How One Private Credit Firm Pushed Deal Volume Up 50%

A private credit deal team handed CIM parsing, memo drafts, and LP reports to an AI app on Dynamics — cutting document time 70% and lifting deal volume 50% with no new headcount.

70%

Faster document and memo production

2–4 months

Implementation Time

Osman Ghandour

Co-Founder & CEO

Soal Labs

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The Challenge

A middle-market private credit firm was running its entire deal operation manually. Analysts extracted data from CIMs by hand, built investment memos in PowerPoint, and copy-pasted figures into iLevel for portfolio tracking. Quarterly reporting took days longer than it should have. The firm wanted to grow its deal volume — but couldn't see a path to doing it without adding headcount it was unwilling to hire.

What They Built

Soal Labs co-designed the solution with the firm's deal team before building anything — mapping the actual workflow first, then engineering against it. The result was a custom underwriting and due diligence application integrated directly with Microsoft Dynamics, iLevel, and SharePoint. AI parses incoming CIMs automatically and populates the deal record. Investment memos are generated with a prompt. Portfolio company handoff documentation is automated. Quarterly reporting runs without delay. The firm didn't add headcount — it added capacity: deal volume increased 50 percent, document production time dropped 70 percent, and LP reporting now arrives on time without exception.

Soal Labs began with a co-design phase — sitting with the firm's deal team to map the actual workflow before writing a single line of code. That sequencing mattered: the resulting application addressed real friction points rather than theoretical ones. The core build integrated directly with Microsoft Dynamics, iLevel, and SharePoint — the three systems the team already used — eliminating redundant data entry and the version-control chaos that came with it. AI was layered in at each high-friction step: incoming CIMs are parsed automatically and used to populate the deal record; investment memos are generated from a prompt rather than built from scratch; portfolio company handoff documentation is produced automatically; quarterly LP reporting runs without delay. The full engagement ran approximately 4–6 months. By the end, deal volume had increased 50 percent, document production time dropped 70 percent, and LP reporting arrived on time consistently — all without adding headcount.

AI Role

Not shared

Infrastructure

Microsoft Dynamics (CRM and deal tracking) • iLevel (portfolio monitoring) • SharePoint (document management) • Custom underwriting and due diligence application

Integration Points

AI CIM parser connected to Dynamics deal record population • Investment memo generator triggered from deal stage • Automated portfolio handoff documentation from iLevel data • Quarterly LP reporting pipeline from aggregated portfolio data

Impact

70% Faster Document Production

CIM parsing, memo generation, and portfolio handoff documentation — previously done manually — now happen in a fraction of the time, with AI handling the first pass automatically.

50% More Deals

Deal volume increased 50 percent without adding a single headcount — the firm scaled its origination capacity entirely through workflow automation.

Zero LP Reporting Delays

Quarterly LP reporting — previously delayed by days due to manual data aggregation — now runs on time without exception, removing a recurring source of stakeholder friction.

Technology Utilized

Implementation Complexity

Revenue Growth

Time Savings

Cost Reduction

Headcount Avoidance

Not shared

Best Fit For

Middle-market private credit, direct lending, and alternative investment firms running deal operations on manual workflows across disconnected systems, looking to scale volume without scaling headcount.

Osman Ghandour

Co-Founder & CEO

Soal Labs

Co-Founder & CEO at Soal Labs, delivering data engineering and AI solutions that modernize private capital operations for scalability and efficiency.

Connect with the Expert

Industry:

Financial Services

Business Function:

Finance & Accounting

Operations

Company Size:

51-250

Project Cost:

Cost not disclosed

Ownership:

Private Equity-Backed

Organization Type:

Private Company

AI Pattern:

Document Processing & Extraction

Generative Design & Content

Process Automation (RPA + AI)

Value Type:

Revenue Growth

Time Savings

Cost Reduction

Headcount Avoidance

AI Model:

Frequently Asked Questions

How did a mid-sized private credit firm push deal volume up 50% with document processing and automation?

The mid-sized private credit firm co-designed a custom underwriting and due-diligence application with its deal team, mapping the workflow before building. Integrated with Microsoft Dynamics, iLevel, and SharePoint, the app parses incoming CIMs automatically, generates investment memos from a prompt, and automates portfolio handoff documentation and quarterly reporting. Deal volume rose 50% with no new headcount.

What AI tools and approach did the private credit firm use?

The build combined document processing and extraction, generative design and content, and process automation in a custom application integrated with Microsoft Dynamics, iLevel, and SharePoint — parsing CIMs, drafting memos, and automating handoff documentation and LP reporting.

What results did the private credit firm achieve?

Three outcomes: 70% faster document production as manual CIM parsing, memo generation, and handoff documentation moved to an AI first pass; 50% more deals with no added headcount; and zero LP reporting delays, with quarterly reporting now running on time without exception.

How long did the engagement take?

Time to results was in the 4–6 month range, covering the co-design phase and the integrated build.

Who is this AI underwriting approach best for?

Middle-market private credit, direct lending, and alternative investment firms running deal operations on manual workflows across disconnected systems that want to scale volume without scaling headcount.