Introduction to Stratus Data

AI for the
PE-Backed
Middle Market
Stratus helps middle market PE funds and their portfolio companies make the leap from manual to AI-powered operations.


The Opportunity
Private Equity is facing an existential crisis
Limited Partners are holding back capital
PE fundraising pace has hit its lowest levels since 2016. Investors are demanding proof of AI readiness across their portfolios.
Acquirers are hesitating to buy companies
Acquirers view portfolio companies without proof of meaningful AI capabilities as unmitigated risks.
Private Equity funds are being squeezed both ways
Without liquidity from exits or investors, many PE funds will cease to exist.

Portfolio companies are struggling to capture value from AI
1
Information is scattered across disparate systems, making it hard to create a unified view of customers, operations, and finances.
2
Inconsistent, incomplete, or unreliable data undermines trust in analytics, making it difficult to base decisions on solid evidence.
3
Insights and strategy are often disconnected, preventing companies from acting quickly and decisively.
4
Legacy tools like Excel create missed opportunities in insights extraction, and leave room for manual data processing error.
5
A dramatic shortage of AI expertise makes it hard to know what to do with new technologies where the state of play is advancing every day.

AI drives value by increasing ROI without additional OPEX through headcount

Growth
Anticipating demand, identifying expansion opportunities, and scoring prospects, companies are able to achieve revenue increases of 10-20%.
Margin
AI-driven price, cost, and product optimization offer the opportunity to increase gross profit by 5-10%. 
Efficiency
Leveraging AI and optimization reduces SG&A by cutting unnecessary cost, increasing SLAs, and driving overall efficiency. Firms adopting these techniques can see 20-30% savings in overhead costs.

About Stratus Data
Stratus brings AI expertise from Silicon Valley, with an 80/20, quick win, value-first approach
200+
Projects
75+
Clients
8+
Years in Operation
Services
1
1
Data Unification
Data unification across disparate systems
2
2
Insights & Reporting
Dashboards enabling realtime views of financial performance and operations
3
3
ML Forecasting & Scoring
Machine learning that predicts the future and simplifies complexity for you
4
4
Artificial Intelligence
AI-powered interfaces to your data and LLM-powered workflows

Recognition
Trusted by

As Seen In

Award-Winning Impact


Team
Founders
Charles Pensig, Co-Founder
Assistant Director, The Wharton School's Center for AI
Research Fellow, NYU Stern's Center for Digital Economy
3x hyper growth IoT and Wearables Data Science leader
20 years applied AI and Analytics experience
Derek Chang, PhD, Co-Founder
PhD Stanford EE, BS Caltech EE
Former nuclear fusion researcher
2x hyper growth Wearables and SaaS Data Science Leader
20 years applied AI and Engineering Experience

Work Experience

Education: STEM PhDs and Masters


Operating Philosophy

1
Listen Carefully
Because organizations don't come out of a box, neither do our solutions. We listen for what matters to build what creates impact.
2
Drive Value
Focus on solutions that drive measurable value by targeting decisions that lead to action. Everything becomes EBITDA and EV.
3
Start Simple
Start simple. Capture 80% of the value with
20% of the effort.
4
Win Quick
Build momentum by creating solutions that prove value quickly.
5
Partner for the Long Run
Plan and build towards our client's long term vision, with a sturdy, scalable, future-forward
technology foundation.

How We Create Opportunity
Our structured approach ensures a clear path from data challenges to tangible AI-driven value for your companies.
Assess: Discover & Define
Conduct a low-cost discovery to audit existing data infrastructure, evaluate data maturity, and map pain points to high-leverage AI opportunities. Deliverable: Data Maturity Assessment Report (2+ weeks).
Strategize: Roadmap & Design
Architect the target data infrastructure, define analytics and intelligence layers, and prioritize projects by ROI and feasibility. Deliverable: Strategic Roadmap & Architecture Document (2+ weeks).
Execute: Build & Implement
Deliver core services including ELT, data modeling, BI dashboards, ML forecasting, propensity scoring, and LLM integration. Implement production-grade AI solutions per the roadmap (initial build 8+ weeks).

Appendix
Case Studies
Stratus Data
Solving messy data and top-heavy product performance
In 2020, Nasco CEO Ken Miller noticed that messy data and outdated techniques were interfering with the accuracy of foundational company-wide initiatives. Miller teamed up with Stratus Data to upgrade Nasco to a data-driven organization.
Stratus Data
AI-optimized scheduling for national logistics operation.
Examinetics provides on-site medical screening and industrial hygiene consulting for more than 3000 companies in 18,000 locations across the contiguous United States...
Stratus Data
Transformed fragmented data for a beloved snack company
The client is a leading snack producer specializing in contract manufacturing and food service. When a private equity firm acquired a majority of the company’s shares in 2023, the PE partners saw an opportunity for improving the snack company’s use of data. At the time of acquisition, the snack company was maintaining more than 350 […]
Stratus Data
Custom data pipelines for a fintech company
“Instead of waiting days or weeks for reports to make decisions, I have the information at my fingertips to make live decisions on the fly.”

Articles
Forbes
Council Post: Three Types Of AI That Aren’t Generative—And Why They Matter
Generative tools may eventually reshape creative workflows, but predictive, optimization and data-driven AI are already reshaping business fundamentals.
Venturebeat
Replacing coders with AI? Why Bill Gates, Sam Altman and experience say you shouldn’t
In the race to automate everything – from customer service to code – AI is being heralded as a silver bullet. The narrative is seductive: AI tools that can write entire applications, streamline engineering teams and reduce the need for expensive human developers, along with hundreds of other jobs.
Forbes
Council Post: Why Most AI Projects Fail—And How To Build One That Succeeds
When companies focus on practical, user-centered implementation, AI can stop being an experiment and start having a real impact.