Predictive · Generative · Agentic
One platform spanning the full enterprise AI lifecycle — from training classical machine-learning models to deploying generative AI applications and orchestrating autonomous agents.
Most teams stitch together a dozen tools to train a model, another stack for retrieval-augmented generation, and yet another for agents. Each layer brings its own data formats, deployment targets, and operational risk.
Many tools, weak seams. Separate stacks for classical ML, GenAI, and agents — with brittle integrations between them.
Hard to audit and control. Data lineage, model versions, and prompt history scattered across systems.
Rigid where you need flex. Many tools assume one cloud, one runtime, one workload class.
Unified data, model, and orchestration layer.
Modes — predictive, generative, agentic — under one roof.
Deployment — edge, cloud, on-prem, HPC.
Train, tune, and serve supervised models, time-series forecasts, anomaly detectors, reinforcement-learning agents, and causal-inference models — distributed across GPUs.
Build retrieval-augmented LLM applications on your own data with production-ready pipelines, vector databases, guardrails, and self-hosted open-source models.
Compose AI agents that perceive, reason, call tools through MCP, and take action — with human-in-the-loop where you need it.
Pre-built solutions and custom enterprise apps.
RAG Engine, Text2SQL, Voice agents, MCP server & client.
Distributed training, serving, model store, evaluation.
Batch & streaming pipelines, multi-store data lake.
Cloud-native deployment across edge, cloud, on-prem, HPC.
Composed on AWS, Azure, GCP or on-prem — sized to the use case you’re deploying, not a fixed template.
The same workloads run identically across deployment targets, so you can stage models on the cloud, serve them at the edge, and train them in HPC — without re-engineering the stack. GPU-accelerated throughout — NVIDIA CUDA, cuDNN, and TensorRT integrated end-to-end.
Data stays inside. For regulated industries or data-sovereignty needs. Same platform, your hardware, your control plane.
Elastic and managed. Run on AWS, Azure, or GCP with Terraform-managed infrastructure. Scale up training, scale down inference.
Low-latency at the source. Deploy inference services close to where data is generated — IoT gateways, retail sites, network points-of-presence.
When models get massive. Integrates with Slurm and HPC workload managers for large-scale distributed training of foundation models.
Agentic NOC for telecom networks. Ingests and correlates cross-vendor alarms, diagnoses root cause, and remediates — from live alarm to closed incident, automatically.
Propensity, churn, fraud, and credit scoring on a unified platform. Combines classical predictive models with generative document-extraction.
Computer-vision and behavioral analytics for physical spaces. Identifies patterns, generates summaries, and triggers downstream automations.
Document assistants, knowledge agents, conversational interfaces — built on your data with hosted open-source LLMs and full guardrails.
Distributed across GPUs with model, data, and pipeline parallelism.
Inference at scale — single-node, distributed, or edge.
Batch and streaming pipelines across SQL, vector, time-series, and file stores.
Production RAG pipelines with vector DB integration and guardrails.
MCP-based tool calling, LangGraph workflows, human-in-the-loop.
Versioning, audit, evaluation, and rollback — for every model and prompt.
Let’s talk about where YantrAI fits into your AI roadmap.