AI AUTOMATION FOR PRIVATE EQUITY

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Built for how deal teams actually work

Your investment professionals spend more time wrangling spreadsheets than evaluating opportunities. We build custom AI systems that automate the data work so your team can focus on what they were hired to do—finding and creating value.

60%of deal team time goes to data gathering, not deal evaluation
DEAL MEMO: THE MODERN PE FIRM TIME AUDIT
Investment professional rate:$500/hour
Hours worked this week:60
Hours on deal evaluation:18
Hours on data aggregation:42
Opportunity cost:$21,000/week
Time spent on:
Deal evaluation & judgment30%
Data extraction & entry28%
Report assembly24%
Portfolio data chasing18%
FINDING: 70% of investment professional time is not investment work.
The Deal Flow

MARKET INTELLIGENCE:

The Modern PE Landscape

INVESTMENT THESIS

The PE operating environment has fundamentally shifted. Higher rates, longer holds, and increased LP scrutiny mean operational alpha is no longer optional—it is the entire thesis.

1.$2.6T

Dry Powder

Global PE dry powder waiting to be deployed, creating unprecedented deal competition

(Preqin Global PE Report, 2024)

2.5.7 years

Avg Hold Period

Average holding period has stretched from 4 years to nearly 6, demanding more operational value creation

(Bain Global PE Report, 2024)

3.12.5x

Median EV/EBITDA

Median entry multiples remain elevated, compressing the margin for error on returns

(PitchBook PE Breakdown, 2024)

4.72%

Ops Value Creation

Share of PE value creation now attributed to operational improvement vs. multiple expansion or leverage

(McKinsey PE Practice, 2024)

5.45%

GP Consolidation

of LPs plan to reduce the number of GP relationships, concentrating capital with top performers

(Coller Capital Barometer, 2024)

6.89%

Data Priority

of PE firms cite portfolio data and analytics as a top-3 operational priority

(EY PE Pulse Survey, 2024)

The operational alpha imperative

With entry multiples above 12x and interest rates resetting expectations, the old playbook of financial engineering and multiple expansion is dead. Returns now live or die on operational improvement—EBITDA growth, margin expansion, working capital optimization. Firms without operational infrastructure are flying blind.

72%Operational Value

of PE returns now driven by operational improvement, not financial engineering

(McKinsey PE Practice, 2024)

The LP transparency ratchet

LPs are demanding more granular reporting, faster. ILPA standards have raised the bar on transparency. LPs who once accepted quarterly narratives now want monthly portfolio KPIs, attribution analysis, and real-time NAV estimates. Firms that cannot deliver lose re-up commitments.

45%GP Consolidation

of LPs consolidating GP relationships, rewarding operational sophistication

(Coller Capital Barometer, 2024)

The data infrastructure gap

Most PE firms operate on Excel models, email chains, and institutional memory. The same firms making $200M investment decisions are tracking deal flow in spreadsheets and collecting portfolio data via email attachments. The gap between investment sophistication and operational infrastructure is widening.

89%Data Priority

of PE firms cite data infrastructure as a critical operational gap

(EY PE Pulse Survey, 2024)

Field Notes from the Deal Table

What deal teams, portfolio operations, and investor relations professionals actually deal with during a typical week.

MONDAY 7:00AM
Q:

What does your Monday morning look like?

A:

Vice President opens inbox to find 12 new CIMs from bankers over the weekend. Each is 80-120 pages. The IC meeting is Thursday. The team needs preliminary assessments on all of them by Wednesday.

“There are 12 CIMs and three analysts. That is four each in three days. We will be lucky to get through half of these properly.”

THE HIDDEN COST:

3 analysts at $350/hr spending 6-8 hours per CIM on initial screening that AI could triage in minutes

TUESDAY 3PM
Q:

How is the due diligence going on the current deal?

A:

Associate is manually cross-referencing financial statements across 4 years of data rooms. The QoE report is due in a week and the target company uses a different chart of accounts than the last three deals.

“I have been normalizing these financials for two days. Every company organizes their GL differently. This should not take an MBA to figure out.”

THE HIDDEN COST:

20+ hours of manual financial normalization per deal that could be templated and automated

WEDNESDAY 9PM
Q:

Why are you still at the office at 9pm?

A:

Portfolio operations analyst is chasing monthly financials from 28 portfolio companies. Six have not reported. Three sent PDFs instead of Excel. One changed their reporting format without telling anyone.

“I sent the reminder last week. I sent the follow-up Monday. I am now calling the CFO of a $40M company to ask for their income statement. This is not what I went to business school for.”

