What Tech Due Diligence Teaches Us About Scaling AI Businesses
- Agu Aarna
- 1 day ago
- 3 min read
When private equity firms evaluate AI-driven companies, they’re not just looking for clever models or a slick interface. They’re asking a deeper question: is this technology defensible, scalable, and operationally ready to deliver value at scale?
That’s the lens through which we find ourselves conducting technical due diligences across the board. These engagements explore the company’s architecture, team, and product maturity - and ultimately conclude on the target’s strength and build for sustainable growth specifically with AI in the picture.
But more importantly, the findings offer valuable lessons for any company seeking to turn AI innovation into an investable, scalable business.
Are you seeking PE investment or evaluating an AI business? We'd be happy to spar and strategize!
True Differentiation Lies in Data and Domain Logic
Deep AI utilization success isn’t simply the result of onboarding LLMs or having fine-tuned open-source models like (e.g., Phi3 or LayoutLMv3). Its edge comes from how those models are applied:
Trained on proprietary data, not generic corpora
Guided by complex validation and compliance rules unique to associated workflows or business processes
Deployed as region-specific models to align with local formats and regulations and reducing bias
This combination makes replication by competitors difficult - and builds long-term defensibility.
Your model architecture is rarely your moat. Your data, domain context, and process logic are.
Scalable Architecture Enables Business Agility
Well designed and segmented granular, and possibly event-driven microservices architecture, using an event queue (e.g., Kafka) and data-optimized (e.g., database-per-service) pattern, creates flexibility to evolve quickly as demand grows. Modular architecture unlocks options for resilient and cost-efficient infrastructure, while creating opportunities to rapidly and more deeply segment and test the workflows increasing system accuracy.
Scalability isn’t about servers; it’s about architectural independence. Design systems so each capability can grow or change without re-engineering the whole platform.
Stability and Investment in Talent Are Competitive Advantages
We’ve found cohesive leadership teams with over six years of collaboration can really build and subsequently harness domain knowledge creating basis for conscious decisions oriented for customer and business success. Smart combination of near/far-shoring and FTEs retains engineering efficiency while keeping the business close to customers and maintaining cost-effective delivery.
AI products evolve fast - but stability in leadership and knowledge retention allows that evolution to stay coherent. Sustained R&D spending is not optional; it’s strategic.
The Hidden Bottleneck: Data Preparation
Despite deeper workflow segmentation, sub-optimization, and various automation tools at organizations’ disposal, data collection and preparation remains one of the key elements that businesses struggle with, having this challenge shared by most AI companies, even successful ones. Interestingly, yet also logically, building an AI company technically starts and ends with data.
AI scale depends less on compute power and more on data pipeline maturity. Streamlined labeling, validation, and feedback loops are where growth either accelerates or stalls.
Infrastructure Readiness: Scale With Compliance in Mind
For smaller businesses, operating from a single region cloud setup is an efficient first setup. However, a single-region dependency could create regulatory or availability risks as the business expands internationally. If a business plans to onboard PE investment, expansion is unavoidable, and having at least plans tackling multi-data-center deployments is a must.
Multi-region, multi-cloud infrastructure is no longer a “later item”. It’s part of early risk design, ensuring compliance and resilience scale alongside revenue.
From Innovation to Investment-Readiness Through Tech Due Diligence Lense
The conclusion of our work so far is clear: to demonstrate a disciplined balance between innovation and scalability, the architecture, proprietary data, smart usage of AI, and stable leadership provide a solid foundation. The following technical growth chapter can then hinge on operational excellence - enhanced automating, expanding infrastructure, and refining compliance.
For AI companies, that journey reflects a broader truth:
Building AI is about invention. Scaling AI is about discipline. The businesses that master both become not just investable - but enduring.
About Intium
We are a technology advisory firm specializing in technical due diligence on buy and sell side, engineering strategy, and value creation support for institutional and strategic investors and high-growth technology companies.
