Optimise user experience and appeal to a Global Audience

With Simplismart’s multimodal data ingestion and content generation capabilities, you can leverage your user research to make great improvements to your user experience. Save costs with all your ML workflows and improve user engagement with human-like chat and voice bots.

User Analysis

Simplismart lets you integrate ML-based user experience testing with your existing workflows to optimise layouts, user flows, and other user elements to improve your conversions.

Customer Support bot

Improve your customer experience with Simplismart voice enabled chatbots that can guide them through routine procedures like refunds or returns.

Generate copy for listings dynamically

Simplismart lets your deploy visual understanding and LLM models that can quickly create attractive copy for listings based on images and your SKU tags.

Sales Forecasting

Develop and implement AI models to generate precise sales forecasts, ensuring optimal inventory planning that drives revenue growth.

Rapid Auto-Scaling

Simplismart autoscales in under 60 seconds versus the industry average of around 5 minutes.

Total Data Security

Simplismart can be deployed on-prem, giving you complete data and model security.

Fast Deployment

We speed up GenAI model inference on the hardware layer, the serving layer and the model backend.

Unmatched Pricing

We optimise models using state-of-the-art techniques saving costs for both us and you.

Latest Updates from our Blog

Learn more about Simplismart and the ever-evolving ML/GenAI landscape. Our blog curates the latest news, updates, and tutorials that can keep you on the cutting edge.

Building a Llama 3 Chatbot with RAG (ft. Guardrails AI)

Learn how to build a secure, high-speed chatbot using RAG and Guardrails AI. This step-by-step guide covers document ingestion, contextual retrieval with LangChain, fast response generation via SimpliLLM, and input validation using Guardrails. Ideal for enterprises deploying AI for support, internal tools, or lead generation—without complex DevOps.

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A Beginner’s Guide to Quantization for Large Language Models (LLMs)

Large Language Models (LLMs) are complex and require a significant amount of computational resources to function effectively. They are designed to understand and generate human-like text, which involves processing vast amounts of data. This blog explores how Quantization can be an effective strategy to save compute time and costs while getting a similar quality.

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A primer on optimising Large Language Models (LLMs) for higher inference speeds

Explore how Simplismart accelerates open-source LLM inference using techniques like batching, quantization, speculative decoding, and flash attention—boosting throughput by 30x while optimizing for cost, speed, and real-time performance.

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Latest Updates from our Blog

Building a Llama 3 Chatbot with RAG (ft. Guardrails AI)

A Beginner’s Guide to Quantization for Large Language Models (LLMs)

A primer on optimising Large Language Models (LLMs) for higher inference speeds

Transform MLOps