Founded in 2018, we focus exclusively on enabling Enterprise AI @ scale. In a very short span of time, we have become one of the fastest growing companies in America.
We focus on questions that matter to businesses with big ambitions, empowering them to elevate outcomes across their value chain.
Mid-level Senior
Mumbai
Mid-level
Remote
Generative AI Execution for Real Enterprise Use
Most enterprises don’t struggle to get started with Generative AI. They struggle when GenAI starts interacting with real enterprise data, real users, and real decisions.
Early pilots often perform well in controlled environments. But once they move closer to production, the gaps show up quickly:
At this stage, GenAI stops being a technical experiment and becomes an operational risk.
AiroGenix is designed for this transition.
It is an execution-led engagement focused on hardening GenAI for production. The work centers on how GenAI is built, grounded, evaluated, and governed so it can scale across teams and use cases without breaking trust.
The goal is not to make GenAI impressive in demos, but dependable in day-to-day business use.
Ground GenAI outputs in enterprise knowledge using well-architected RAG pipelines. We design ingestion, embedding, retrieval, and relevance tuning to deliver accurate, context-aware responses.
Move beyond single-prompt interactions by enabling agent-based collaboration. Specialized agents retrieve, reason, validate, and act—supporting more complex and reliable GenAI
Prepare high-quality inputs for GenAI through structured annotation, enrichment, and knowledge organization—improving accuracy, relevance, and downstream performance.
Adapt foundation models to your domain and use cases using fine-tuning, prompt optimization, and evaluation frameworks—balancing performance, cost, and control.
Embed GenAI directly into enterprise workflows by integrating with applications, data platforms, APIs, and automation tools—turning insights into action.
Design evaluation frameworks, feedback loops, and lifecycle controls to continuously measure accuracy, relevance, and adoption as GenAI
Pre-built pipelines for ingestion, chunking, embedding, and retrieval—reducing time to deploy grounded GenAI applications.
Reusable orchestration patterns and agent templates that accelerate development of reasoning-driven GenAI systems.
Automated and human-in-the-loop frameworks to prepare, enrich, and validate GenAI data at scale.
Proven approaches for tuning, evaluating, and optimizing models to ensure reliable, predictable performance.
A repeatable delivery model to deploy production-ready GenAI use cases in weeks using standardized architectures.
Pre-configured guardrails for prompt control, data access, output validation, and monitoring—reducing risk while improving enterprise adoption.
Deployed an AiroGenix-hardened RAG pipeline to ground LLMs in the bank’s private institutional knowledge and regulatory data. We deployed specialized Agentic AI to autonomously retrieve data, extract key risk indicators, and draft comprehensive credit summaries.
Leveraging our Rapid AI Factory, we built a multi-channel loyalty engine using LLMs to analyze consumption patterns and sentiment. The system autonomously generates personalized energy-saving tips and tailored discount offers via email and text.
We deployed the AiroGenix RAG pipeline to synthesize information from thousands of PDFs, maintenance logs, and sensor data. frontline operators were equipped with a multimodal voice assistant that provides real-time, context-aware instructions for mechanical resets.
faster GenAI deployment
improvement in response accuracy with RAG-based systems
from idea to production-ready GenAI solutions
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