Driving Returns through
Tech, Data & AI
The New Value Equation: leverage and multiple expansion no longer carry a deal. Returns come from operational value creation across the hold, and that runs on systems, data, and AI.

PRIVATE EQUITY PARTNERS
Value Creation Timeline
Tech DD
Audit the target's tech, data and security before you sign. Risk priced, upside quantified.
Seven-dimension tech DD. The red flags that move the price. The upside the seller never built.
First 100 Days
Turn the investment thesis into a tech and data value creation plan. Baseline set, quick wins shipped.
100-day plan. Reporting baseline live by day 30. First AI use cases scoped.
Value Creation
Execute the roadmap. Centralized data, AI leverage, operational visibility across the company.
Data hub and BI layer. AI in the P&L, not the deck. Quarterly value tracking.
Exit-Ready
Make tech a valuation premium, not a diligence risk. The next buyer finds a platform.
Mock tech DD. Key-person dependencies removed. Data governance as a selling point.
Our Private Equity Expertise
Before the LOI, we audit the target across seven dimensions: technical debt, security, data quality, team. You get a risk-adjusted view of what you're buying and what it should cost.
We turn the investment thesis into a tech and data roadmap with a number attached. Every initiative tied to EBITDA, valuation, or risk. No roadmap for its own sake.
The first hundred days set the tone. Reporting baseline by day 30, quick wins by week eight, the value creation plan sequenced and owned. We've run enough of these to know what slips.
Financial, operational and commercial data consolidated automatically. Stop chasing manual month-end packages from twelve different stacks.
AI that shows up in the P&L, beyond POCs. We find the use cases that move margin or throughput inside a portfolio company, scope them, and ship them.
We evaluate proprietary tools: keep and refactor, migrate to SaaS, or sunset? Experienced CTOs who've made these exact decisions so you focus P&L where the company value stands.
Unified data models, automated pipelines, access controls. The frameworks that become a valuation driver when the next buyer's DD team finds a platform, not a pile of spreadsheets.
When a portfolio company needs senior tech leadership now, we step in. CTOs who've made the build-versus-buy calls and carried the roadmap.
How We Work with Your Portfolio
We screen the portfolio with you to find the companies where IT, data or AI is holding back value, or hiding it.
We sit down with the portfolio company's CEO to qualify the situation and scope what's actually at stake.
A focused assessment: where the gap is, what it costs, and what closing it is worth. Tied to a number and a timeline.
We deliver the plan and stay to drive it. Advice you can't execute is just a slide.
Customer Stories

Blackroom: An AI-native M&A data room, with Claude in the product.
Stratos built Blackroom to challenge two-decade incumbents in the virtual data room market, with Claude powering the intelligence dealmakers use inside the room.

Castalie: How Stratos Guided Castalie’s IT Roadmap for Scalability
Stratos partnered with Castalie to audit and overhaul an aging IT infrastructure. By moving away from a limiting monolith to a "Best of Breed" stack, Stratos designed a roadmap to secure revenue, optimize field operations, and support Castalie’s rapid growth.

Chronos Dental - Digitalizing the Dental Lab Network
Chronos Dental modernized its network of laboratories by deploying a custom tool to replace manual workflows with real-time traceability and a seamless digital link between dentists and prosthetists.
Insights
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The Mismatch Trap on CRM: Why One Size Does Not Fit All in Business Services CRM
Your CRM succeeds only when it reflects how you actually go to market

EFOR: Discipline Before Digitalization
EFOR’s success shows the paradox: no tools until 100m of revenue, just world-class execution
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The Current State of Systems, Data, and AI in B2B Services
B2B services firms show strong delivery digitization but still operate with fragmented systems, complex data, and uneven AI adoption.