HIVERY
AI-driven, store-level assortment and space optimization for retailers and CPG brands.
By HIVERY · 4.0/5 Value-Position score (estimate)
Positioning guardrails
Best for
- Retailers and CPG category managers optimizing assortment, space and planograms store by store
- Merchandising, space-planning and revenue growth management teams that want rapid scenario simulation
- Chains with rich store-level or SKU-level sales data ready to move beyond cluster averages
- Brands selling through grocery, mass, convenience and drug channels
Ideal size: Category, space and merchandising teams in 50+ store… people · Data-mature retailer or CPG brand with store-level sales data
Not for
- Small chains or single-store operators with little store-level transaction data
- Pure-play e-commerce merchants with no physical shelf or planogram constraints
- Buyers who need published, self-serve pricing before a sales conversation
- Teams looking for a full merchandising execution or supply-chain suite rather than decision analytics
Value metrics scorecard
Time-to-Value
~3 months from pilot to deployment
~90 days to first production value
Total Cost of Ownership
On request
Not published; enterprise, quote-based
Implementation Friction
4/5
Engineering + admin effort required
Value-Position score
out of 5 · model estimate
Full cost breakdown
Mandatory implementation fee
None
Seat tiers
Not listed
Add-on costs
- None
Company & support
Who is behind HIVERY, and how your team gets help once it is live.
Company
- Founded
- 2015 · 11 yrs in business
- Headquarters
- Not recorded
How you get support
We haven’t recorded support channels for HIVERY yet. Nothing here means unverified — not absent.
Market position
Where HIVERY sits against its closest alternatives. Pick any two of cost, speed, friction and buyer score, and up to 9 companies to compare.
Quadrant view
Typical annual cost × Time-to-value
The lines cross at the median of the solutions shown, so about half sit on each side of each line.
Companies on the chart 6 / 10
- HIVERY
- invent.ai
- Bazaarvoice
- Numa
- Searchspring
- Lily AI
Add or change companies
Up to 10 companies including HIVERY. Listed closest first.
Stack fit signal
Compatibility with standard B2B ecosystems.
No supported MCP path today, so it cannot be driven from an AI client.
AI & MCP readiness
What HIVERY ships in AI, and what it asks of your ecosystem.
AI features shipped
Curate applies proprietary machine learning and applied mathematics to store-level sales data, forecasting the revenue impact of assortment and space decisions and prescribing store-specific assortments. The vendor describes 'AI as your co-pilot' for decision-making, but publishes no detail on underlying model providers, customer-supplied keys or AI audit logging.
Your data & models
- Trains on your data
- Not recorded — ask the vendor
- Runs on
- Not recorded
- AI pricing
- Not recorded
In your ecosystem
- AI connection
- Not supported
- Model key
- Not recorded
- AI usage audit
- Not recorded
Compliance attestations
* IAPP AIGP certifies individuals, not products. It means named staff hold the credential — not that the platform does.
Bottom line
HIVERY Curate is an AI/ML assortment and space optimization platform for retailers and CPG brands. It applies store-level sales data and proprietary machine-learning models to simulate and prescribe assortment, space and planogram decisions store by store, predicting revenue impact in minutes. Founded in 2015 out of Coca-Cola's Founders Program, the company reports 110+ retailer-category combinations and work with large CPG brands. Pricing, support SLAs and security certifications are not published on the vendor site.
Frequently asked questions
What does HIVERY Curate actually do?
Curate is a hyper-local category simulation and optimization platform. Using store-level sales data and machine-learning models, it simulates and prescribes the ideal assortment and shelf space allocation per store and predicts the revenue impact of assortment decisions within minutes.
How long does it take to get into production?
HIVERY describes a discover, pilot, deploy methodology: discovery of objectives and available data, a pilot in which models are trained and run as live experiments, then full deployment with an operational plan and quarterly reviews. Buyers should plan on roughly a quarter from kickoff to production value.
Is pricing published?
No. The vendor site does not publish list pricing, seat tiers or implementation fees. Expect a sales-led, quote-based enterprise commercial process, so budget for a discovery and business-case phase before signing.
What data do we need to get value?
The platform is built on granular, store-level sales and item data, which it uses to model shopper demand and transferable demand. Retailers and brands with clean SKU-by-store transaction history will get results fastest; aggregated national or cluster-level data limits what can be simulated.
Which retailers or brands already use it?
HIVERY states it operates across more than 110 retailer-category combinations and works with the top 25 to 50 CPG brands. It has been recognised by CB Insights and Fast Company, but it does not publish named customer references on the pages reviewed.
Does HIVERY use our data to train its models?
The vendor pages reviewed do not state whether customer data is used to train or improve models, or whether customers can opt out. HIVERY says models are created and trained during the pilot, so this should be clarified contractually and in a security review before adoption.