When teams are stretched, the obvious answer is to add more capacity. But in front office, risk and quant technology, the constraint is rarely just headcount. 

E-trading platforms, pricing libraries, risk engines and quant models can’t fail, and the people who understand them are scarce and expensive. Internal teams are already stretched across new delivery, modernisation, regulatory change and BAU. Using partners or contractors to fill individual gaps can relieve some pressure, but doesn’t fix the underlying issue. The harder work is building a team that operates as part of the bank, not a collection of CVs. 

What the solution looks like in practice is a team of specialists, embedded in the client’s environment, built around a specific platform, function or objective, and accountable for outcomes over time. At Phi, we call this a pod.

Anatomy of a pod

A pod has a hands-on lead, delivery consultants with the right mix of skills and seniority, and – where it makes sense – graduate consultants to support continuity. Senior practice oversight sits behind it. The pod uses client tooling, client governance and the client’s release cadence, while drawing on Phi’s experience from similar environments. 

Pods are built around the skills and roles the work requires, as opposed to following a standard template. A model development pod needs quants and quant developers. A risk platform takeover might need risk-literate developers, DevOps and production support skills. The composition follows from the outcome the pod is accountable for. 

Seniority matters too. A pod isn’t a pyramid of management – it’s a structure in which senior practitioners design and build alongside the team. The mix of seniority is what makes pods both productive and cost-effective. 

This isn’t a post about AI, but it’s worth saying: AI is changing how the work gets done – by our pods and by our clients’ own teams – but it doesn’t change the argument. AI makes good teams more productive, but the pod advantages hold regardless of the level of AI adoption among engineers. 

Location as a Capability, Not a Cost Factor

There’s an assumption worth addressing directly: that an individual contractor in London, Paris or New York is an expert, and a nearshore pod is something less. In our experience, the opposite is often true. 

The senior people in our delivery centres are not graduate-pool engineers. Our locations are chosen specifically for the availability of deep specialist talent – quants, quant developers, risk technologists, e-trading engineers – drawn from a mature local financial services ecosystem or, in many cases, repatriated from tier-1 banks in the major financial centres. Casablanca anchors our quantitative practice. Budapest sits at the quant-to-tech intersection. Bucharest is our front office and risk technology hub at scale. Our local practice leaders are senior practitioners who have built and run quantitative, front office or risk technology teams at major investment banks.

A pod isn’t a cheaper version of an on-site contractor. It’s a different model for accessing specialist expertise, in locations where deep capital markets talent already exists. 

What a pod looks like in practice 

For a Global Asset Manager modernising its strategic quant library, we provided a 3-person pod based in our Casablanca Centre of Excellence to implement inflation product models – swaps, options and curve building for valuation and risk. The pod delivered the new models in one month against the client’s original three-month expectation. This was a small team, with a sharp scope, and with senior quant leadership behind it. 

That is the unit of delivery. Now consider what happens when you scale it. 

From pod to Virtual Delivery Centre 

A Virtual Delivery Centre (VDC) extends the same model across multiple pods and workstreams. Clients gain a dedicated, specialist delivery location as if it were their own – without setting up a local entity, building recruitment, hiring local leadership and managing retention.

For one Global Investment Bank’s risk re-engineering programme, we run a VDC of 65+ consultants in Bucharest, working in 11 pods across market risk, credit risk, FRTB, stress testing, ETL pipelines, risk engines, UI, aggregation and data engineering. The team ramped over six months and has now been operating for around two years. The client’s engineers use approved AI coding tools extensively as part of their daily workflow; our pods use the same tools in the same way.

From a focussed inflation product quant pod, to a multi-pod VDC for risk technology, the model can be scaled to fit the work. 

The importance of this approach today

Our clients are juggling more programmes than ever: modernisation, simplification, regulatory change and the usual business-driven delivery. When the work is more than their own teams can absorb, the usual answers come with a cost: filling gaps one person at a time is slow and management-heavy, while generic outsourcing can bring volume but at the price of domain knowledge.

Our pod model sits somewhere between augmentation and outsourcing. We can bring senior expertise where it matters, and scale around it. A focused pod can grow vertically as scope expands, or additional pods can be added horizontally to form a VDC. Our clients get a stable team that integrates into existing ways of working. Knowledge is held by a team, so continuity is less dependent on one individual staying in a seat.

Capital markets systems are architected with care. The teams that build them should be too.