Saakshi Saggu works at the centre of NakedAI's internal intelligence and operational coherence, holding the systems that allow complex work to remain structured, traceable, and usable over time.

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She joined NakedAI directly from school, following her studies at Lady Eleanor Holles, where she focused on English Literature, Psychology, and History of Art. She has since taken ownership of the firm's internal knowledge base: the single source of truth across research, transcripts, decisions, and analytical artefacts, consolidating fast-moving work into a coherent, governed environment.

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Alongside this, Saakshi has contributed to the firm's record architecture: the system through which every engagement's reasoning is captured, retrieved, and reused. This work is carried out under Malcolm Maxwell's supervision as part of her architectural training, where she is developing deep familiarity with AI concepts, system logic, prompting structures, and analytical frameworks. Her role is not observational. She is embedded in the work, learning how decisions translate into architectures, how assumptions introduce risk, and how governance must be carried through systems rather than described abstractly.

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Saakshi brings a particular focus on the human dimension of AI work. She looks at how people behave around new systems, where adoption breaks down, where resistance emerges, where trust holds or fails, and what unintended consequences surface once a decision is in motion. Technology fails when organisations misread how people behave around it. Her contribution is to keep that visible inside every engagement. Within the team, this is what makes her the firm's red team. She tests claims, framings, and outputs with a cold human eye, flagging where the business logic or the AI logic has missed the human one. The principals rely on her to do this honestly, even when the answer is inconvenient.

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Within the firm, Saakshi is relied upon to maintain continuity across fast-moving work, to spot inconsistencies, and to surface gaps before they become structural problems. Her contribution is deliberately behind the scenes, but foundational: ensuring that NakedAI's thinking remains disciplined, cumulative, and operationally sound.