AI Search for Ecommerce and Recommendation Systems Built for Conversion
We build AI search for ecommerce and custom recommendation systems on Qdrant and specialized neural indexing frameworks. Semantic product discovery, cross-sell relevance, and personalized ranking at high throughput.
Ecommerce search that understands buyer intent
Generic keyword matching leaves conversion on the table. A customer who types "warm jacket for hiking in the rain" gets zero results, while your catalog holds twenty products that fit. Pento designs ecommerce search that maps buyer intent to your catalog semantically, so high-intent queries land on the right products, cross-sells surface at the right moment, and ranking reflects both relevance and your business rules.
We built a recommendation engine for an ecommerce platform with more than 2 million SKUs. Add-to-cart rate up 34 percent. Fully live in seven weeks.
What you get
- Deployed recommendation API integrated with your product catalog
- A/B testing setup to measure lift against your current baseline
- Reranking logic you can tune as business rules change
Qdrant vector search and neural indexing for production
Pento's approach maps your catalog, content, and user behavior directly to a Qdrant vector database. We design the embedding pipeline, index schema, and query routing layer so product discovery runs with low latency at scale, whether the query is text, an image, or both. Every implementation includes drift monitoring for embedding quality and retrieval precision.
How we build and deploy search and recommendation systems
Discovery and relevance assessment
We start by reviewing your catalog, content sources, user behavior, business rules, and current discovery experience.
That review shows us where users drop off and where recommendations can create measurable value.
Retrieval, ranking, and personalization design
Next, we design the architecture for indexing, semantic search, ranking features, recommendation logic, and experimentation.
The design spells out how relevance signals, embeddings, and business constraints work together.
Pilot and relevance validation
Before scaling broadly, we validate the experience with pilot implementations and offline or live evaluation.
We measure ranking quality, click-through behavior, conversion impact, latency, and edge cases.
Production integration and optimization
After validation, Pento integrates the solution into your product, content platform, or internal tools.
We support APIs, experimentation, analytics, and monitoring so your team can manage relevance over time.
AI product discovery and personalized ranking at scale
From storefront discovery to internal knowledge retrieval, better search drives engagement and conversion.
Ecommerce product discovery and cross-sell recommendations that increase conversion
Semantic search for ecommerce catalogs, content, and support that understands intent
Visual search: customers find products from an image, not a description
Personalized ranking for feeds, listings, and marketplaces
Internal knowledge retrieval for teams and operations
Recommendation system development that balances relevance and revenue
Pento combines practical machine learning experience with strong product engineering and data infrastructure expertise. We build recommendation systems that improve relevance without losing sight of latency, governance, or commercial outcomes.
Clients choose Pento because we provide:
Frequently Asked Questions
Contact us
Ready to improve discovery and conversion?
If your organization needs AI search for ecommerce or a recommendation system that holds up in production, book a scoping call. We will review your catalog size, query patterns, and latency targets before designing the retrieval architecture.