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Build vs Buy AI: Should Private Equity Firms Develop Custom AI Applications?
- 7.8 min read
- September 10, 2026
The build vs buy AI decision in private equity firms is not binary. Commodity capabilities such as transcription, translation, generic summarization, and document extraction are better bought. Building custom AI applications, either internally or with a specialist partner, makes more sense when the AI must reflect proprietary investment theses, institutional memory, portfolio KPIs, or firm-specific workflows. For most PE firms, the practical answer is a hybrid AI approach, where firms buy proven foundation models and build the orchestration, giving it proprietary context.
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7.8 min read
- September 10, 2026
The build vs buy AI decision in private equity firms is not binary. Commodity capabilities such as transcription, translation, generic summarization, and document extraction are better bought. Building custom AI applications, either internally or with a specialist partner, makes more sense when the AI must reflect proprietary investment theses, institutional memory, portfolio KPIs, or firm-specific workflows. For most PE firms, the practical answer is a hybrid AI approach, where firms buy proven foundation models and build the orchestration, giving it proprietary context.
The Hybrid “Tech Sandwich”: Why This Is Not a Binary Choice
AI has changed the economics of software development, but faster development still hasn’t eliminated the costs of operating, governing, securing, and maintaining production AI. Recent build-versus-buy frameworks increasingly recognize a third path, i.e., to combine bought infrastructure with differentiated capabilities developed internally or with a partner. For PE firms accelerating AI adoption in private equity, this creates a “tech sandwich”:
| Layer | Approach | PE example |
|---|---|---|
| Foundation models | Buy via API | Frontier models accessed through APIs |
| Commodity utilities | Buy | Transcription, translation, generic OCR |
| Orchestration and retrieval | Build or partner | Retrieval across deal history and portfolio KPIs |
| Workflows and guardrails | Build or partner | IC memo workflows, variance rules, LP reporting |
The differentiator is how proprietary data, workflows, permissions, and investment context surround that model. Kairos, Brownloop’s layer of intelligence, reflects this architecture, while preserving institutional memory and orchestrating AI across PE workflows.
Turn proprietary data and workflows into AI capabilities competitors cannot simply buy.
When to Buy AI: Four Signals for PE Firms
Buying makes sense when the capability is standardized, and rapid deployment is more important than customization.
1. Standard Utility (No Advantage Gained)
Generic transcription, translation, and document extraction rarely create proprietary advantage. These are established examples of how private equity teams use AI without needing to own the underlying capability: meeting transcription, cross-border translation, and routine document processing.
2. Speed Needed (Weeks, Not Quarters)
When a tested solution must go live in weeks, buying can provide faster time-to-value. Consider a portfolio company onboarded mid-fund-cycle that immediately needs a reporting capability. Waiting quarters for a custom build may create more operational cost than strategic benefit.
3. Low Maintenance Burden
Uptime, model and API changes, security patching, monitoring, and support become ongoing data strategy implementation challenges, particularly when lean technology teams are already integrating fragmented fund, portfolio, and operating systems. AI-assisted development may accelerate prototypes, but production ownership is a long-term commitment.
4. High Regulatory Risk
When to Build AI Solutions: Three Signals Worth the Investment
The build vs buy AI equation changes when proprietary data materially improves its output or the firm needs greater control over its data environment.
1. Core Intellectual Property
Build when the model or agentic workflow is itself an intellectual property or a primary value driver. At the fund level, this may be relatively uncommon. An AI-native portfolio company, whose product depends on proprietary models or workflows, may find that ownership is fundamental to enterprise value.
2. Proprietary Data Advantage
Historical deals, diligence findings, IC rationale, portfolio operating data, and evolving investment theses contain insights unavailable to public foundation models. When there is a layer of institutional intelligence in private equity, AI interactions are not isolated prompts. Firms can connect historical decisions and proprietary context into reusable memory. Kairos is designed around this principle, using a compound knowledge graph that preserves firm-specific intelligence.
3. Strict Data Sovereignty
Custom architecture can also make sense when sensitive deal, portfolio, or investor information requires isolated infrastructure or greater control over data location and model access. Data residency, permissions, and security requirements should therefore enter the architecture decision early, particularly when LP DDQs or jurisdictional requirements impose additional scrutiny.
A 5-Question Decision Framework
Technology leaders should assess the use case alongside the firm’s AI maturity model for private equity. Their data alignment, security, governance, and workflow maturity must be capable of supporting it. Leaders should ask:
- Does this touch proprietary deal or portfolio data? → If yes, lean toward build or partner-build.
- Would the firm lose its edge if competitors bought the same tool? → If yes, lean toward build.
- Does it need to go live in weeks or quarters? → If weeks, lean toward buy or partner.
- Does the firm have DevOps, MLOps, and SecOps capacity to maintain it? → If no, lean toward buy or partner.
- Will LPs, regulators, or portfolio companies scrutinize governance? → If yes, prioritize a compliant vendor or governance-first partner.
An AI and private equity readiness checklist can then turn these questions into concrete requirements before procurement or development begins.
Where Brownloop and Kairos Fit: A Hybrid Approach to AI Development
Pure in-house development can place an unnecessary burden on engineering teams, but pure off-the-shelf AI misses the proprietary context. Brownloop occupies the partner-build middle, where foundational models and infrastructure can be bought, while PE-specific orchestration and custom workflows create the layer of differentiation.
For technology leaders evaluating how to choose an AI partner for a private equity firm, the question is therefore not only what a provider can build. It is whether that partner can help the firm own what differentiates it without forcing it to reinvent what does not.
Frequently Asked Questions
Should we build or buy AI for our firm?
Should we build or buy AI for our firm? Buy for speed and standardized needs. Build or partner-build when proprietary data, workflows, or institutional knowledge create differentiation.
What is the hybrid or “tech sandwich” approach to AI?
What is the hybrid or “tech sandwich” approach to AI? It combines bought foundation models and infrastructure with custom orchestration, retrieval, governance, and workflows that preserve firm-specific advantage.
When does building custom AI make sense for a PE firm?
When does building custom AI make sense for a PE firm? Build when proprietary data creates an edge, workflows require customization, or data sovereignty demands greater control.
What are the risks of building AI in-house at a PE firm?
What are the risks of building AI in-house at a PE firm? Key risks include longer timelines, ongoing maintenance, specialized talent requirements, and greater responsibility for security and governance.
Turn proprietary data and workflows into AI capabilities competitors cannot simply buy.
