An AI assistant that opens the conversation and asks the right question
Conversational AI for abandoned-cart recovery, upsell, post-purchase engagement, and inactive-customer re-activation. One RAG engine (it looks things up before it replies), applied to sales scenarios as well as support.
Discuss a pilot →Quick facts
- Business size
- E-commerce / marketplaces with > 5k SKUs and meaningful traffic
- Timeline
- 4-6 weeks test, 2-4 months pilot, 4-6 months production
- Budget range
- Pilot from €30k.
- Hardware
- Cloud-based.
- Data needed
- Product catalogue, customer order history, abandoned-cart logs.
- Evolution
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- Genesis
- Custom-built
- Product
- Commodity
No product gives you this. We assemble and train it around you.
What this scale means
Further right means more proven and cheaper. Further left means newer and riskier. Here is the test for each step.
- Commodity
- You could get the result yourself from a ready service, with almost no work. We rarely take these on.
- Product
- A vendor already sells this result turnkey, like shelf recognition from Trax or document reading from ABBYY. If one of them fits you, use it. You come to us when it does not: when it has to run on your own servers, cost less, or fit systems the product cannot reach.
- Custom-built
- Vendors sell only the parts. A tool like Tableau hands you charts, but the dashboards and metrics for your business still have to be built. That build is the work, and that is us.
- Genesis
- The approach exists but does not work reliably yet. You are betting on it maturing, so it costs more and carries more risk.
Expected outcomes
The Problem
Static abandoned-cart emails and generic upsell pushes have hit a wall. Open rates trend down; the templates are tired; customers ignore them. Sales teams know there’s signal in cart-abandonment, post-purchase, and inactivity, but conventional automation can’t act on it conversationally.
A proactive AI sales bot changes the cadence. The old approach sends “you left items in your cart” to everyone. The bot opens a personalized conversation: “I noticed you looked at the Aurora 2L pendant, were you comparing it to the Stella version? They differ on…”. The conversation is grounded in the catalogue and the customer’s history, with citations the customer can verify.
What the Solution Does
A conversational AI optimized for outbound and re-engagement scenarios:
- Abandoned-cart recovery, chat with customers who left, identify why, suggest alternatives or address concerns.
- Upsell / cross-sell, during or after browsing, suggest complementary products.
- Post-purchase engagement, confirm satisfaction, suggest accessories, encourage reviews.
- Inactive-customer re-activation, re-engage customers who haven’t shopped recently with relevant catalogue updates.
- Multi-channel, web chat, email, SMS, messenger platforms.
Where It Fits
This makes sense if you…
- Operate e-commerce / marketplace with > 5k SKUs
- Have meaningful traffic with measurable cart abandonment / re-engagement opportunity
- Have customer order history to personalize
- Already invest in ecommerce-product-assistant or similar foundational AI
This is probably not the right time if you…
- Have small SKU count where personalization isn’t valuable
- Lack customer history data
- Operate in B2B contexts where mass-automated outbound feels wrong
- Don’t have the multi-channel infrastructure
Business Value
Recovery rate improvement. Conversational engagement on abandoned carts typically lifts recovery rate 10-25 percentage points over static email. Context-aware messaging lands better than a generic template, because it speaks to what the customer actually looked at.
AOV uplift. Upsell conversations grounded in real catalogue knowledge surface relevant complementary items. AOV lift on engaged sessions is typically 5-15%.
Engagement metrics. Open and response rates on bot-driven outbound run typically 30-60% higher than static templates.
Channel-flexibility. One engine, multiple channels. Customer interaction history persists across channels, so each new message has the prior context.
How It Works
1. Trigger detection
Cart-abandoned: triggered by session inactivity. Post-purchase: triggered by order confirmation. Re-activation: triggered by inactivity threshold.
2. Personalization context
Customer history (past orders, browsed items), product catalogue, cart contents, current promotions, all flow into the conversation context.
3. Conversational LLM
The bot opens with a personalized message and continues in natural conversation. RAG over catalogue (like ecommerce-product-assistant) handles questions; the upsell logic suggests alternatives.
4. Multi-channel surface
Email, SMS, WhatsApp, web chat, messenger platforms. Conversation context persists across channels.
5. Conversion tracking
Each conversation linked to outcomes, recovered cart, additional purchase, re-engaged visit. A/B testable against control (static template baseline).
Stack
OpenAI, Anthropic, or self-hosted LLMs. RAG over the product catalogue (shared with ecommerce-product-assistant). Customer-history embeddings. Multi-channel delivery infrastructure. Datapipe for tracking and retraining.
What You Need to Make This Work
Data. Product catalogue, customer order history, abandoned-cart event log.
Integrations. Channel delivery (email gateway, SMS provider, messenger platforms). Tracking pixels for outcome measurement.
Hardware. Cloud-based.
Team. Marketing / sales lead. Data engineer.
Implementation Roadmap
1. Test (4-6 weeks)
Pick one scenario (cart-abandoned typically). Build the bot with personalization. A/B test against control. Output: a working bot with measured uplift.
2. Pilot (2-4 months)
Production deployment for the priority scenarios. Multi-channel delivery. Tune conversational tone and timing. Output: a working deployment with documented outcomes.
3. Production (4-6 months)
Full scenario coverage. Continuous improvement.
Keep in Mind
- Tone matters. A pushy bot drives customers away. Helpful and light-touch is the goal.
- Frequency caps. Customers don’t want daily AI messages. Cap-and-pace logic is part of the design.
- GDPR and privacy. Outbound messaging has consent rules per jurisdiction. We design with compliance built in.
- A/B against control matters. Without measurement, claims of “the bot worked” are guesses. We deploy with measurement infrastructure.
- Catalogue accuracy is the ceiling. Wrong product recommendations damage trust. The accuracy concerns from ecommerce-product-assistant apply here too.
FAQ
Email or chat, which channel performs best?
Depends on customer base and product category. We typically pilot email first (lower friction); chat / messenger when there’s clear customer preference.
Can the bot complete the purchase?
Yes, with appropriate guard rails. Some operations let the bot handle through-checkout; others route the customer to the standard purchase flow.
Does this need integration with our marketing automation?
Helpful but not required. The bot can run standalone or alongside existing marketing automation (HubSpot, Klaviyo, etc.).
Ready to Discuss?
If you operate e-commerce with meaningful abandonment / re-engagement opportunity, this is a fast pilot. We’ll walk through your channels and your data, and tell you what to expect.