Repeated sizing uncertainty
- Business implication
- Validate fit guidance against product specification
- Action owner
- Development and digital product
Digital commerce & conversion
Build a shopping assistant that helps customers choose.
Complex ranges leave shoppers unsure which product fits their needs.
TriggerA shopper asks for advice or compares products.
- Shopper
I need a washable bag for a weekend trip. Will the compact weekender fit in the cabin?
- Shopping assistant
Which airline or size limit do you need it to meet? The compact weekender’s approved care record says spot-clean only, so it would not meet your machine-washable requirement.
- Shopper
Machine washable matters more. Can you suggest an alternative?
- Shopping assistant
The washable tote is a supported alternative: its approved care record permits machine washing, and the current stock record shows the navy variant available. The stone variant is unavailable. I can show the tote’s dimensions and current price; I would need your airline’s limits before confirming cabin fit.
Grounded in product information
- Care instructions and dimensions come from the approved product records.
- Variant availability and price are checked against the current selling source.
- Cabin fit remains unconfirmed until the customer supplies the relevant size limit.
What you getA product-grounded guided-shopping experience with useful next actions.
Discuss this use case How it works
Led by operators with 15+ years building commerce products, teams and systems. About us
Today
Shoppers face a complex range and ask questions the product grid cannot answer. Staff or a generic chatbot must piece together product fit.
With this in place
A shopping experience asks useful questions, recommends eligible products from approved facts and explains the next step.
Who does what, step by step.
Your team
Your team defines product claims and service boundaries and handles questions the available evidence cannot answer.
AI
AI asks about the shopper’s needs, compares eligible products and explains the recommendation.
Automation
Integrations retrieve approved product facts and current price, stock and channel eligibility.
Step 1: AI
Understand the buying decision
Ask which needs and constraints matter, including any missing requirements needed to assess product fit.
Step 2: Automation
Check approved product facts
Check approved care instructions, dimensions and compatibility, then retrieve current price and variant availability.
Step 3: AI
Recommend with reasons
Explain which eligible products fit the stated needs, why an alternative may be unsuitable, and the useful comparison or purchase next step.
Step 4: Your team
Resolve or hand off uncertainty
Ask for the missing information before confirming fit. Route specialist or unresolved requests to the appropriate customer-service path.
Guardrails
- Do not invent compatibility, claims or specifications.
- Retrieve current stock and price before recommending a purchasable item.
- Route specialist or unresolved requests to the appropriate customer-service path.
What a first project includes.
A bounded first build around your systems and team. We agree the scope, fee and baseline before we start.
We build
- An embedded guided-shopping experience in the existing storefront or app
- A product-grounding and eligibility layer
- A review process for answers, unresolved questions and changed product facts
We start from
- Approved product facts and compatibility rules
- Current price, availability and channel eligibility
- Representative buying questions and customer support boundaries
From your team
- Owner
- Head of Ecommerce
- Digital Product Lead
- Involves
- Digital product
- Catalog
- Design
- Engineering
- Customer service
Access to these systems and real examples to test against.
Adapts to
- Web commerce
- Mobile apps
- Complex product ranges
We’d measure
- Recommendation fit and source accuracy on representative questions
- Useful customer next actions and assisted purchase completion
- Operating cost and unanswered-question trends
Baselines agreed before we build.
Questions teams ask.
- Do we need to replace our current tools?
Usually not. We start with the systems you already use and build only what’s missing.
Typical connections: Your commerce platform, analytics, site search, reviews and testing tools.
- Who owns it once it’s running?
Your team does. You own the agreed deliverables under the engagement terms, with documentation and training for the people who run it. Ownership usually sits with your head of ecommerce or digital product lead.
Ongoing support is optional and agreed separately.
- How do we get started?
If the problem and scope are already clear, we can scope this build directly. If you’re weighing it against other priorities, start with Discovery & Action Plan.
Its fee is credited in full if we build the recommended work.
How discovery works
Often paired with.
Part of AI shopping & agentic commerce
- Growth & acquisition
AI shopping readiness
Products are difficult for shopping agents to find or understand.
Channel-readiness assessment, corrected product feeds and an update process.
- Catalog & product information
Supplier data to product records
New products wait while teams rekey inconsistent supplier information.
Structured product records with source references and exceptions to resolve.
- Customer service & support
Customer intelligence into action
Customer feedback never consistently reaches the teams that can act on it.
Evidence-backed themes, owners and action briefs.
More connected work
Help every shopper find the right product. Start with one complex category.
Prefer to start with discovery?
We reply within one business day.