AI Value Creation
Turn the AI thesis into working systems.
Automation and, where it is justified, agentic AI that ship into production safely, with a human approval step on anything irreversible. Margin and efficiency you can measure, not another set of slides. We identify the highest-value workflows, build them, and put them into production, governed.
- Workflows built on the company's own data
- Human approval on anything irreversible
- Shipped into production, not left in a deck
- +280%
- AI advisory demand Q1 2026 (BluWave)
- 75%
- RevOps tasks AI-executed by 2028 (Gartner)
- HITL
- Human-in-the-loop governance
- 48h
- Onboarding to start building
A practical lever on margin, not a technology trend.
Assessed, automated, governed. Shipped into production.
Why AI matters for PE value creation
Worth attention only to the extent it moves the numbers in the value creation plan.
Treated that way, AI is a direct lever on the outcomes the hold depends on: margin improvement, operational efficiency, scalability, and competitive advantage, applied to workflow automation, reporting acceleration, revenue operations, and everyday process improvement rather than treated as a standalone innovation initiative.
The appetite is real and rising. Industry trackers reported a +280% surge in AI and ML advisory demand in Q1 2026 (BluWave), and Gartner projects that 75% of RevOps tasks will be AI-executed by 2028. The question is no longer whether AI matters to the thesis, but whether the portfolio company can actually turn it into working systems rather than stalled pilots.
Common AI challenges in portfolio companies
Why most portfolio AI plans stay on slides.
- AI idea on a slide No workflow chosen
- Data pulled together Fragmented, untrusted
- Pilot built Works in a demo
- Governance review No safe way to deploy
- Handed to teams Not trusted or used
- Live in production Governed, adopted
Fragmented data
AI needs a reliable data foundation, and most companies do not have one. Fragmented, duplicated, or untrusted data is the most common reason an AI initiative cannot get off the ground.
Lack of AI strategy
Interest without a plan: no clear view of which workflows would actually create value, so effort scatters across low-impact experiments.
Failed pilots
Initiatives that show promise in a demo but never reach production, because the gap between a pilot and a governed, live system is wider than it looks.
Governance concerns
Reasonable caution about letting an AI system act on its own, which stalls otherwise sound initiatives when there is no safe way to deploy them.
Adoption challenges
Systems that ship but are not trusted or used, so the efficiency gain on paper never materializes in practice.
The DevriX AI value creation framework
From opportunity to a governed system in production.
Grounded in the company's own data, not generic use cases.
A read on where AI can realistically create value in the business, grounded in the company's actual data and workflows rather than generic use cases.
A realistic read on where AI creates value in this business.
Impact and feasibility first, not visibility.
The opportunities ranked by impact and feasibility, so effort goes to the workflows that move margin and efficiency first, not the most visible ones.
A ranked list, so effort goes where margin moves first.
The fastest, lowest-risk efficiency gains.
The high-volume, rule-bound work identified and automated. These are the fastest, lowest-risk efficiency gains.
High-volume, rule-bound work automated first.
Where the value justifies it.
Where the value justifies it, agentic systems that handle more complex workflows, built to operate within defined boundaries.
Agentic systems built to operate within defined boundaries.
Efficiency without ceding control.
Human oversight and approval workflows on anything irreversible, with operational controls and risk management built in, so the firm gets the efficiency without ceding control over consequential actions. For PE-backed companies where reliability matters, this is what lets cautious initiatives actually ship.
Approval on anything irreversible, so cautious initiatives ship.
An initiative that stalls before production creates no value.
The systems put into production and into use, with optional ongoing ownership, because an AI initiative that does not reach production creates no value.
Systems in production and in use, with optional ongoing ownership.
High-impact AI use cases
Where AI tends to pay off first.
Revenue operations
Automating the manual work in the revenue engine, including lead qualification, routing, data hygiene, and the reporting that supports forecasting.
Reporting and analytics
Speeding the assembly of board-ready reporting and surfacing the signal in the data, so decisions rest on faster, cleaner information.
Customer service
Automating high-volume support workflows, with human handling reserved for the cases that need it.
Internal operations
Removing repetitive back-office process work that consumes team capacity without adding value.
Workflow automation
Connecting systems and automating the handoffs between them, so work moves without manual intervention.
Portfolio-wide AI programs
For a fund, AI is most valuable when it is applied consistently rather than reinvented at every company. DevriX can run AI value creation as a portfolio-wide program.
This connects to the broader Portfolio-Wide Value Creation Programs offer; AI is one lever within it.
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Program 01
Standardized frameworks
The same opportunity assessment and prioritization approach across every company, so each is evaluated and built on a consistent basis.
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Program 02
Shared best practices
What works at one company informs the next, with a data and pattern advantage that compounds as more of the portfolio adopts the approach.
