We focus on measurable business results, not just tech delivery.
Faster execution with 10x deployment improvement and 80% automation.
Trusted by top players across telecom, legal, sustainability & media.
AI, Cloud, and Product Engineering under one roof.
Operating across India, UAE & SEA with contextual delivery.
Backed by partnerships with AWS, IBM, Microsoft, and VMware.
At Revdau.ai, we bring together innovation and execution to build meaningful solutions for the modern enterprise.
We blend velocity, trust, quality, and relevance to create solutions that power the modern enterprise.
We build solutions that matter, deeply relevant to our customers' realities, adaptable to their growth, and focused on lasting outcomes.
At Revdau.ai, our name tells our story. Each letter stands for the principles that drive us and the mission we’re committed to every day.
Deeply aligned with real-world needs and evolving markets.
Transparency, inclusivity, accountability, and fairness.
Fast and purposeful in everything we deliver.
We challenge convention and innovate with intent.
We adapt quickly and stay responsive to change.
Users are at the heart of every decision we make.
Deployed a hybrid CPU-GPU architecture, with Kafka, MongoDB, and Redis for ingestion and queues, running FFmpeg/Librosa and OpenAI Whisper, orchestrated via Apache Airflow.
Deployed Logstash for multi-source data collection, XGBoost and MLP models on PyTorch for fault prediction, LangChain AI agent, Ansible automation, and Airflow orchestration.
Integrated ML with CRM to automate churn segmentation, recommend retention actions, and enable real-time scoring.
Deployed a 60–70% accurate prediction model using TRP and VRP algorithms, integrated via Google APIs, with SHAP interpretability and real-time ELK data pipeline.
Deployed a RAG using LangChain, structured digital library data for AI, trained LLM on Indian legal datasets, tuned vector databases with MMR, and built real-time ELK pipeline.
Trusted By Tech-First Enterprises
The perfect balance of automation and expertise.
Reduction in AI deployment time
Improvement in enterprise AI adoption rates
Decrease in AI operational costs
