AI and Data Services for Private Equity
DATA EBITDA

Insight Factory builds AI, data science, and data engineering solutions for PE‑owned portfolio companies โ€” turning fragmented data into measurable EBITDA growth.

We work at two levels: the portfolio company's P&L, and the sponsor's portfolio-wide AI agenda. Every engagement is designed around a specific financial outcome, built for production, and owned by your team.

>16×
ROI on Insight Factory Fees (Factory Automation Parts Company Solution)
$15M–$30M
Annualized incremental EBITDA on key projects
20+ yrs
Average experience, three founding partners
~100
Data, cloud, AI & full-stack engineers
Sponsor AI Solutions

A portfolio AI strategy, answered with evidence rather than narrative.

Investors increasingly expect PE firms to have an AI strategy for their funds and for every portfolio company. We work alongside deal and portfolio teams to build one โ€” company by company, sized in dollars, grounded in each company's own data.

Deal-Side

AI Due Diligence

Assessment of AI upside while the deal is in diligence. Within days of receiving deal materials, we identify where AI can grow EBITDA and what each opportunity is worth โ€” then confirm it against data-room detail before close. Sized as value above management's plan, with a clear list of what to verify.

Fund-Wide

AI Portfolio Review

A portfolio-wide assessment of AI opportunity across existing companies โ€” where AI creates EBITDA, what each initiative is worth, and the order to build them. The evidence behind a sponsor's answer when investors ask about AI.

Post-Close

AI Value Creation Planning

The first-hundred-days AI plan for a new platform: data foundations, priority solutions, owners, and measurement โ€” scoped during diligence so building starts at close.

Example: for a sponsor in diligence on a multi-site consumer services platform, we identified and sized AI-driven EBITDA opportunities within days of receiving deal materials, and quantified site-level value gaps from the data room within a week.

Our Portfolio Company AI Focus:   Data » EBITDA

Messy, fragmented portfolio company data, converted into organic EBITDA drivers.

FRAGMENTED PORTCO DATA REVENUE GROWTH ML LEAD SCORING · PIPELINE GENERATION MARGIN IMPROVEMENT AI CUSTOMER SUPPORT · LEAKAGE RECOVERY PRICING POWER PRICING & MARKET-SIGNAL INTELLIGENCE COST REDUCTION BACK-OFFICE AUTOMATION · LABOR OPTIMIZATION OPERATIONAL EFFICIENCY DATA UNIFICATION & STANDARDIZATION CUSTOMER RETENTION CHURN PREDICTION · ACCOUNT HEALTH

Revenue Growth

ML lead scoring · pipeline generation

Margin Improvement

AI customer support · leakage recovery

Pricing Power

Pricing & market-signal intelligence

Cost Reduction

Back-office automation · labor optimization

Operational Efficiency

Data unification & standardization

Customer Retention

Churn prediction · account health

Portfolio Company AI Solutions

Built one at a time, around one company's data and one P&L.

Each solution is designed around a portfolio company's unique operational data and economics โ€” delivered for value creation in business operations.

Our AI Thesis
Most portfolio company AI stalls in pilot. It fails on two things: the data foundation beneath it, and the engineering that keeps it running โ€” so we build both first. What we build gets used โ€” every day, in the real operation.

AI Lead & Financing Approval Scoring

Every inbound lead receives two AI scores in seconds โ€” likelihood to convert and likelihood of financing turn down โ€” built on the company's own funnel data plus hundreds of external household-level data points. Best leads are called first; financing risk is flagged before rep time is spent.

16%
Conversion rate lift
$23M–$28M
Projected annual EBITDA impact, full company roll-out

Built for an $800M D2C home services roll-up โ€” 9 brands, 35+ states; $10M–$12M of annual impact in the largest division alone. Model now owned and run by the client.

AI Churn Prediction & Account Health

Each account receives a churn risk score built from operational signals the company already generates โ€” service quality, contact-center sentiment, billing friction, relationship cadence โ€” with root causes attached. Account managers step in months before renewal, not after notice.

CFO Award
For Solution Innovation — leading financial services information & data company

Relevant for shipping / 3PL, facilities, managed services, security, equipment rental, parking & hospitality.

AI-Powered Pricing & Procurement Engine

For distributors, remanufacturers, and large-catalog sellers โ€” SKUs scored daily on margin and demand from unified company and market data, flagging what to reprice, what to buy, and what to skip. Ranked actions daily, executed by people or AI.

16×
Client ROI on fees
$6.5M+
Incremental gross margin, first six months

Live in production at a PE-owned global parts distributor.

AI-Enabled Complex Data Unification

Acquisitions leave the same procedures, parts, and billing items coded differently in each business. AI matches and scores the codes; your team approves โ€” producing one unified catalog in days rather than months, with new acquisitions plugging in at deal close.

90–95%
AI match accuracy
Months → Days
Manual harmonization eliminated

Relevant for veterinary, dental / DSO, healthcare, collision repair, parts distribution, home & field services roll-ups.