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Program 03
Governance and compliance
A consistent human-in-the-loop and data-privacy standard across the portfolio, so AI is deployed safely and the fund can stand behind how it is governed.
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Program 04
Portfolio benchmarking
A common basis for comparing AI maturity and impact across companies, so the fund can see where to focus next.
Trusted by teams at
- Escape Hunt
- SwimSwam
- Brainjolt
- TechSmith
- American Restoration
- TrekTravel
- Angel Reyes
Why DevriX
The team that ships AI, not just recommends it.
Strategy and execution from one partner
DevriX identifies the AI opportunities and builds them. The plan and the production system come from the same team, which is precisely where most AI initiatives break.
Technical expertise
Senior product, data, growth, and platform engineers with 15 years in market, founded and led by Mario Peshev, a 5x CEO with two exits, advisor to more than 400 organizations, and author of MBA Disrupted. The team engineers high-traffic, regulated, GDPR-native platforms, the kind of environment where governed deployment matters.
Embedded operating model
Senior people build and deploy the systems, embedded with the company, with no model where partners advise and junior staff implement. Onboarding in 48 hours means the work starts fast.
AI-assisted delivery (company-reported)
DevriX also uses AI internally to accelerate its own delivery. AI-enabled workflows that improve execution speed and efficiency and contribute to faster implementation, an estimated 30 to 50 percent compression of delivery timelines per the company's own figures.
Long-term value creation focus
AI is treated as a lever on EBITDA, not an end in itself. DevriX pioneered the embedded operating-unit retainer model in our space in 2015, so the AI systems are built to be owned and run over the hold, with optional ongoing ownership not handed over and abandoned.
Proof points
A verifiable track record.
A verifiable track record.
Company figures across 15 years of embedded delivery.
- 15y
- Embedded operating model
- 2015
- Retainer model pioneered
- 92%
- Recurring revenue
- 5-10y
- Avg. client tenure
- 400+
- Advisory engagements
- 10+
- M&As facilitated
- ~1B
- Monthly pageviews engineered
- 4.8★
- Clutch, top-rated B2B
92% recurring revenue and a 5-10y average client tenure.
Vehicle distributor
A documented client automation outcome, not an autonomous AI system.
DevriX automated WhatsApp outreach and synced it to HubSpot, reducing manual work by 80%, replacing a manual, rep-by-rep process with a scalable, compliant workflow. Automation and workflow improvement, not an agentic or autonomous AI system.
80% less manual work, with a scalable, compliant workflow.
Long-term programs, not one-off projects.
Evidence DevriX sustains long-term programs rather than one-off projects.
- Smart Meetings Since 2015 (~10 years): 350% traffic growth, 4.7x Google sessions.
- Kustomer Since 2019 (later acquired by Meta).
- Senior Executive Media Since 2021, across multiple disciplines.
Smart Meetings, since 2015.
Where the market is heading.
Third-party industry trends, not DevriX results.
- +280%
- AI & ML advisory demand surge, Q1 2026 (BluWave).
- 75%
- Of RevOps tasks projected AI-executed by 2028 (Gartner).
BluWave and Gartner, third-party context.
Named, public client tenures.
- Years embedded
- 15
- M&As facilitated
- 10+
- Recurring revenue
- 92%
- Clutch, top-rated B2B
- 4.8★
- ~1B pageviews engineered
- 5–10y avg. tenure
- 48h onboarding
Escape Hunt
Delivery partner whose work led the same group to bring DevriX in for Boom Battle Bar, with a working site for every location.
SwimSwam
10+ years leading the path for the number one swimming media in the US through a series of engineering products and scalability innovations.
Brainjolt
Managing product, data, and engineering over the course of 8 years, through nearly a 10X growth stage in the first several years.
TechSmith
8 years of platform unification, synergy consolidation, and modernization of multiple solutions onto a proprietary FOSS multilingual build.
American Restoration
Bringing 25 brands under one multisite, so each post-acquisition integration is streamlined and each new brand has a scalable technical foundation for hitting revenue targets.
TrekTravel
+25% YoY online bookings after a new Express Checkout flow, which drove 15% of that growth.
Angel Reyes
182% YoY increase in website-driven signed cases after a platform rebuild with 100% uptime during migration.
Recurring revenue, monthly pageviews, M&A counts, and the 30-50% delivery-compression figure are company-reported and not externally audited. The vehicle-distributor outcome is an automation/integration example, not an autonomous AI deployment. Agentic AI is presented as a capability applied where justified, not as a track record of shipped autonomous systems. BluWave and Gartner figures are third-party context, not DevriX results.
Get started
Get the AI plan that actually ships.
Start with an AI assessment: a read on where AI can realistically create value in the business, the use cases ranked by impact, and a path to systems in production, governed with a human approval step on anything irreversible. Built and run by the same team.