Turn AI capability into repeatable portfolio value.
Private equity does not need another collection of isolated AI pilots. It needs a repeatable way to identify, build, measure, and scale value across the investment lifecycle.
Dual Logic helps funds improve the work of investing and build practical AI capability inside portfolio companies—combining investment judgment, operating knowledge, implementation, and measurable ownership.
What we hear from private equity firms
Three things come up in the first conversation, almost every time.
Pilots in every company, a pattern in none
Each portfolio company is trying something, and nothing carries across.
Plans that stop at productivity
Value-creation plans mention AI without showing what it does to EBITDA.
Diligence under time pressure
The data room is bigger than the time there is to read it.
The difficulty is turning scattered activity into an approach that is repeatable without ignoring the differences among companies.
Where the opportunity sits
- Inside the fund: sourcing, diligence, investment work, portfolio monitoring and LP operations
- Across portfolio companies: products, processes, workforces and operating economics
The goal is to remove the friction that gets in the way.
Each piece of the work that matters, and the AI support that gives people more time for it.
Developing differentiated investment judgment
- Market mapping and sourcing
- Competitive and regulatory intelligence
Finding evidence others miss
- Commercial and operational diligence
- Document and data-room review
Testing an investment thesis rigorously
- Deal screening
- Investment-committee preparation
Working productively with management teams
- Portfolio monitoring
- AI governance and leadership capability
- Product and service enhancement
Identifying the few operational changes that materially affect value
- Revenue and go-to-market improvement
- Customer operations
- Software-delivery acceleration
- Back-office automation
Transferring useful patterns without forcing every company into one playbook
- Internal knowledge reuse
- Data-foundation improvement
Showing how operating improvement becomes financial value
- LP reporting and communications
- Finance and reporting
How Dual Logic helps
1 · Strategy & Vision
Decide where AI should change how your organization works.
At the fund level, define where AI changes investment work. At the portfolio level, identify company-specific opportunities tied to the value-creation plan, holding period, operating maturity, and exit thesis.
2 · Literacy & Enablement
Help people get better at the work AI changes.
Build practical skill among deal teams, operating partners, portfolio leadership, and functional champions. Create a shared language without assuming each company needs the same tools or use cases.
3 · Design & Build
Build AI around the work that matters.
Develop fund-level research and knowledge systems or portfolio-company products, workflows, automations, and engineering capabilities.
4 · Operate & Enhance
Turn the capacity AI creates into industry-leading margins.
Track reliability, adoption, economics, and operating value. Maintain reusable standards while improving each implementation based on company-specific evidence.
From a client
“Part project manager, part architect, part AI guide.”
Portfolio-company capabilities that remain valuable through exit.
What we aim for, and how we would know. Each aim gets a baseline in discovery and a target you agree to.
Faster, deeper investment work
Measured byTime from first look to investment-committee decision
A more systematic view of AI opportunity and risk
Measured byShare of data-room documents reviewed, not sampled
Value-creation plans that move beyond productivity claims
Measured byOpportunities assessed per portfolio company, scored the same way
Reusable methods across the portfolio
Measured byMethods reused across portfolio companies
Faster movement from opportunity to operating impact
Measured byTime from identified opportunity to live system
Clearer measurement of EBITDA, growth, or product value
Measured byEBITDA, growth or product impact attributed to each initiative
Scaling marketing output without scaling headcount
50+MQLs a month, with no new hires2–3xCampaign conversion against B2B SaaS benchmarksDual Logic worked inside Paperclip’s marketing team for about 50 hours a month, with AI built into research, content and campaigns. The funnel was rebuilt and lead scoring added, with no new hires.
Software as a ServiceRead the case studyDeveloping internal champions to support AI integration across an 800-person SaaS
25%+Improvement in sales rep productivity60+daysEarlier identification of renewal riskEmburse’s leaders went first, through blended training and biweekly cross-functional coaching cohorts. The program found the internal champions and produced an organization-wide 2026 roadmap.
Software as a ServiceRead the case studyBuilding scalable support within federal compliance standards
FedRAMPCompliant AI solution identifiedCoSo Cloud's support leaders were covering nights and weekends. Dual Logic analyzed the support workflows and found a path inside their FedRAMP boundary: Salesforce Agentforce, on the Service Cloud they already ran. They now have a validated plan for AI triage and routine responses that scales support without adding headcount.
Professional ServicesRead the case studySee every case study
Each one covers the business problem, the work we did and the result. Filter them by industry or service.
Does Dual Logic work with private equity funds or their portfolio companies?
Dual Logic works with both: the fund’s own investment work and the portfolio companies it owns. At the fund level, that means AI for market mapping, deal screening, investment-committee preparation, portfolio monitoring and limited partner (LP) reporting. At the portfolio level, it means capability built inside each company and tied to its value-creation plan. The two can run together or one at a time.
Can one AI approach work across several private equity portfolio companies at once?
Yes, an AI approach can work across several portfolio companies if it shares methods and standards without forcing every company into one playbook. Dual Logic keeps reusable standards, scoring and governance across the portfolio, then tailors each implementation to the company’s products, systems, workforce and operating maturity. Each company is assessed the same way, so the fund can compare results and reuse what works.
How should a private equity firm tie AI to the value-creation plan and measure the results?
A private equity firm should score every AI opportunity against the value-creation plan, the holding period and the exit thesis before anything is built. Dual Logic then attributes EBITDA (earnings before interest, taxes, depreciation and amortization), growth or product impact to each initiative, measured from a baseline set in discovery, so the plan moves beyond productivity claims.
What happens to a portfolio company’s AI capability at exit?
The AI capability stays with the portfolio company at exit: the systems, the skills and the documentation are its own. Dual Logic plans for that from the start. We document what we build, train the company’s people to run and improve it, and agree up front who runs it afterwards, so the capability stays valuable through the sale and under new ownership.
Where should a private equity firm start with AI?
A private equity firm should start in one of two places: a fund-level assessment of where AI changes investment work, or one portfolio company that becomes the pattern for the rest. The second suits a fund that wants proof inside one company before it scales. Either way, Dual Logic scores opportunities the same way, so the next company starts from evidence rather than from scratch.
Start with the work your organization most needs to improve.
We will help determine what needs better tools, what needs a redesigned workflow, what needs a custom build, and what should remain distinctly human.