Wexford Stern Operating Thesis

Operating Model

Foundry operates as a holding company with sector-focused operating platforms. Individual operating platforms focus on clusters of businesses with similar workflow structures - this structure allows each platform to develop domain expertise while leveraging shared transformation infrastructure developed centrally.

Financial Workflow Platform

Our initial area of focus is a class of financial services businesses that operate structured, compliance-driven workflows. These companies sit within the operational infrastructure layer of the financial system, enabling financial risk and obligations to be evaluated, documented, executed and monitored.

Across insurance, lending, servicing and compliance, the same underlying architecture recurs: document-heavy intake, rules-based decisioning, structured case management, regulatory verification and repeatable reporting. These shared primitives are more important than the end-market category and define the true boundaries of the sector.

This common operating model makes the platform particularly well suited to AI-enabled transformation. High levels of manual processing, fragmented data and human-in-the-loop decisioning create clear opportunities for standardisation, automation and margin expansion.

Our current UK screening universe comprises 469 companies with estimated enterprise values ranging from £10m to £1bn. Delegated underwriting and MGAs represent the largest cluster, with 181 companies mapped, followed by specialty lending with 160. The market skews heavily toward the lower mid-market, with 63% of companies below £50m EV and 87% below £100m.

Insurance workflows remain the most attractive initial entry point due to their density of these primitives, but the broader Financial Workflow Platform provides a more scalable framing than a narrow MGA-only lens. The current dataset is sufficient for market structure analysis and target identification, with further enrichment planned as the platform develops.

The Financial Workflow Platform sits between capital providers and end customers, with workflow operators acting as the operational layer that evaluates, routes, services and evidences transactions.

​​Acquisition Strategy

We’re building an operating system for AI transformation and deploying it into increasingly bigger deals, laddering up towards the enterprise.

Larger businesses contain more workflow volume, more data, and greater embedded inefficiency. As a result, the same system produces disproportionately greater gains when deployed at scale. Each successive acquisition increases both the size of the platform and the effectiveness of the system applied to it.

The system improves with each deployment, becoming more capable, more reusable, and faster to implement. This creates a compounding effect and the result is a non-linear scaling dynamic. Within a small number of transactions, the holdco transitions to enterprise-scale deals, this progression can allow us to reach approximately £5B in enterprise value within five years.

Workflow primitives and business archetypes

The most useful way to analyze this market is through workflow primitives. These are the repeatable operating units that recur across very different-looking businesses: intake, extraction, verification, routing, decision support, servicing, settlement and reporting. The same primitives reappear across delegated underwriting, claims administration, specialty lending and compliance operations. That is what creates the possibility of a shared automation stack.

Representative workflow lifecycle shared across many financial workflow businesses.

• Delegated underwriting / MGA businesses concentrate risk evaluation, pricing, broker coordination, policy issuance and bordereaux production.
• Insurance services, claims businesses, and carriers concentrate case management, evidence handling, QA, customer communication, reporting, and core underwriting workflows.
• Specialty lenders and servicers concentrate document intake, credit verification, collateral review, servicing and collections.

Financial Workflow Platform market map

Our initial market map is designed to show where workflow density, fragmentation and target-size concentrations sit, however it’s not yet a final database. The objective is to map the platform and identify structural patterns, not to present diligence-grade financials for every company.

• Scope limited to UK companies
• Universe screened to an estimated EV range of £10m to £1bn.
• Current mapped set contains 469 companies across 11 sectors and 4 broad archetypes.

The market is heavily weighted toward lower-mid-market targets. 63% of mapped names sit below £50m estimated EV, 87% sit below £100m, and only 10 companies sit at or above £250m EV. This is attractive for sourcing our first deals, however it demonstrates that this one niche vertical is limited in scale and opportunity, especially at the higher end of the market.

MGA Market Map

Our current UK mapping identifies circa 144 MGA businesses, with a strong skew toward the lower mid-market and only a limited number of scaled platforms. This distribution makes MGA an effective starting point.

The market is sufficiently fragmented to provide accessible entry opportunities, whilst still offering enough depth in the small and mid-market to support initial acquisitions and early platform development. It allows us to deploy the operating system in live environments where workflows are clear, measurable, and economically meaningful.

At the same time, the supply of large, enterprise-scale assets within MGA is limited. This constrains the ability to continue scaling purely within the vertical and naturally defines the boundary of the initial market.

This is acceptable to us since MGA is not the destination of the strategy but rather the entry point. It provides an ideal environment in which to establish the system and generate early proof of performance. From there, expansion follows into adjacent parts of the insurance value chain and into the broader Financial Workflow Platform, where similar primitives are found as well as businesses at significantly greater scale.

MGA sector deep dive

We're starting with MGAs because they sit at the intersection of durability, embeddedness, and operational intensity.

MGA businesses are deeply integrated into the financial system. They perform a core economic function, underwriting and distributing risk, within a regulated framework that is unlikely to be disintermediated. Demand is persistent, switching costs are real, and their role in connecting brokers, carriers, and insured parties is structurally embedded.

This satisfies the first part of our lens: we are acquiring into a category that should still exist in decades.
Within that durable category, MGAs are highly operational businesses. Their performance is not primarily constrained by demand or pricing power, but by how effectively work is executed. Core functions such as submission handling, underwriting support, policy administration, and reporting remain heavily manual, often relying on fragmented systems and repeated human intervention.

As a result, a significant portion of cost is concentrated within a relatively small set of repeatable workflows. These workflows define the operational core of the business and represent the primary surface for improvement. Improving how this work is executed does not just reduce cost, it enhances the competitive position of the business.

More efficient underwriting workflows improve responsiveness and consistency, strengthening broker relationships. Better data capture and processing improve risk selection and reporting, strengthening carrier relationships. Reduced operational friction allows higher-quality underwriters to focus on decision-making rather than process, improving talent density and retention.

In this way, productivity gains compound beyond margin expansion. They reinforce the core relationships and capabilities that define long-term success in the market.

Ideal MGA targets

We seek MGAs with embedded franchise value and clear upside from automation.

Embedded value comes from access to capacity from multiple carriers; durable broker distribution; and deep expertise in lines where conditions are likely to harden over time.

In order for automation to materially uplift EBITDA, MGAs must have labour-heavy opex, a high proportion of manual workflows, high spend on software and IT consultancy. Where automation matters most is not necessarily in pricing. It is in the heavy, repetitive operating flows around e.g. submissions, referrals, bordereaux, endorsements, renewals. Addressing these these properly can create value without needing a heroic growth case.

They should be specialist enough to maintain margins, sufficiently high volume to deliver returns from automation, and big enough that carriers and brokers care about service quality.

We like SME and lower mid-market commercial lines (eg cyber for SMEs, professional indemnity, and contractor/trades liability), where submission volume is high and workflows are messy. Specialist commercial niches such as marine cargo, fleet/commercial auto, and renewable energy contractors packages are also attractive, because they have real underwriting IP but enough volume to industrialise parts of the workflow.

The UK MGA market is attractive because it combines capital-light underwriting models with genuine specialization, fragmented asset supply and a growing delegated authority backdrop.

The AI transformation opportunity