Palette

For retail buyers and merchants

Stop guessing on price.
Stop over-assorting.
Start selling through.

Palette tells you what to price, what to keep, what to cut, and when to act — with evidence behind every recommendation. It gets sharper every cycle and empowers your Open-to-Buy / Line Strategy decisions.

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Price with Precision

Every SKU gets a recommendation informed by demand elasticity, margin constraints, competitive position, and lifecycle stage. Every recommendation shows its reasoning.

Simplify with Confidence

Know which products earn their shelf space. When Palette recommends a removal, it models where demand migrates — so you cut SKUs without cutting sales.

See What's Next

Catch rising attributes before they peak. Spot fading trends before overbuying into them. Palette defines products optimized by attribute and frees budget to innovate.

How Palette Works

At the core of Palette is Sherlock, a fully-custom AI engine. Every opinion Sherlock forms is explicitly evidence-based — evidence compounding on evidence, cycle after cycle. As it becomes more familiar with your business, it develops strong convictions about what works and what doesn't, and sets expectations for what will happen next.

When your actuals align with those expectations, conviction strengthens. When they defy expectations, Sherlock reasons through the source of the difference and revises its stance based on the new evidence.

It doesn't start over from scratch. It doesn't retrain on your entire history. It reasons — like its namesake — from models it's built, and revises only where challenged. Each time you flip a Product Card in Palette, it's akin to the great detective rounding everyone up and explaining what's what.

Your data is processed and returned.
The intelligence stays.
The data doesn't.

Your DataSnapshot InSherlockForms OpinionsExpectationsDeliveredActualsReturnConfirmedChallenged

A Different Kind of Platform

Most retail optimization vendors share the same architecture... and limitations.

With Palette
Typical ML Vendor
Works from your first data submission
6–18 month implementation before first insight
Explainable — every recommendation shows its reasoning
Black box — results without rationale
Deterministic — same input, same output
Statistical — results vary with retraining
Zero data retention — your data is returned
Warehouses your data to retrain models
Accumulates intelligence, not history
Requires years of historical data
$120K–$2M annually based on scale
$2–5M+ annually with usage-based surprises
No dedicated IT team required
Dedicated data engineering to maintain
Full platform at every tier
Features gated behind premium modules

What Merchants See

This is the actual Palette portal. Every card is a product. Every metric traces to a specific analytical finding.

Palette portal — In-Season pricing viewPalette portal — Events workbenchPalette portal — Review & Planning

Tap to expand

Explore the Demo

Simple pricing. Full platform.

Every tier gets the same intelligence. We don't charge for features. We charge based on the scale of the business we're serving.

Retailer TypeAnnual RevenueService Fees
Emerging$10M – $100M$120K
Growth$100M – $500M$300K
Enterprise$500M – $2B$600K
Premier$2B – $5B$1M
Flagship$5B – $10B$1.5M
Titan$10B +$2M

Every tier includes the full platform

No feature gating, no module upsells. No nickels, no dimes.

Weekly Analysis of Your Entire Assortment

Every SKU, every store, every attribute — pricing, sell-through, and rationalization in one pass. A comparable single-category review from a consultancy runs $75K–$200K and delivers a static snapshot. Palette does it every week, across all categories, and it gets sharper each cycle.

Explainable Recommendations

Every price, every keep/cut/review, every chase/maintain/exit decision traces to a specific reason. No black box.

Cross-Category Trend Analysis

Rising and fading attributes surfaced across your business, not siloed within departments.

Promotional Event Planning

Seasonal demand detection, product-level tier assignments, offer and timing optimization together.

Accumulating Intelligence

Elasticity estimates, track records, and attribute signals sharpen with every cycle thanks to Sherlock, our custom AI engine.

48 Hours of Realtime Mode

Hourly analysis and opt-in execution during your most critical selling windows.

Zero Data Retention

Your data is processed and returned. We keep the intelligence and signals we need, not your information. No lock-in.

