Recommendations Engine
Supercharge Your Product with Intelligent Recommendations
Unlock the full potential of your AI initiatives with a platform designed to automate, monitor, and evolve your model operations—at scale.
Recommendations Engine
Recommendations Engine
LLM/Agentic
Workflow Setup
Workflow Setup
LLM/Agent
Experimentation
Experimentation
Evaluate for Accuracy / Cost / Latency etc.
FloTorch
Evaluation
Evaluation
Deploy to
Production
Production
Monitor Types of
Queries and Usage Patterns
Queries and Usage Patterns
Monitoring
Iterate
Recommend Experiment(s)
Recommend Experiment(s)
Benchmarking
Production
Observability and Security
Observability and Security
Benchmarking
Data
Data
.avif)
.avif)
Hyper-Personalization at Scale
Engineered for Modularity, Context-Aware Intelligence, and Scalable Adaptive Learning
Composable Retrieval and Ranking Pipelines
Supports hybrid (dense + sparse) retrieval strategies and customizable ranking models with plug-and-play orchestration — no need to rewrite logic per use case.
Agentic Workflow Orchestration
Built on FloTorch’s proprietary Agentic Workflow Manager, enabling modular, goal-driven recommendation pipelines with multi-agent collaboration and context memory.
Multimodal and Multi-turn Input Support
Understands and processes rich inputs — from product images and reviews to long-form queries — in multi-turn interaction flows.
Fine-Grained Context Injection with prompt partials
Ingests structured and unstructured context (user behavior, metadata, content embeddings) with token-efficient summarization and re-ranking at inference time.
Experiment Recommendation
Based on Benchmarking data, type of user queries and latency, accuracy etc parameters, FloTorch will recommend new experiments to conduct such that users can find out if the new experiment produces results that outperform existing production workload for accuracy, cost or latency. If the recommended experiment is successful, deploying it to production is just a click away.