Forward Deployed Engineering (FDE) for a Big Four AI innovation projects

A Big Four firm faced growing client demand for AI solutions, but many of the proposed use cases carried real uncertainty: would the model perform on the client’s actual data, would users adopt it, and would the business case hold? Traditional delivery models made it expensive to find out, requiring long scoping phases and large upfront investment before any value was proven. On top of that, each new engagement tended to start from scratch, so lessons and assets from previous projects were rarely reused. The firm needed engineering capacity that could work close to the business, move at startup speed, and still meet the security, compliance, and quality standards expected of a Big Four.

We embedded a team of forward deployed engineers within the firm’s engineering organization, working side by side with consultants and client stakeholders. The model rests on three pillars.

Validate fast, then commit. Each use case starts with a short, time-boxed cycle that delivers a working prototype on real data rather than a slide deck. Clear decision gates determine whether a use case moves forward, pivots, or is stopped, so investment follows evidence.

Gen-AI accelerated delivery. The team uses generative AI throughout the lifecycle: refining specifications, exploring designs, generating and reviewing code, and producing tests and documentation. This shifts engineering effort away from boilerplate and toward the judgment calls that determine quality.

Componentize everything. Every project is expected to leave something reusable behind: agent patterns, prompt libraries, data connectors, evaluation harnesses, reference architectures, and spec templates. These are curated into a shared library so each new engagement starts further ahead than the last.

When a use case proves its value, the same team hardens, scales, and deploys it to production, avoiding the knowledge loss of a handoff between “pilot” and “delivery” teams.

In less than three months, many AI use cases tested, with two already in production. Time from kickoff to a first working prototype reduced from several months to weeks. 100% of new projects now start from existing components rather than from a blank page, and unviable use cases are identified and stopped within less than two weeks, before significant budget is committed.

For the firm, the engagement delivers faster time to value, lower risk when investing in AI, and a growing library of proven assets whose value compounds with every project. The team augmentation model gives flexible, specialized capacity that scales with demand without the overhead of building it in-house. For the firm’s end clients, it means seeing a working solution early, making go/no-go decisions based on evidence rather than assumptions, and reaching production with systems built to enterprise standards from day one.