ML · DL · NLP · Computer Vision · Generative AI · Agentic Development
Your use case isn't generic, so your models shouldn't be either. Extend is how we build the custom agents, models and reasoning your business needs — using YantrAI's own AI Engines and ML Core, not a from-scratch stack.
Six practices, one shared foundation: every model or agent we build for you draws on YantrAI's AI Engines (RAG, Text2SQL, Text2CLI, LangGraph) and ML Core, so it's never a one-off prototype.
Mathematical and statistical techniques that let models learn from your data automatically — across unsupervised, supervised, reinforcement, anomaly-detection and dimensionality-reduction methods. Example use cases: fraud detection in financial services, product recommendations in retail, predictive maintenance in manufacturing, and path/schedule optimization in logistics.
Statistical models and tooling for understanding data — the foundation for a large number of ML algorithms and use cases.
Interpreting ML results and answering counterfactual queries — building AI that is fair, interpretable, and goes beyond correlation to find causes.
Deep Learning mimics layers of neurons in the human brain to learn complex patterns in data — the "deep" refers to the large number of neuron layers in models that help achieve better performance, built on the same ML Core that powers YantrAI's anomaly and time-series models.
ANN, CNN and RNN architectures — large-scale neural networks typically referred to as deep neural networks.
Agents that learn to make decisions from live or historical data using algorithms inspired by brain neural networks — e.g. DQN, DDPG — powering robotics and automation.
NLP processes written and spoken natural language data to produce varied interpretations — powering virtual voice assistants, chatbots, and automated voice recognition and response systems, built on the platform's RAG and Text2SQL engines.
Chatbots for question answering; intelligent document processing for summarization and topic modelling; social media stream processing for entity handling and sentiment analysis.
Signal processing and data science combined for speech-to-text conversion and conversational AI.
Processing visual data such as images and video to extract complex information — powering autonomous vehicles, facial recognition for security, industrial quality control, and video analytics like intruder detection.
Object detection, optical character recognition (OCR), and medical imaging — spanning signal processing and data science.
Object detection and video analytics for real-time monitoring and insight.
Generative AI is a branch of deep learning that generates text, imagery, audio or code — built directly on YantrAI's Generative & Agentic Building Blocks (RAG, Text2SQL, Text2CLI, LangGraph), not stitched together from scratch per engagement. Example enterprise use cases:
With our enterprise Generative AI practice, we help build and deploy custom, reliable and secure solutions that achieve significant, tangible outcomes — from first MVP to scalable, enterprise-ready systems that reduce cost and time to value.
YantrAI ships with a core agentic framework — LangChain and LangGraph for orchestration, MCP for tool calling, human-in-the-loop built in — that already runs the platform's own agents. Extend is where we build on top of it: add-on agents scoped to your environment, your tools and your data, rather than a generic assistant bolted on afterward.
Purpose-built agents layered onto the core framework for your specific environment — wired to your tools, data sources and approval workflows using LangChain, LangGraph and MCP-based tool calling.
Locally hosted open-source LLMs (e.g. Llama, Mistral) where data residency, latency or cost rule out a hosted API — alongside hosted models where that's the better fit, on the same agent framework either way.
From first MVP to enterprise-scale deployment — let's talk about what Extend looks like for your use case.