curtain. The people below are the people on your calls.
The lineage
60+
Combined years in retail & ecommerce data
Recommendation engines at Kohl's. Holiday war rooms at Sears. Systems that decided what 20 million shoppers saw on the busiest shopping days of the year.
How we staff
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1 delivery lead — named from week 0, on every call
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1 data engineer — builds the pipeline into your stack
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Fractional ML/analytics — pulled in as the milestone needs
Where we work
Delivery team
Pune, India
US-hours overlap
9am – 1pm ET, every business day
On-sites
Quarterly for retainer clients
Communication
Shared Slack, weekly demo, async by default
HappySellers Live Lab
Every technique we recommend has already run on real commerce.
HappySellers is our own platform: 6,000+ registered businesses, 250+ live sellers today. Real orders. Real inventory. Real returns. Real settlements. Not demo datasets.
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Every forecast, ranking, and reconciliation model is tested on our GMV before it touches yours.
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Multi-channel, multi-warehouse, multi-brand — the same data shape your business runs on.
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What it means for you: we've already made the mistakes on our own P&L, not yours.
Representative milestones from a typical engagement.
The work
Proof.
Client case studies publishing shortly. In the meantime, here's the actual output of the systems we build — redacted where clients require it.
forecast vs actual · 90 days
Demand forecast vs. actual
Stockouts dropped from 8.1% to 2.4% in 12 weeks.
reconciliation · daily run
Reconciliation exception report
12 hrs/week of finance ops recovered.
recommendation payload · user 3,201
{
"basket": ["SKU-2231"],
"recos": [
"SKU-1104" · 0.94,
"SKU-0876" · 0.81
]
}
Recommendation output
AOV lift of +18% from cross-sell placements.
customer segments · repeat rate
Customer segmentation view
Retention campaigns targeting at-risk lifted repeat rate +9pt.
reorder recommendations · this week
Reorder recommendation screen
Excess stock cut 31% without a single stockout.
data pipeline · overnight batch
System architecture
One source of truth. Every downstream model reads from it.
What we ship
Four AI systems, merchandised like your catalog
Every system below is in production somewhere right now. Browse like you'd browse your own store: outcomes up front, shipping times honest.
Demand Forecasting
Know what sells, when, by SKU and channel. Buy the right stock and stop guessing reorder points.
Recommendation Engines
The architecture our founder built for 20 million Kohl's shoppers, sized for your catalog and margins.
Reconciliation Automation
Payments, fees, refunds and returns matched overnight. Exceptions go to a human, the rest just runs.
Inventory Prediction
Reorder points, safety stock, and warehouse rebalancing computed from your real sell-through, not gut feel.
Your stack
We work with the data you already have.
No data team required. We build the pipeline as part of the engagement.
Ecommerce & marketplaces
Inventory, ops & finance
Customer & ads
The process
From first call to live AI, week by week
No discovery phases that never end. Every step below has a date and a deliverable.
The fit call
30 minutes. We tell you honestly whether AI is worth it for your store right now, and which use case pays back first. If it's not a fit, we say so on the call.
AI Fit Sprint
We map your data, score the highest-ROI use cases, and hand you a prioritised implementation roadmap with owners and dates. Not a deck. A plan.
First milestone ships
Working AI in your production stack. A forecast feeding your purchase orders, a reconciliation run posting to your books. If it doesn't ship, you don't pay for it.
Scale milestones
We ship the next use cases sprint by sprint. Every milestone has a defined deliverable and a metric it has to move. Ship-or-don't-bill, every time.
Embedded retainer
We stay in. Monitoring, retraining, expanding to new use cases. A senior AI engineer on call, without the full-time hire.
Who this is for
Who we work best with
Qualified by operational complexity, not one revenue number. Complexity is what actually predicts whether AI pays back.
We're a fit when
One of these is true
-
D2C or e-commerce doing $5M+
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A retailer with 10+ doors
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A wholesaler or distributor with 50+ active accounts
And at least two of these
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500+ active SKUs
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Selling across 2 or more channels
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Inventory is the largest number on your balance sheet
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Someone rebuilds the same spreadsheet every week
We're probably not a fit if
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You're under $5M in revenue
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You have under 100 SKUs
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You're on a single channel
At that size the answer is usually better process, not AI — and we'll tell you that on the call.
The offer
Priced openly. Shipped or not billed.
Engagements
30 days. We map your data, score the highest-ROI use cases, and hand you a prioritised implementation roadmap.
$8K–$20K per month
Month-to-month after milestone one. Cancel any time. Everything shipped stays yours.
What you get
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Ship-or-don't-bill. Every milestone has a defined deliverable. If it doesn't ship, you don't pay for it.
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You own everything. Code, models, data pipelines. No lock-in, no license fees.
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Data handling under NDA. Read-only where possible. DPA on request. Read the security page →
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Not ready to book? Get the free AI Fit Score — a 5-minute scorecard on whether AI pays back in your business.
Common questions
Before you book a call
How long does it take to ship a first production result?
Four weeks. A full demand-forecasting or recommendation-engine build runs 8–12 weeks end to end. Work is structured in milestones so you see progress every sprint — and if a milestone doesn't ship, you don't pay for it.
What does an engagement actually cost?
The AI Fit Sprint starts at $5K and delivers a prioritised roadmap. Implementation retainers run $8K–$20K per month depending on scope. We publish ranges openly. Book a 30-minute call for a specific number against your use case.
Who is on the team, and who do I actually work with?
A senior team with 60+ combined years in retail and ecommerce data — the same people who built recommendation and forecasting systems inside Kohl's and Sears. Every engagement gets a named delivery lead from week 0, one data engineer, and fractional ML/analytics as milestones need. You never meet a sales pod behind a curtain of engineers.
How do you handle our data and security?
NDA on day one. Read-only access wherever possible. Data stays inside your infrastructure or a scoped environment we can prove is isolated. DPA available on request. At the end of an engagement, credentials rotate and any residual data is deleted. Full details on our /security page.
Do I need a data team to work with TwoDots?
No. We work with the data you already have: Shopify exports, Amazon settlement files, ERP dumps, Klaviyo events, warehouse CSVs. We build the pipeline as part of the engagement.
Is TwoDots right for my size of business?
We work best with D2C/ecommerce at $5M+, retailers with 10+ doors, or wholesalers with 50+ active accounts — as long as complexity is real (500+ SKUs, multiple channels, inventory-heavy). Under $5M with a single channel is usually a process problem, not an AI one, and we'll say so on the call.
Your move
Let's ship.
Book a free AI fit call. We'll tell you exactly which AI use case is worth pursuing first, and whether TwoDots is the right partner for it.
Free assessment. No commitment. Honest about fit.