Executive Summary
From generic chat widget to a governed, guided-selling operating model
The client's highest-margin products required the kind of guided, consultative selling that in-store staff provided naturally, but that experience simply did not exist online. I led the architecture, build, and governance program for an avatar-based assistant that replaced a generic, inconsistent chat widget with brand-safe guided discovery and explainable recommendations — bringing premium, high-touch selling to every visitor, at every hour, without adding always-on staff.
The Business Problem
The client's highest-value SKUs were high-consideration purchases that customers historically relied on in-store staff to evaluate — comparing options, resolving objections, and building confidence before committing. Online, that guidance was reduced to a generic chat widget that gave inconsistent answers, could not explain its recommendations, and often escalated to a human agent who wasn't always available, creating friction at exactly the moment a customer needed reassurance.
A baseline audit at engagement kickoff quantified the gap: cart abandonment on high-consideration SKUs ran at 71%, well above the client's templated-product baseline, average time-to-decision exceeded nine minutes with no guided path to a recommendation, and post-purchase support tickets citing "wrong product for my needs" were a top-five contact driver. Leadership needed a way to replicate consultative, trustworthy guidance online at scale, without the brand risk of an assistant that could go off-message or make unexplainable recommendations.
The Solution
I designed and delivered an avatar-based shopping assistant that asks lightweight, guided questions to capture intent and preferences, then produces explainable product recommendations with clear reasons and side-by-side comparisons. The assistant handles objections and comparison questions within a governed, policy-safe response set, and is wrapped in a premium interface built to match the brand's in-store experience rather than a generic support widget.
Every recommendation is explainable and traceable back to the customer's stated preferences, and every response the assistant can give is bounded by brand and policy guardrails defined jointly with merchandising and brand leadership. That governance — not just the avatar interface — was what earned trust from a brand team that had previously resisted AI-driven customer-facing experiences.
How It Works
Discovery
Guided questions capture genuine intent and preferences in the same natural flow as an in-store consultation.
Recommendations
Product ranking with explainable reasons and side-by-side comparisons, so every suggestion earns customer trust.
Experience
A premium avatar interface aligned to brand expectations, designed for speed, trust, and always-on availability.
Architecture & Governance
The diagram below traces how a customer's stated preferences become an explainable recommendation, with every response bounded by a brand-safety and policy checkpoint before it ever reaches the customer.
System Architecture — Discovery-to-Recommendation Flow
Five stages · one mandatory brand-safety checkpoint
Lightweight, conversational questions capture genuine intent, use case, and preferences
Structured intent signals mapped against live catalog metadata and merchandising rules
Explainable ranking over inventory, pricing, and product content, not a static lookup table
Every response bounded by brand-approved answers to objections, comparisons, and edge cases
Delivered in the premium avatar interface with clear, traceable reasons for every suggestion
Funnel & intent analytics run beneath every stage — capturing behavior and preference signals that continuously refine the discovery flow.
- Conversation layer tuned specifically for commerce discovery, with policy-safe outputs bounded by brand and merchandising rules.
- Product retrieval and ranking built over live catalog metadata and merchandising rules, not a static recommendation table.
- Integration points into inventory, pricing, and product content systems, keeping every recommendation current and purchasable.
- Analytics hooks capturing funnel behavior and intent signals, feeding continuous improvement of the discovery flow.
- Governance controls: explainable recommendation logic and a bounded, brand-approved response set for objections and comparisons.
Business Impact & ROI
- Cut cart abandonment on high-consideration SKUs from 71% to 44% by replacing generic chat with guided, explainable discovery.
- Lifted average order value 24% on assistant-guided sessions as customers moved more confidently toward higher-fit, higher-value options.
- Reduced time-to-decision from over nine minutes to under four by giving customers a structured, consultative path to a recommendation.
- Delivered a fully costed ROI model presented to the executive team: total program investment recovered in approximately 10 weeks of production use, driven by conversion lift and AOV growth, with a 4.3x return in year one.
- Reduced "wrong product for my needs" support contacts by pairing customers with better-fit recommendations before purchase.
- Gave the brand team a governed, explainable AI customer experience they could stand behind publicly — resolving the trust concern that had stalled prior AI initiatives.
My Role
I owned this engagement end-to-end as the client's AI/ML product leader, operating at CTO scope: I built the business case and ROI model presented to the executive team, defined the discovery flows and governance guardrails, and aligned recommendation logic to brand and merchandising rules that had to hold up under public scrutiny.
I also owned the integrations required for a trustworthy premium experience — inventory, pricing, and product content — and led the phased category rollout that let the brand team validate trust and performance before expanding to the full catalog. The result was a governed, explainable guided-selling capability the client's leadership could scale with confidence rather than a customer-facing AI experiment.