EigenStrategy - AI Systems for Buy-Side Credit, Direct Lending, and Structured Finance

In practice

What the work looks like.

Representative engagement

Buy-side credit fund · anonymized

The input

A multi-tenant, multi-document data room - 45 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

Seven days of manual review compressed to one hour. The investment professional still owns the judgment - this gets them there faster.

Available workflow

Portfolio exposure monitoring

The problem

An 80-name credit portfolio needs to be reviewed for concentrated sector exposure after a rapid industry re-rating - a process that currently touches every position manually.

What it surfaces

A ranked watchlist of affected positions with supporting citations - leverage ratios, maturity dates, and covenant proximity organized by severity across the book.

The result

An unstructured manual process replaced with a ranked, cited flag list - ready for analyst review.

Available workflow

Documentation and covenant monitoring

The problem

A 50-name direct lending portfolio with active covenant packages across multiple credit agreements - tracked manually in spreadsheets, changes discovered reactively.

What it surfaces

Amendment velocity, covenant proximity flags, and structural changes across the book - synthesized from raw filings and surfaced by name, not discovered by hand.

The result

Reactive, spreadsheet-driven tracking replaced with a systematic read - material changes surface before they reach 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

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

Document Intelligence and Conflict Detection

Source hierarchy enforcement, conflict detection, and underwriting gap identification across complex deal documents. The system knows which source controls, what to reconcile, and what a credit professional checks next - without being told.

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 Information Problem

Most of the information credit decisions depend on was never designed to be analyzed at scale. Knowing which of it matters, and how to structure it for decisions, is the hardest part.

What exists but isn't being used

Market signals tracked separately from the deal analysis workflow
Earnings language that telegraphs stress before the numbers do
Filing patterns, amendment velocity, and sector data synthesized by hand
Portfolio-level signals buried across documents and periods that don't compare
LP reporting and borrowing base mechanics that live in static PDFs every quarter

What systematic conversion enables

Market data integrated into the investment workflow, not tracked alongside it
Signals surfaced before they reach the investment committee
Comparable output across issuers, time periods, and capital structures
Risk and stress testing that draws on the full information set
Leverage that compounds: every engagement makes the next decision faster

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.

Whether you are still mapping where AI can help, running your first pilots, or ready to build a production-grade workflow - the entry point adjusts to where your team is. The domain expertise and the architecture scale with you.

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.

About

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

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headshot
prashant_headshot.png

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.

A scoping conversation focused on a specific workflow or deal type, then a proof of concept on a live deal so you can evaluate the actual output before committing to anything broader. POC work is scoped and priced separately. Ongoing relationships are structured as monthly retainers once 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. Seventeen years in public credit - high yield, IG crossover, leveraged loans - before private credit reshaped where capital flows. Private credit and direct lending are strong use cases: large data rooms, dense documents, significant manual review burden. The most interesting analytical questions often sit at the intersection of both.

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.

Best starting points: a data room, a covenant-monitoring process, a borrowing base review, or an LP reporting bottleneck.

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Email

[email protected]

Web

eigenstrategy.com