High-Throughput Search and Recommendation Infrastructures
We build custom recommendation engines and vector search systems using Qdrant and specialized neural indexing frameworks. Optimized for high-intent queries including computer vision ecommerce product discovery at scale.
Computer vision ecommerce and high-intent query optimization with vector search
Generic keyword search leaves conversion on the table for high-intent queries. Pento designs vector search and recommendation infrastructures that map queries directly to Qdrant database implementations, enabling semantic product discovery, cross-sell relevance, and personalized ranking at high throughput.
We built a recommendation engine for an ecommerce platform with 2M+ SKUs. Add-to-cart rate up 34%. Fully live in 7 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 implementations and neural indexing for production search
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 that product discovery, from computer vision ecommerce to document retrieval, runs with low latency at scale. 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.
This helps us identify 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.
This includes how relevance signals, embeddings, and business constraints should 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.
Product recommendation AI: personalization at scale
From ecommerce discovery to internal knowledge retrieval, better search drives engagement and conversion.
Ecommerce product discovery and cross-sell recommendations that increase conversion
Semantic site search for content, documentation, and support that understands intent
Internal knowledge retrieval for teams and operations
Personalized ranking for feeds, listings, and marketplaces
Recommendation engines for catalogs, media, and learning platforms
Demand forecasting integrated with your recommendation layer
Pento combines practical machine learning experience with strong product engineering and data infrastructure expertise. We build 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 vector search or recommendation engines built on Qdrant and production-grade neural indexing, book a scoping call. We will review your catalog size, query patterns, and latency targets before designing the retrieval architecture.