Demand forecasting
SKU-level forecasts that fuse seasonality, promos and external signals — planners trust them because they can see why.
Industry
AI for Retail & eCommerce
Improve margin, inventory, and CX with AI grounded in your catalog, demand signals, and channels.
- 35%forecast accuracy improvement
- 70%of support contacts resolved by AI
- +8%margin lift from pricing intelligence
Capabilities
AI across the retail value chain
Forecasting, pricing and customer experience AI for retail brands.
Pricing intelligence
Competitive-aware pricing recommendations with margin guardrails and clear rationale.
CX and support agents
Order status, returns and product questions resolved end-to-end across channels.
Merchandising copilots
Assortment insights, product-content generation and catalog quality at scale.
How we deliver in retail
From signals to shelves to service
Retail AI wins on execution: forecasts planners act on, prices with guardrails, service that actually resolves.
Planning
Forecasts your planners stop overriding
Demand models that explain their drivers — weather, promos, trends — with planner copilots that answer 'why is this number what it is?' in plain language.
- SKU/location forecasts with driver attribution
- Promo and event signal fusion
- Planner Q&A over the forecast, in plain language
99.9%Uptime
1.2sp95 latency
$0.021Cost / task
QualityCostLatencyDrift
Customer experience
Service that resolves, pre- and post-purchase
Agents connected to orders, inventory and returns handle the full journey — where's my order, size questions, exchanges — and escalate gracefully with context.
- Order, delivery and returns actions — not just answers
- Product Q&A grounded in your catalog
- Peak-season scale without seasonal hiring
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Merchandising
Catalog quality at catalog scale
Product descriptions, attributes and SEO content generated and enriched consistently across tens of thousands of SKUs — on-brand, accurate and localized.
- Attribute extraction and enrichment from supplier data
- On-brand copy generation with human spot-checks
- Localization across markets and channels
Copilot
Draft renewal summary for Acme Corp
3 accounts show churn risk — view analysis
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Use cases
Where retailers deploy AI with us
Demand and inventory
Forecasting and replenishment that cut stockouts and markdowns.
Customer support automation
WISMO, returns and product questions resolved without queues.
Dynamic pricing
Competitive-aware pricing within margin and brand guardrails.
Product content at scale
Descriptions, attributes and SEO across the full catalog.
Personalized marketing
Segments, campaigns and creative variants driven by first-party data.
Store operations
Task prioritization and knowledge support for store teams.
Delivery
Industry-first delivery
Every engagement is grounded in your domain — regulations, operating models, and the use cases that actually move the needle.
- Domain-ready use cases
- Compliance-aware design
- Ops & CX impact
- Reusable industry playbooks
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1
Map the domain
Identify high-ROI workflows and risk boundaries.
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2
Prioritize use cases
Score impact, feasibility and data readiness.
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3
Pilot with guardrails
Ship a controlled production pilot fast.
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4
Scale across ops
Expand to adjacent processes with shared platforms.
Stockouts down 28%, markdowns down 19% in two seasons. The difference from past forecasting projects: planners can interrogate the number, so they actually use it.
Works with your stack
Works with your commerce stack
Commerce platforms, OMS, ERP and data stacks — integrated for real-time action, not just reporting.
- Shopify
- Salesforce Commerce
- SAP
- Manhattan
- Zendesk
- Klaviyo
- Snowflake
- Databricks
- AWS
- Anthropic Claude
- OpenAI
- Kafka
FAQ
Common questions
Peak season is brutal. Can AI handle Black Friday?
That's when it earns its keep — AI absorbs the volume spike that used to require seasonal hiring, and we load-test to your peak multiples before the season, not during.
Will automated pricing damage our brand?
Guardrails prevent that: margin floors, price-change velocity limits, MAP compliance and category rules. Recommendations come with rationale, and you choose what's automated versus suggested.
How do you improve forecasts beyond our current tooling?
Usually by fusing signals your current models ignore — promos, content changes, weather, social trends — and by making outputs explainable enough that planners stop overriding them. We benchmark against your incumbent before rollout.
Can support AI access our order systems?
Yes — that's the difference between deflection and resolution. Agents read and act on orders, shipments and returns through governed integrations with your OMS.
Next step
Turn retail data into retail results
Pick the season-killer — stockouts, support queues or content backlogs — and we'll attack it first.