EigenStrategy - AI Consulting for Credit and Investment Teams

What I Build

Practitioner judgment, encoded into tools your team uses at the desk.

Not generic AI applied to finance. Systems built around how experienced credit professionals actually reason.

01

Domain-Encoded AI Agents

Covenant analysis, borrowing base review, deal screening, risk assessment. Practitioner judgment encoded into agents your team can use at the desk - not a general-purpose model pointed at credit.

02

Investment Process Architecture

Turning diligence, monitoring, and reporting workflows into sourced, repeatable systems that fit how credit teams already work.

03

Practitioner Advisory

A clear read on where AI creates real signal versus noise in your process. Workflow mapping, agent design, vendor evaluation - what to build, what to buy, what to avoid. I have sat on both sides.

Where this shows up

The workflows where domain knowledge is hardest to replicate.

iDeal screening & initial diligence

iiData room processing & extraction

iiiCovenant & indenture analysis

ivBorrowing base & collateral review

vPortfolio monitoring & reporting

viLP reporting & investor updates

viiMarket data integration & hygiene

viiiRisk assessment & stress testing

The Thesis

The bottleneck is not access to AI. Everyone has access. The bottleneck is that generic tools don’t know what matters, what to reconcile, or what to check next.

Generic AI

Can define a covenant - but not whether this one actually protects you
Doesn’t reliably know what matters, what to reconcile, or what to check next
Produces output that looks right - until a credit professional reads it
Every deal still requires a senior person to validate from scratch

Domain-Encoded AI

Reconciles the source hierarchy - executed documents override the deck
Identifies conflicts between documents - catches what fatigue misses
Knows what a credit professional checks next
Organizes output the way credit decisions actually get made

How engagements work

Start small, on a real problem, with no long-term obligation.

01

Scoping

A focused conversation to map where AI can actually help: what the bottlenecks are, where domain expertise is hardest to replicate, and what a useful output looks like for your team.

02

Proof of concept

Run a POC on a live deal or workflow. You see exactly what gets built and what the output looks like - before committing to anything broader. Scoped and priced separately.

03

Implementation

Build, refine, and hand off the workflow inside how the team already operates. Documentation and a handoff your team can use and extend independently.

In practice

What the work has looked like.

Representative engagement

Buy-side credit fund · anonymized

The input

A multi-tenant, multi-document data room - 37 documents spanning executed agreements, financials, and seller materials.

What it surfaced

A conflict between the term sheet and the executed agreement, and a structural term that warranted follow-up before IC review - details the team's initial review had missed.

What changed

A fully sourced diligence read in 3 hours instead of 3 days, organized by document tier and confidence, ready to take to the IC.

Initial screening compressed from days to hours, with a standardized extraction framework across a multi-site, multi-tenant portfolio - structured for LP reporting.

Credit fund · 2026 · anonymized

AI does not create credit judgment on its own. It magnifies what skilled managers already have.

The EigenStrategy thesis

About

Built from inside an investment process, by someone who lived it.

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Prashant Radhakrishnan

Founder, EigenStrategy

I spent seventeen years in credit markets, most recently as Managing Director and Head of High Yield Trading and E-Strategy for Leveraged Finance at RBC Capital Markets. I hold a graduate degree in computational finance from Carnegie Mellon, where my interest in applying mathematics to markets took shape.

What has always drawn me to credit is the pattern recognition - the connections between capital structures, the signals buried in document language, the way information moves unevenly across asset classes. I have managed traders, managed risk, and deployed real capital across market cycles.

These tools are designed from the inside of an investment process. The AI extends that experience. It does not substitute for it.

Managing Director, Head of HY Trading & E-StrategyLeveraged Finance

RBC

Credit Trading

Bank of America

Credit Trading

Deutsche Bank

MS, Computational Finance

Carnegie Mellon

Questions

What teams ask before we start.

Generic tools can summarize documents. They do not reliably know which terms matter, which source controls, what conflicts need escalation, or what a credit professional checks next. That judgment lives in people who have made credit decisions with real capital - and it exists in no training dataset.

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. POC work is scoped and priced separately. Most engagements that proceed move into an implementation or advisory phase, and ongoing relationships are typically structured as monthly retainers once the scope is defined.

Every engagement begins with a mutual NDA. I work exclusively with model configurations that do not train on client data and do not retain it beyond the engagement - a non-negotiable baseline for confidential deal materials. For teams with strict requirements, I can build inside your environment rather than routing documents through third-party platforms, scope access to the specific materials in play, and document data handling so it clears an information-security review. I have worked with pre-close investment data, names-based watchlists, and LP-facing reporting under confidentiality obligations throughout my career, and I am comfortable mapping the engagement to your existing vendor and access-control policies.

Yes, and that breadth is a meaningful part of what I bring. Seventeen years in public credit markets - high yield, investment grade crossover, leveraged loans - before private credit reshaped where capital flows. The most interesting analytical questions often sit at the intersection of both: relative value between a public bond and a private instrument on the same issuer, or what a public price signal says about stress building in a private portfolio. Private credit and direct lending are strong use cases on their own - large data rooms, dense documents, significant manual review burden.

The gap between generic AI tooling and the specialized analytical requirements of credit investment. 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. Common entry points: data rooms that take days to process manually, covenant monitoring that still lives in spreadsheets, and watchlists that require synthesis across unstructured sources. Most clients arrive with one bottleneck and expand once they see what systematic document intelligence can do.

Get in touch

Send the workflow. I’ll tell you if AI can actually help.

Send a workflow or data-room bottleneck. I’ll tell you whether AI can help, what the first test should be, and where it is likely to fail. Most engagements start with a single scoping conversation - no long-term obligation.

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Email

[email protected]

Web

eigenstrategy.com