AI Customer Support โ€” Front Office

Agentic AI support grounded in live operational data, with a person as fallback. For a large rideshare fleet manager, we handle more than 5,000 driver calls a day โ€” segmenting each call by need (billing, vehicle service, scheduling, complaints), assessing customer sentiment, and routing the most frustrated drivers to customer success managers daily.

>5K
Driver calls handled per day
Daily
Frustrated-caller escalations to customer success

Suited to any high-volume service business โ€” status calls, scheduling, routine account requests.

AI Back-Office Automation

Document-heavy finance work โ€” statement reconciliation, payables, cash application โ€” read to the line by LLM document intelligence โ€” digital or scanned โ€” and matched to system records. Staff see only the exceptions; headcount and labor costs stop increasing with volume and each new acquisition.

Line-level
Document reading & matching
Exceptions only
Reach your staff

Example AI Solution: an AI statement-reconciliation engine for a multi-site automotive services platform โ€” reads vendor statements to the line, matches them to posted bills, and surfaces only the exceptions. Designed for roll-ups, where every acquisition adds vendors and statements.

AI Revenue Leakage Recovery

AI that finds revenue the contracts earned โ€” but the invoices never captured. Contracts, rate schedules, and amendments become an entitlement model; the engine reconciles entitled versus invoiced against the operational record of delivered work, and every finding carries its contract clause and the evidence, ready to send. Nothing is billed automatically โ€” a person reviews the evidence and approves every recovery. One engine, repeatable across a services portfolio: a sponsor can deploy it company by company.

3–5%
Typical leakage in complex-contract services businesses
Direct EBITDA
Recovered dollars land in your own ledger, measured against a pre-agreed baseline

Fits any services business whose contracts carry escalators, surcharges, tiers, or minimums — facilities & business services, logistics & transportation, field & mechanical services, testing & inspection, managed IT, healthcare services.

Portfolio Company Data Solutions

From fragmented data to trusted decisions.

Fragmented data leaves portfolio companies with reports that disagree, AI that stalls before production, and decisions made without reliable inputs. We build the foundations and single-source-of-truth that fix it.

01

Data Foundations

One trusted set of numbers. Sources combined into a single system in your cloud, metrics defined once, reports rebuilt and checked against live figures.

02

Cloud & Platforms

Secure, governed, cost-controlled cloud at scale โ€” with sprawl and rising spend brought under control.

03

Applied AI

Assistants, document processing, and copilots grounded in your own data โ€” proven reliable before going live, monitored in production.

04

ML & AI Ops

Models watched, retrained, and governed continuously โ€” accurate, dependable, and audit-ready for the full hold period.

Built for production โ€” and owned by your team

In practice: for a large global rideshare fleet owner, we built the cloud enterprise data platform from the ground up, automated reporting, and AI/ML infrastructure โ€” including unit-level MOIC โ€” recognized by investment bankers ahead of a capital raise.

How We Work

Three commitments, on every engagement.

EBITDA-measured

Every solution is scoped against a specific financial outcome and reported against it โ€” visible to leadership and sponsors.

Accelerator-driven

Every solution starts from a proven, reusable core, then configured to your data, your funnel, and your economics โ€” faster to production, no generic templates, no commodity builds.

You own the IP

Work is delivered work-for-hire with full handover. Models and platforms are run by your team when we step away.

“We LOVE working with you guys. I had that feeling the first time we met you and still feel it more than ever. There's a meaty roadmap I think we can tackle together and this first project is such a strong signal of the potential.”

C-Level Sponsor · Active ML Lead Scoring Engagement · D2C Home Services Roll-Up
Leadership

Operators and advisors, backed by a 100-engineer data, cloud, and AI build organization.

Jerry Wish
President + Co-Founder

Jerry Wish

20+ years building and advising on data analytics for Fortune 500 companies. CEO and C-level trusted advisor on high-impact data initiatives that build competitive advantage.

Columbia (MBA) · George Washington (JD) · Michigan (BA)
Max Jacobson
Head of Data Science & AI + Co-Founder

Max Jacobson

Scaled a Sequoia-backed startup to acquisition by Dun & Bradstreet and led data science post-close. Former management consultant at Boston Consulting Group.

Princeton (BA) · Stanford (Data Science)
Naveen Gainedi
Head of Data & Technology

Naveen Gainedi

Two decades in IT consulting, data analytics, cloud, and software development. Proven builder of engineering teams and products from the ground up. Founder, DataGrokr.

IIT Kharagpur (Mechanical Engineering) · IIM Calcutta (MBA)

Delivery pairs a US-based data science and AI solutioning group with a 100+ engineer build organization spanning data, cloud, AI, and full-stack development โ€” certified across AWS, Azure, and Google Cloud, and delivering production-grade systems to ISO 27001 and HIPAA standards.

The three founders outside the Time to Eat Diner in New Jersey
Founded 2023, Time to Eat Diner, New Jersey