THE HIDDEN COST:

40+ hours per month chasing, normalizing, and consolidating data from portfolio companies

THURSDAY 6PM
Q:

How is the quarterly LP letter coming?

A:

Investor relations director is manually pulling performance data from three different systems to compile the quarterly LP letter. IRR calculations need to match the fund admin, but they never do on the first pass.

“The GP is going to ask why Fund III shows 14.2% net IRR when the fund admin shows 14.7%. I will spend tomorrow reconciling. Again.”

THE HIDDEN COST:

80+ hours per quarter across IR team on LP reporting that requires manual data aggregation and reconciliation

FRIDAY 4PM
Q:

What keeps you up at night about portfolio performance?

A:

Operating partner reviews the 100-day value creation plan for a recent acquisition. Half the KPIs are behind schedule but the data is two months stale because the portfolio company cannot produce timely reports.

“I cannot manage what I cannot measure. By the time I see these numbers, the window for course correction has already closed.”

THE HIDDEN COST:

Delayed visibility into portfolio company performance costs millions in unrealized value creation

Due Diligence Findings

Every inefficiency in PE shows up in three places: deal velocity, portfolio returns, or LP confidence.

FINDING I: YOUR DEAL TEAM IS DROWNING IN CIMS

THE ISSUE:

Analysts spend 6-8 hours per CIM on initial screening. With 50-100+ CIMs per month, your team is doing data extraction instead of deal evaluation. The best opportunities get the same first-pass treatment as the obvious passes.

EVIDENCE:

6-8 hoursper CIM for initial screening and financial extraction
$2,500-$4,000 per CIM in analyst time

FROM THE FIELD:

“We looked at 200 CIMs last quarter. We could only do deep analysis on 15. I guarantee we missed opportunities in the other 185.”

FINDING II: YOUR PIPELINE IS A SPREADSHEET, NOT A SYSTEM

THE ISSUE:

Deal flow tracking across bankers, proprietary outreach, and portfolio company referrals lives in spreadsheets and email threads. IOI and LOI deadlines get tracked manually. Follow-ups fall through the cracks.

EVIDENCE:

200+potential deals tracked manually per year
Missed opportunities from dropped follow-ups and slow response times

FROM THE FIELD:

“A banker told me he sent us a CIM six months ago. I never saw it. We lost the deal to a firm that responded in 48 hours.”

FINDING III: AGGREGATING 28 PORTFOLIO COMPANIES IS A FULL-TIME JOB

THE ISSUE:

Monthly financial collection from portfolio companies means chasing CFOs, normalizing different chart of accounts structures, reconciling intercompany transactions, and producing consolidated views. Every company reports differently.

EVIDENCE:

40+ hoursper month on portfolio company data collection and normalization
$15,000-$25,000/month in team time for data aggregation alone

FROM THE FIELD:

“I have 28 portfolio companies. Five use QuickBooks, eight use NetSuite, three use SAP, and the rest use a mix of everything. Getting a consistent monthly view is a nightmare.”

FINDING IV: QUARTERLY LP REPORTING IS AN 80-HOUR FIRE DRILL

THE ISSUE:

LP letters require pulling data from fund admin systems, reconciling IRR/MOIC/DPI/TVPI calculations, writing portfolio commentary, generating capital account statements, and formatting everything per ILPA standards. Every quarter.

EVIDENCE:

80+ hoursper quarter across the IR team for LP reporting
$30,000-$50,000 per quarter in senior team time

FROM THE FIELD:

“We have $2B under management and I spend two weeks every quarter on LP letters instead of LP relationships. The reporting crowds out the relationship building.”

FINDING V: YOU CANNOT MANAGE WHAT YOU CANNOT MEASURE IN REAL TIME

THE ISSUE:

100-day plans and value creation initiatives require continuous KPI tracking. But portfolio company data arrives late, in inconsistent formats, and often incomplete. By the time you see problems, the correction window has passed.

EVIDENCE:

2-3 monthslag between portfolio company performance and GP visibility
Millions in unrealized value creation from delayed course correction

FROM THE FIELD:

“We set aggressive EBITDA targets in the 100-day plan. Six months later we found out the portfolio company had been missing their numbers since month two. Nobody told us because they did not have the reporting infrastructure to tell us.”

FINDING VI: CAPITAL CALLS AND WATERFALLS SHOULD NOT REQUIRE FORENSIC ACCOUNTING

THE ISSUE:

Capital call calculations, distribution waterfalls, and GP/LP allocation schedules require meticulous accuracy. Manual processing across Excel models introduces errors that damage LP trust and create compliance risk.