Higher tiers add dedicated onboarding support, quarterly business reviews, priority roadmap influence, and named support contacts. Not additional features. Palette doesn't charge more for more intelligence.

2-Month Pilot — Try Before You Buy

2-month pilots start at $15K for Emerging retailers and scale with tier — the same proportional commitment at every level.

Week 1

Palette prices your assortment. Full reasoning on every recommendation. You execute.

Weeks 2–3

Results come in. Palette refines its estimates based on observed outcomes.

Weeks 4–8

Palette is learning your business. Recommendations sharpen. Trends emerge.

By the end of Week 8, you're
not evaluating Palette.
You're using it.

Your pilot investment applies in full toward your first annual contract.
The pilot isn't a test. It's the beginning.

Frequently Asked Questions

How long until we see results?

It depends on how quickly your team can provide data. There’s typically a short onboarding period — aligning on how data should be formatted, defining business logic like product hierarchies and subsets, and establishing your first weekly feed. For a pilot, that prep work usually takes a couple of weeks. For a full enterprise-wide implementation, expect 2–3 months. Once data is flowing, Palette produces actionable recommendations from the first submission.

What data do you need?

We only need a weekly snapshot: SKU details, sales, inventory, costs, attributes. Your ERP already has this. We provide a template to expedite things. However, we’re able to surface additional pieces of data — competitive pricing, review information, etc. — within the Product Cards where that would be helpful. It’s really up to you what you want to see to help you materially make decisions.

Do you store our data?

No. Your data is processed and returned. Palette retains derived intelligence — elasticity priors, track records, trend signals, among others — not your raw data.

What if we want to leave?

There’s nothing to migrate, unwind, or extract. Palette doesn’t embed itself in your systems. Every contract includes an early termination clause — if it’s not working, you can exit. Accumulated intelligence is deleted and you walk away clean.

How is this different from Revionics / Blue Yonder / PROS?

Those platforms require years of historical data, 6–18 months of implementation, and $2–5M annually. Palette works from day one, explains every recommendation, doesn’t warehouse your data, and costs a fraction — not because we do less, but because our architecture doesn’t need what theirs does.

Does this require IT involvement?

No. Palette ingests a weekly CSV — your team can upload it directly. No systems integration, no API setup, no IT project. If you want to automate the feed later, a scheduled export from your ERP is all it takes.

Does Palette integrate with our POS / ERP / planning system?

It doesn’t need to. Palette works from a flat file: SKU details, sales, inventory, costs, attributes. If your team can pull a report, Palette can run. Automated feeds are optional and straightforward.

What if Palette recommends something we disagree with?

Every recommendation comes with the evidence behind it — sell-through pace, elasticity, margin analysis, and constraint logic. You can accept, modify, or ignore any recommendation. Palette informs your decisions. It doesn’t make them for you.

How does Palette handle markdowns differently than our current process?

Palette evaluates sell-through pace, margin floors, competitive position, lifecycle urgency, and remaining weeks — simultaneously, for every SKU. Most manual processes optimize one variable at a time. Palette holds the full picture so your team doesn’t have to.

What size retailer is Palette built for?

Any retailer with 200 or more active SKUs and weekly sell-through data. The platform scales with assortment complexity, not company revenue. The pricing tiers reflect scope — number of SKUs, doors, and analytical depth — not artificial feature gates.

Can Palette handle multiple categories or divisions?

Yes. Palette evaluates products within whatever hierarchy you define — handbags, apparel, electronics, home goods. The methodology adapts to the assortment, not the other way around.

Is our data secure?

Data is transmitted over TLS, processed in isolated environments, and never commingled across clients. Palette retains analytical signals — elasticity priors, trend intelligence, track records — not your raw transaction data. When you stop, everything is deleted.

What Experts Say

“Palette transforms a complex, often opaque process into something clear, actionable, and highly strategic. It delivers both simplicity and sophistication — an uncommon but valuable combination.”

Steve Schnur

Head of Data and Analytics, Ascent Enterprise Solutions

25+ years in retail merchandise planning and pricing