Analyzing financial markets has become harder than ever for companies. With the explosion of alternative datasets, high-frequency data, and rapidly evolving market dynamics, decision cycles have compressed from months to days—often hours. Markets now operate at a level of sophistication that legacy software was never designed to handle.
As a result, firms are overwhelmed with data but short on actionable insight. They rely on systems built for a slower, simpler era, making it increasingly difficult and time-consuming to identify which signals actually matter.
That’s where Auxage comes in. Founded by Rohit Chaudhary, Auxage applies AI to continuously ingest, organize, and reason over raw market data at machine speed, compressing what once took teams weeks of analysis into moments and delivering ready-to-reason insight at a scale no human workflow can match.
The data problem facing the financial world
The surge in high-frequency data has fundamentally accelerated markets. Research workflows that once unfolded over months are now compressed into days—sometimes hours—leaving traditional processes unable to keep up.
To keep pace, firms have dramatically expanded their hiring of software engineers and data scientists, making asset managers resemble technology companies in their staffing models. According to studies by Deloitte and Northern Trust, 98% of investment professionals now rely on alternative datasets like credit card transactions, web scraping, and social media sentiment as core inputs for generating returns.
Yet, as McKinsey notes, higher technology spending has not translated into proportionally better outcomes. Much of that investment still goes toward maintaining or enhancing legacy infrastructure—third-party data feeds and rigid market terminals—alongside general-purpose analytics and data infrastructure tools. While these systems can aggregate and visualize information, they don’t always provide integrated, governed workflows, potentially leaving knowledge fragmented across dashboards and making it difficult for teams to see the full picture in one place.
As a result of this, investment professionals wind up spending more time managing inputs than making decisions.
Rohit Chaudhary: A decade deploying billions through legacy infrastructure
Rohit Chaudhary has lived this evolution firsthand. Over the last decade, he worked at leading global investment firms, including Viking Global, Balyasny, and Apax Partners, tracking hundreds of stocks and helping deploy billions of dollars across public markets. There, he saw how the rhythm of investing shifted from thoughtful research on business fundamentals to constantly chasing data points, and he witnessed a growing gap between market complexity and the tools available to track it.
He spent years building proprietary systems alongside investment teams, data scientists, and ML engineers to overcome this gap, helping fundamental research through automation and contributing to tasks in portfolio analysis, risk management, and insight generation.
Rohit is now taking this a step further with Auxage. He is building a knowledge foundation designed to reset how markets are tracked and understood, embedding decades of institutional best practices directly into the platform’s technology stack.
How Auxage treats AI as a core operating system
Auxage is an AI-native technology stack built on a proprietary data structure designed for the generative AI era. Instead of relying on manual ingestion, static search indexes, and siloed datasets, Auxage continuously constructs and updates a machine-readable model of financial markets. This model forms a foundational intelligence layer that is ready for reasoning - one that legacy tools were never designed to support.
The platform unifies billions of structured and unstructured data points into a live, queryable representation of financial markets. This foundation serves as the substrate for domain-specific AI agents capable of generating outputs intended to support reliable, precise, and traceable results. Rather than bolting AI onto legacy architectures, Auxage grounds its reasoning agents directly in domain rules and structured context, a practice that seeks to reduce the possibility of hallucinations and generate outputs aligned with enterprise requirements.
With Auxage, users can analyse decades of market and company financial history with full granularity and fidelity. They can benchmark financial and operational metrics across companies and industries, track long-term trends, and understand relative positioning at extreme levels of detail using simple instructions. Users define once and track forever, as the platform generates outputs that remain continuously synchronized with new information, updating automatically as markets move.
The desired result of Auxage is to reduce manual overhead for workflows that historically required weeks of human effort. According to Auxage’s internal evaluations, its knowledge foundation has shown material performance advantages compared with standalone foundation models and incumbent platforms attempting to retrofit AI capabilities.
Giving the industry immediate and dynamic market insights
Since the digitisation of public markets in the 1980s, investors have been trapped between two extremes: data-heavy terminals that expose raw information, and productivity tools like spreadsheets to manually assemble meaning from it; neither is designed to make institutional knowledge persistent, computable, or self-updating.
Auxage represents a step-change. It moves investors from data ingestion and cleanup to ready-to-reason knowledge. The platform is vertically integrated and purpose-built for financial markets, combining real-time market signals with a workflow-native web interface inspired by spreadsheets and collaboration tools. Autonomous, agentic systems operate continuously in the background, processing information and surfacing insights without constant human prompting.
Rather than acting as another point solution, Auxage aims to become the agentic foundation of enterprise knowledge infrastructure for firms investing in public markets. Over time, this enables new ways to identify signals, understand markets, and act at a scale and speed that were previously impractical or required constant manual oversight.
Auxage is designed to create a self-reinforcing flywheel: as workflows become more AI-native, the platform captures institutional knowledge from experienced investors and embeds it directly into how agents detect, filter, and surface signals. Over time, the system is intended to adapt to investor-defined priorities, helping distinguish higher-impact signals from lower-relevance information.
Resetting the standard for financial intelligence
The financial data industry is under growing pressure to evolve. Market signals are emerging faster than most existing tools can interpret or connect. With Auxage’s AI-native approach and domain-specific infrastructure, Rohit Chaudhary is setting a new standard for how institutional investors and corporations operate in markets — one where technology compounds insight rather than simply organizing data.
Disclaimer: This content is for informational purposes only and does not constitute investment advice, a recommendation, or an offer or solicitation to buy or sell any securities. Any references to capabilities or performance are based on internal assessments and are not guarantees of future results.
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