EVIDENCE:

15-20 hoursper capital call or distribution event for calculation and verification
$5,000-$10,000 per event in senior finance time plus audit risk

FROM THE FIELD:

“We caught a $200K allocation error in the waterfall model right before it went to LPs. The Excel formula referenced the wrong cell. That is a trust-destroying mistake we barely avoided.”

AI automation by fund strategy

Every PE strategy has unique workflows, data challenges, and automation opportunities. Here is how AI transforms operations across the most common fund types.

Firms focused on $50M-$500M deals face the highest volume of deal evaluation with the leanest teams. Due diligence is the primary bottleneck, followed by portfolio data aggregation and LP reporting.

Deal SourcingDue DiligencePortfolio OpsLP Reporting

Workflows

CIM Screening & Analysis6-8 hrs/CIM

Financial extraction, market analysis, thesis fit assessment, preliminary valuation

Automation
80%
Financial Due Diligence80-200 hrs/deal

QoE analysis, working capital normalization, EBITDA bridge construction

Automation
60%
Portfolio Financial Aggregation40+ hrs/month

Collecting, normalizing, and consolidating financials across 15-30 portfolio companies

Automation
85%
Quarterly LP Reporting80+ hrs/quarter

Performance calculations, portfolio commentary, capital account statements

Automation
70%

Benchmarks

Avg hours/deal200-500 hours per deal
Avg cost/deal$100,000-$300,000
SourcePE Deal Cost Survey 2024

Priority Opportunities

AI CIM Triage & Extractioncritical
Time savings70-85%
Cost savings$2k-$4k per CIM
high
Automated Portfolio Data Collectioncritical
Time savings60-80%
Cost savings$10k-$20k/month
high
LP Report Generationhigh
Time savings50-70%
Cost savings$15k-$30k/quarter
high

Deal Analysis

What this looks like in practice

Example scenarios based on common PE operational challenges. Numbers reflect industry benchmarks.

12 CIMs. 3 analysts. Thursday IC meeting. Something has to give.

$500M AUM|Middle-market PE|D.C. metro

Challenge

Monday morning brought 12 new CIMs from three different bankers, all with IOI deadlines within two weeks. The VP had three analysts and needed preliminary assessments for Thursday's IC meeting. Traditional screening at 6-8 hours per CIM meant the team could properly evaluate maybe four of twelve. The other eight would get a quick skim at best.

CIMs received12
Traditional screening time72-96 hours
Available analyst hours36 hours
IOI deadline2 weeks

Outcome

AI-powered CIM screening extracted financials, assessed thesis fit, and generated structured screening memos for all 12 CIMs by Tuesday morning. Analysts spent their time reviewing AI output and performing deep analysis on the 4 most promising opportunities. One of those became a $75M platform acquisition.

“The deal we closed came from CIM number 11. Without automation, our analysts would never have gotten to it.”

28 portfolio companies. 28 different formats. One consolidated report due in 5 days.

$1.2B AUM|Buyout fund|Mid-Atlantic

Challenge

The fund had 28 portfolio companies across manufacturing, services, and technology. Each had different accounting systems (QuickBooks, NetSuite, SAP, Excel-only), different chart of accounts structures, and different reporting cadences. The portfolio operations team of four spent the first two weeks of every month just collecting and normalizing data. By the time the consolidated view was ready, the numbers were already stale.

Portfolio companies28
Accounting systems7 different
Monthly collection time320+ hours
Data staleness3-4 weeks

Outcome

Automated data pipelines connected to each portfolio company's accounting system or ingested standardized Excel templates. Monthly financials auto-normalized to the fund's chart of accounts and populated a real-time dashboard. Consolidated portfolio view available by the 5th of every month. Operations team reallocated to value creation analysis.

“We went from spending two weeks collecting data to having a consolidated view by Monday of the first week. The ops team finally has time to actually analyze what the numbers mean.”

$200K allocation error caught at 11pm before LP distribution. An Excel formula referenced the wrong cell.

$2B AUM|Multi-strategy PE|Northeast

Challenge

The fund was processing a $50M distribution to 47 LPs, each with different waterfall provisions, preferred return thresholds, and catch-up mechanics. The Excel waterfall model had grown to 15 tabs over three fund cycles. At 11pm the night before distribution, the CFO found a $200K allocation error—a formula referenced Fund II data instead of Fund III. The entire distribution had to be recomputed manually, delaying the distribution by a week and requiring a correction letter to LPs.

