What is EigenStrategy?
EigenStrategy is an AI consulting practice founded by Prashant Radhakrishnan, a former managing director and head of high yield trading at RBC Capital Markets. The practice builds AI systems specifically designed for credit funds, direct lenders, family offices, and investment teams that work with complex financial documents and investment processes. Unlike general-purpose AI vendors, EigenStrategy encodes seventeen years of institutional credit market experience into the systems it builds, covering corporate credit, structured finance, leveraged loans, and high yield bonds. The name reflects a mathematical principle about extracting the underlying signal from complex, noisy data. That is what good credit analysis has always been.
Who is Prashant Radhakrishnan?
Prashant Radhakrishnan is the founder of EigenStrategy and a credit markets practitioner with seventeen years of institutional experience. He served as Managing Director and Head of High Yield Trading and E-Strategy at RBC Capital Markets, and held senior credit trading roles at Bank of America and Deutsche Bank. He holds a graduate degree in computational finance from Carnegie Mellon University, where his interest in applying mathematics to market structure took shape. Since leaving RBC in 2024, he has focused on building AI systems for credit and investment teams, combining deep domain expertise in leveraged credit with hands-on experience developing AI workflows and agent-based tools.
What problems does EigenStrategy solve?
EigenStrategy addresses the gap between generic AI tooling and the specialized analytical requirements of credit investment. The core problems are document-heavy workflows that consume disproportionate analyst time, the need to screen deals faster without sacrificing rigor, and the challenge of building AI that reflects how credit professionals actually think rather than how software engineers imagine they do. Common entry points are data rooms that take days to process manually, covenant monitoring that still lives in spreadsheets, and portfolio company watchlists that require synthesis across multiple unstructured sources. Most clients arrive with one specific bottleneck and expand the scope once they see what systematic document intelligence can do.
Who do you work with?
Buy-side credit funds across corporate and structured credit, direct lenders, family offices, and LPs. Most clients have experienced practitioners who understand their investment process well but have not found AI tooling that reflects how they actually think about risk. The common thread is teams that are analytically rigorous, deal with large volumes of complex documents, and want technology built around their workflow rather than forcing their process to fit a generic product. I also work with LPs that want to apply systematic analysis to manager due diligence and portfolio monitoring across credit strategies.
How is this different from off-the-shelf AI tools?
Most AI tools in finance are built by people who have never sat on a credit desk. The logic is generic because the builders do not know which covenants actually matter in a given capital structure, what a data room is designed to obscure, or how to read appropriate confidence into an output. EigenStrategy starts from how an experienced credit analyst actually works and encodes that domain knowledge into the system. The result is not a document summarizer or a chatbot layered on top of your data. It is a system that understands the difference between a secured first-lien and an unsecured PIK note, knows what a change-of-control provision means for recovery, and surfaces the information that changes decisions rather than everything in the document.
What does an engagement look like?
It starts with a scoping conversation, usually 30 to 60 minutes, focused on a specific workflow pain point or deal type. From there I run a proof of concept on a live deal or data room so you can evaluate the actual output before committing to anything broader. Proof of concept work is scoped and priced separately, which means you can test the approach on a real problem with no long-term obligation. Most engagements that proceed past the POC move into an implementation or advisory phase over four to eight weeks. Ongoing relationships are typically structured as monthly retainers once the initial scope is defined.
Can you help with private credit and direct lending?
Yes. Private credit and direct lending are among the best use cases for this kind of work. The data rooms are large, the documents are dense, the deals are bespoke enough that generic tools fail, and the manual review burden is significant. I have built extraction and analysis frameworks for structured credit data rooms with 40 or more documents, compressing days of initial screening into hours. The output is structured for LP reporting and downstream analysis, not just a narrative summary. For direct lenders running high volumes of origination, systematic document processing creates an operational advantage that compounds over time as the model learns the specific document types and deal conventions in your portfolio.
What about data security and confidentiality?
Every engagement begins with a mutual NDA. I work exclusively with model configurations that do not train on client data, which is a non-negotiable baseline for working with confidential deal materials. For teams with strict security requirements or existing infrastructure constraints, I can build within your environment rather than routing documents through third-party platforms. I have worked with deal materials under confidentiality obligations throughout my career and understand the sensitivity of pre-close investment data, names-based watchlists, and LP-facing reporting.
Do you work on a retainer or project basis?
Both. Initial engagements are structured as a scoping conversation followed by a proof of concept, which allows you to evaluate the output on a real problem before committing to a broader scope. If the POC produces useful output, most clients move into either a project-based implementation or an ongoing monthly advisory retainer. Scope and pricing are set after the scoping conversation, not before, because the right structure depends on what you are actually trying to solve. There is no minimum engagement size, and the scoping conversation itself is free.
