Teresa Dickens

Why this persona fits the audience

She leads a 5-person engineering team at a B2B SaaS company and is the person who reviews how the team uses model APIs in production. Her team has already standardized on one main LLM provider for some endpoints, while other internal scripts still call different models depending on task type, cost, and response speed.

• Age: 38
• Location: London, UK
• Occupation: CTO, SaaS company
• Income: £120k-£180k
• Education: Master's in software engineering
• Company size: 12 total employees (4 sales/marketing, 8 engineering/product)
• Years in role: 2.5

“You can't automate your way out of bad architecture; you just get faster at breaking things.”

Teresa Dickens is a seasoned technology leader whose career trajectory reflects a deliberate shift from pure academic rigor to pragmatic startup execution. Born in Manchester and educated at Imperial College London, where she earned her Master's in Software Engineering with a focus on distributed systems, she spent her early twenties building scalable backend infrastructure for fintech giants in the City. However, the allure of building something from the ground up drew her to the London startup scene six years ago. She joined her current employer as its first engineering hire, transforming a two-person prototype into a functional B2B SaaS platform with a dedicated team of five. Now, at 38, Teresa sits at the helm of the engineering department, navigating the turbulent waters of AI integration. Her current reality is defined by a complex patchwork of model usage. While the company has standardised on a primary Large Language Model (LLM) provider for its core customer interactions, ensuring stability and predictable pricing, the internal tooling and experimental scripts remain fragmented. Different team members have independently selected various models for tasks ranging from code generation to data summarisation, driven by immediate needs for speed or cost reduction. This lack of centralised governance has become Teresa's primary pain point. She spends a disproportionate amount of her week auditing these disparate connections, reconciling billing statements, and troubleshooting why a script behaves differently on one provider versus another. Despite the chaos, Teresa thrives on the challenge. She is a proponent of 'structured autonomy,' allowing her team to experiment but demanding rigorous post-mortems and documentation. She is deeply invested in the concept of intelligent auto-routing, seeing it as the only viable path forward to reduce the cognitive load on her engineers and the financial bleed from inefficient API calls. Her income, hovering between £120k and £180k, affords her a comfortable lifestyle in Shoreditch, but she views money primarily as a resource to be allocated efficiently rather than a status symbol. With a background in system design and a personality skewed heavily towards conscientiousness, she is driven by a desire to build a resilient, scalable foundation that can support the next phase of growth without collapsing under the weight of technical debt. She is currently evaluating whether a unified abstraction layer could solve her routing headaches, though she remains wary of vendors promising 'one API to rule them all' without proof of performance.

Background & daily life

A day in their life

Teresa wakes at 6:15 AM, avoiding screens until after a 45-minute run along the Thames Path. Breakfast is a quiet affair, often reviewing overnight CI/CD logs on her phone. By 8:45 AM, she is cycling to her office in Shoreditch, arriving just in time for a stand-up where she addresses a critical latency spike in the customer-facing chat widget. The morning is dominated by a deep-dive session with her lead backend engineer to refactor the routing logic for their billing agent, a task currently hampered by inconsistent responses from their secondary model provider. Lunch is a standing meeting with the CEO to align on roadmap priorities, followed by two hours of focused coding or documentation. Afternoon meetings cover hiring interviews and a sync with their cloud architect. She leaves by 6:30 PM to attend a local tennis match, switching off mentally until dinner, where she might read a paper on transformer efficiency before bed.

Lifestyle

Teresa lives in a meticulously organised flat where every tool has a designated spot, reflecting her need for control in a chaotic industry. She maintains a rigorous fitness routine, playing competitive doubles tennis twice a week which serves as her primary stress relief from screen time. Her financial habits are conservative yet strategic; she maximises pension contributions and invests in long-term tech stocks rather than chasing crypto trends. Socially, she prefers deep conversations over large gatherings, often hosting small dinner parties for fellow founders or old university friends. She is an avid gardener, tending to a compact rooftop plot where she grows herbs and tomatoes, finding the slow growth of plants grounding compared to the rapid iteration cycles of her job.

Work life

Her days are split between architectural planning and hands-on code review. As the sole technical leader, she balances the immediate needs of a five-person dev team with the strategic vision required by investors. She manages a hybrid environment where the core product relies on a single, stable LLM provider, but legacy scripts and experimental features still route through disparate models based on ad-hoc cost-benefit analysis. This fragmentation creates significant overhead: she spends hours debugging why a specific prompt failed on Model A but succeeded on Model B. Her team operates with high autonomy, but she enforces strict accountability regarding API costs and uptime. The pressure is constant—balancing feature velocity against system reliability in a market that demands both instant AI capabilities and ironclad security.

Relationships

Teresa is close with her older brother, a civil engineer in Manchester, whom she calls weekly for perspective outside tech. Her network is heavily concentrated in London's startup ecosystem, specifically among CTOs of Series A companies. She relies on a trusted peer group of three other female engineering leaders for confidential advice on scaling challenges. At home, she has no children but shares a cat named 'Stack Overflow' with a partner who works in finance, creating a dynamic where they discuss risk management differently. She is influenced more by technical mentors from her Master's programme than by industry influencers.

How they decide

Analytical and evidence-based. Teresa rarely makes decisions based on marketing narratives or early adopter hype. She requires concrete data points, preferably from peer benchmarks or small-scale POCs, before committing to any infrastructure change. She is cautious about vendor lock-in and prefers solutions that offer clear migration paths. While she values speed, she will always pause to verify that a proposed shortcut does not introduce hidden technical debt. Her process involves gathering requirements, running a quick cost/benefit simulation, and consulting her lead developers before final approval.

How they sound

Teresa speaks with a clipped, precise RP accent, typical of London professionals in high-tech sectors. She avoids hyperbolic language like 'revolutionary' or 'game-changing,' preferring terms like 'robust,' 'efficient,' and 'proven.' Her cadence is steady and measured; she pauses to ensure clarity rather than filling silence. She frequently uses metaphors drawn from systems engineering and tennis strategy. She never admits to uncertainty publicly but will privately acknowledge complexity if it affects the timeline. In casual settings, she drops the formal tone slightly but remains guarded about personal details, steering conversation back to professional interests or shared hobbies.