Distribution amount$50M
LP investors47
Error caught$200K
Delay caused1 week

Outcome

Purpose-built waterfall calculation system replaced the Excel model. Automated validation checks flag allocation discrepancies before distribution. LP-specific capital account statements generate automatically. Audit trail captures every calculation step. Fund admin reconciliation happens in hours, not days.

“One misreferenced cell nearly cost us an LP relationship worth $100M in commitments. That is a risk we will never take again.”

The Value Creation Plan

How we fix it

Four operational bottlenecks, four custom-built AI systems. Each one addresses a specific pain point with measurable before-and-after results.

Analysts spend 6-8 hours per CIM extracting financials, assessing thesis fit, and building preliminary models—before any real evaluation begins.

AI extracts key financials, calculates preliminary metrics (revenue growth, margins, EBITDA, capex intensity), assesses thesis fit, and generates structured screening memos in minutes.

Before6-8 hrs/CIM

Analysts buried in PDF extraction. The best deals get the same rushed first look as the obvious passes.

After30-45 min/CIM

Analysts review AI-generated screening memos and focus judgment on the deals that matter.

90% savings

“Screening 12 CIMs before Thursday IC used to mean an all-nighter. Now it means a focused Tuesday morning.”

Operational Due Diligence

Our Qualifications

Building AI for PE firms demands deep understanding of fund economics, regulatory requirements, and the operational realities of managing portfolio companies across heterogeneous businesses.

I. Regulatory & Compliance Awareness

SEC & Form ADV Compliance

Our systems are designed with SEC regulatory requirements in mind. We understand Form ADV registration obligations, Form PF private fund reporting, and the evolving compliance landscape for investment advisers.

ILPA Reporting Standards

We build LP reporting systems that align with ILPA best practices for transparency, fee disclosure, and performance reporting. Standardized templates ensure consistency across fund families.

GAAP/IFRS Fair Value (ASC 820)

Our portfolio valuation systems support ASC 820 / IFRS 13 fair value measurement requirements, including appropriate disclosure of valuation methodologies and Level 1/2/3 classifications.

Data Security & Confidentiality

Deal data, LP information, and portfolio company financials demand the highest security standards. Our systems implement encryption at rest and in transit, role-based access controls, and comprehensive audit trails.

II. Platform & Tool Familiarity

PitchBook

Deal Intelligence

S&P Capital IQ

Financial Data

DealCloud

Deal Management

Cobalt / eFront

Portfolio Management

Carta

Cap Tables

Datasite

Virtual Data Rooms

Chronograph

LP Reporting

Altvia

Investor Relations

III. Data Sources & Research

  1. 1.Preqin Global PE & VC Report
  2. 2.Bain & Company Global PE Report
  3. 3.PitchBook PE Breakdown
  4. 4.McKinsey PE & Principal Investors Practice
  5. 5.EY PE Pulse Survey
  6. 6.ILPA Reporting Best Practices
  7. 7.Coller Capital Global PE Barometer

IV. Regulatory Context

SEC Regulatory Framework

Our systems support compliance with Form ADV, Form D (Reg D filings), Form PF (private fund reporting), and Investment Advisers Act requirements.

Anti-Money Laundering & KYC

Built-in support for AML/KYC requirements including investor verification workflows, beneficial ownership tracking, and OFAC screening integration points.

Deal Compliance

Support for HSR Act (Hart-Scott-Rodino) threshold tracking, CFIUS review considerations for cross-border acquisitions, and regulatory filing deadline management.

Returns Analysis

Potential Savings Model

Conservative estimates based on your firm parameters

Firm Parameters

50

CIMs and teasers reviewed per month

20

Companies requiring monthly financial reporting

150

Total team hours per deal through close

80

Total IR team hours per quarterly reporting cycle

4

Professionals working on a typical deal

$350

Blended fully-loaded cost per hour across team

Potential Returns

CIM Screening Savings

$1,050,000

Deals/month x 5 hrs saved x 12 months x Hourly Cost

Portfolio Reporting Savings

$72,000

Portfolio Companies x 2 hrs saved/month x 12 x $150 ops rate

LP Reporting Savings

$78,400

LP Report Hours x 70% automation x 4 quarters x Hourly Cost

Due Diligence Efficiency

$84,000

DD Hours x 40% automation x 4 deals/year x Hourly Cost

Total Annual Savings

$1,284,400

Sum of all savings categories

ROI Multiple

10.7x

Total Annual Savings / Annual Partnership Investment

Assumptions & Sources

Assumptions

  • 1.CIM screening assumes 5 hours saved per CIM at 80% automation (industry benchmarks)
  • 2.Portfolio reporting assumes 2 hours saved per company per month for data collection
  • 3.LP reporting assumes 70% efficiency gain on report assembly (per ILPA workflow analysis)
  • 4.Due diligence efficiency assumes 40% time savings on data-intensive DD tasks
  • 5.ROI calculated against Growth Partnership tier
  • 6.Actual results vary based on fund strategy, portfolio composition, and current tech stack

