Semantic search engines, personalized recommendation systems, intelligent data pipelines and MLOps — every layer of your search stack, built to perform in production.
From search engine to fine-tuned model — each building block is designed to work together and improve over time.
Vector indexing of your product catalogs, knowledge bases and content. Hybrid search (vector + BM25) for relevant results from the first word, even with typos or vague formulations.
Collaborative filtering, content-based filtering and hybrid approaches to surface the right product or service to the right person at the right time. Integrated directly into your interface via API with automated A/B testing.
Complete ETL architecture with CDC (Change Data Capture), real-time synchronization between your data sources and data warehouse, and MCP layer to query your data in plain language from any tool.
Training AI models on your proprietary data, RLHF, deployment on your infrastructure or ours (GCP, AWS, Azure), continuous performance monitoring and automated retraining based on detected drift.
From the initial audit to production deployment, here is how we build and deliver your solution.
Inventory of your data sources, current search queries, conversion metrics and existing models.
Design of vector indexes, recommendation engine and MLOps pipeline adapted to your stack.
API integration into your product, A/B testing framework setup and relevance metrics.
Continuous performance monitoring, drift detection and automated model retraining.
Describe your use case and we'll propose an architecture tailored to your stack and goals. Response within 24h.