One containerised workspace for data labeling, model training, LLM fine-tuning, and production deployment — on-premise or cloud. Built for data scientists and ML engineers who ship.
Ingest images, video, text, audio, and sensor data into a unified repository. Collaborative labeling, automated quality analysis, and full dataset versioning.
Automated and manual feature pipelines with a centralised Feature Store. Consistent features across training and inference — eliminating training-serving skew.
Distributed GPU training for DL and LLM workloads. Automated hyperparameter tuning, experiment tracking, RLHF pipelines, and LoRA/QLoRA fine-tuning.
One-click deploy to cloud, on-premise, or edge. ONNX and TensorRT optimisation. Canary deployments, A/B testing, and automated rollbacks.
Continuous drift detection, accuracy monitoring, and anomaly alerts. Automated retraining triggers with governed promotion pipelines.
Purpose-built for computer vision, NLP, and multimodal deep learning. Every pipeline stage optimised for GPU-accelerated, large-model workflows.
End-to-end LLM infrastructure: dataset curation, RLHF, prompt versioning, evaluation benchmarks, and inference optimisation — all governed and auditable.
Build autonomous agents on ADVIT's LLMOps layer. RAG pipeline management, tool-use orchestration, agent memory, and multi-agent coordination with full observability.
Integrated labeling with polygon, bounding box, and segmentation tools. Synthetic data generation. Classification, detection, segmentation, and pose estimation.
Optimise and deploy to NVIDIA Jetson, TensorRT, and custom edge hardware. Quantisation, pruning, and compilation for real-time inference.
RBAC, AES-256 encryption, TLS 1.3, DPDP compliance, audit logging, data residency controls. Air-gapped deployment for defence and government.
Train with the tools your team already knows. ADVIT abstracts the infrastructure layer.
Cloud, on-premise, edge, or hybrid. Containerised via Docker/Kubernetes with GPU pass-through.
Connect to your existing data infrastructure without migration overhead.
Programmatic access for CI/CD integration and notebook-native development workflows.
Custom vision models trained on labelled defect images, optimised for edge inference, deployed to on-premise cameras with automated retraining.
Multi-spectral segmentation models trained at scale with GPU-accelerated pipelines. Continuous monitoring deployments for land classification and change detection.
Vehicle detection, speed estimation, and incident identification across highway camera networks. Edge-deployed for real-time alerting.
GPU-accelerated, large-model workflows are first-class citizens, not plugins bolted onto a tabular ML tool.
One platform from raw data ingestion to production monitoring. One governance layer. One audit trail. Not 6 open-source tools held together by scripts.
Your models, your data, your infrastructure. Runs in air-gapped environments, government data centres, and factory-floor edge servers.
Designed and built in Pune. <2-hour response SLAs. Eligible for government procurement under Atmanirbhar Bharat.
Request a live demo on your infrastructure. We'll scope a pilot in under 2 weeks.
Or write to us at info@automatonai.com