Sources

  • [1]Preqin Global PE Operations Survey 2024
  • [2]Bain & Company Global PE Report 2024
  • [3]EY PE Operational Excellence Study
  • [4]ILPA Reporting Best Practices Framework

Portfolio Performance Review

Projected Outcomes

I. From the Field

We were screening 15 CIMs per week with 3 analysts. Now we screen 50+ with the same team. The AI handles the data extraction so our team focuses on deal judgment.

— Vice President, Middle-market PE firm, $800M AUM, Example scenario

Portfolio reporting went from a 40-hour monthly fire drill to a 4-hour review session. Our ops team went from data collectors to strategic advisors overnight.

— Head of Portfolio Operations, Growth equity fund, 22 portfolio companies, Example scenario

Our LPs started commenting that our quarterly reports were more detailed and arrived faster. They had no idea it was because we automated 80% of the assembly.

— Investor Relations Director, Buyout fund, $1.5B AUM, Example scenario

I run a search fund by myself. The CIM screening automation gave me back 20 hours a week. That is the difference between looking at 5 deals and 25.

— Search Fund Principal, Solo search fund, Example scenario

II. Before & After

Managing Directors / Partners

Before:

Reviewing CIM screening memos that took analysts days to prepare

After:

AI-generated screening memos ready for review within hours of CIM receipt

3x more deals evaluated per IC meeting

Vice Presidents / Associates

Before:

Weekends spent extracting data from CIMs and normalizing financials in Excel

After:

AI handles extraction and normalization. VPs focus on deal judgment and thesis development

70% more time on deal evaluation vs. data work

Portfolio Operations

Before:

Two weeks every month chasing portfolio company data and building consolidated reports

After:

Automated data pipelines deliver consolidated portfolio views by the 5th of every month

85% reduction in data collection time

Investor Relations

Before:

Two weeks every quarter consumed by LP report assembly instead of LP relationship building

After:

LP reports generate from live data. IR team reviews, personalizes, and distributes

75% reduction in quarterly reporting time
Terms of Engagement

How we work with PE firms

We understand fund economics, carry mechanics, and the reality of managing a portfolio across diverse industries. We build custom AI systems for firms like yours.

AI Opportunity Assessment

Fixed scope, discussed during consultation
What:

We audit your deal flow, portfolio operations, and LP reporting workflows to identify the 3-4 highest-ROI automation opportunities specific to your fund strategy.

Deliverable:

Prioritized opportunity roadmap with economics

2–4 weeks
Learn more

Portfolio Data Pilot

Standalone or as part of partnership
What:

Prove the value on 3-5 portfolio companies. See automated data collection and normalization in action before committing to firm-wide implementation.

Deliverable:

Working automated data pipeline with consolidated dashboard

2–4 weeks
Learn more
Most Popular

Growth Partnership

Monthly partnership, scoped to your needs
What:

Embedded engineering partner building custom systems for your firm. Most popular tier for PE firms with 10-30 portfolio companies.

Deliverable:

Production AI systems, ongoing enhancement, priority support

Ongoing monthly engagement
Learn more
Indication of Interest

Letter of Intent

The undersigned firm, having reviewed the operational case for AI automation, hereby expresses interest in exploring a partnership for the purpose of eliminating manual data work and accelerating value creation.

Talk to someone who understands fund economics

Carry waterfalls, MOIC targets, LP reporting standards. We speak your language.

Value Creation Thesis
  1. 1.That your deal team evaluates every opportunity, not just the ones they have time for
  2. 2.That portfolio reporting is a 4-hour review, not a 40-hour fire drill
  3. 3.That LP confidence increases because your reporting is faster, more detailed, and always reconciled
No commitment required
30-minute conversation
Confidential

Or email hello@scalewerk.net directly

Respectfully submitted,

SCALEWERK CONSULTING LLC

Operating Partner for Operational Excellence