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.
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.
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.
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.
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.
ML lead scoring · pipeline generation
AI customer support · leakage recovery
Pricing & market-signal intelligence
Back-office automation · labor optimization
Data unification & standardization
Churn prediction · account health
Each solution is designed around a portfolio company's unique operational data and economics โ delivered for value creation in business operations.
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.
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.
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.
Relevant for shipping / 3PL, facilities, managed services, security, equipment rental, parking & hospitality.
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.
Live in production at a PE-owned global parts distributor.
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.
Relevant for veterinary, dental / DSO, healthcare, collision repair, parts distribution, home & field services roll-ups.
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.
Suited to any high-volume service business โ status calls, scheduling, routine account requests.
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.
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 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.
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.
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.
One trusted set of numbers. Sources combined into a single system in your cloud, metrics defined once, reports rebuilt and checked against live figures.
Secure, governed, cost-controlled cloud at scale โ with sprawl and rising spend brought under control.
Assistants, document processing, and copilots grounded in your own data โ proven reliable before going live, monitored in production.
Models watched, retrained, and governed continuously โ accurate, dependable, and audit-ready for the full hold period.
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.
Every solution is scoped against a specific financial outcome and reported against it โ visible to leadership and sponsors.
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.
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-Up20+ 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.
Scaled a Sequoia-backed startup to acquisition by Dun & Bradstreet and led data science post-close. Former management consultant at Boston Consulting Group.
Two decades in IT consulting, data analytics, cloud, and software development. Proven builder of engineering teams and products from the ground up. Founder, DataGrokr.
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.