01 Summary
A six-month engagement translating an early LLM product vision into a tangible and evidence-based direction for a scientific hypothesis-review platform.
Product + AI Strategy
Product, Science, Engineering + Leadership
Design sprint, vision prototyping, MVP definition and scientist validation
Human-in-the-loop scientific workflow and strategic product direction
Voyager was a six-month initiative exploring how an LLM-enabled workflow could support scientists reviewing potential biological hypotheses between Initiation and Hit Identification. The existing process required substantial specialist effort, relied on fragmented tools and captured critical decisions inconsistently. I helped translate the initial AI opportunity into a tangible product direction through workflow modelling, sprint facilitation, vision prototyping, MVP definition and triangulated user research.
Challenge
Thousands of hours of specialist review effort to arrive at Go / No go / Hold decisions were required to meet pipeline goals, with work fragmented across Slack, Hex dashboards, Google forms, Google sheets, Monday boards, 3rd party literature applications (DepMap, Pub Med), Google slides and manual decision records.
Key questions
- How could agent-generated content reduce manual review work?
- How could decisions be captured in-app rather than in spreadsheets?
- How could review context support future agentic workflows?
Approach
Current state workflow mapping, product discovery, design sprint facilitation, prototype creation, MVP definition and validation.
Output
- HITL workflow
- Trust framework
- MVP scope
- Capability themes
02 Context
The Nomination workflow sits between early hypothesis Initiation and progression towards Hit Identification. It requires scientists to review biological and chemical evidence, assess relevance and risk, validate information and decide whether a potential programme should progress (Go / No Go decisions).
Scientific hypothesis review is a high-effort, high-judgement workflow involving evidence gathering, biological assessment, feasibility checks, confidence evaluation and programme-level decision-making.
The previous process relied on a mixture of Hex, Looker, Google Sheets, Monday, PubMed, DepMap, Enhanced Chat and other internal and external sources. Reviewers repeatedly cross-checked outputs and copied information between systems.
Voyager was conceived as an intelligent, LLM-based interactive application to support scientists completing manual hypothesis nomination reviews.
The longer-term vision was to streamline the Nomination of potential Targets to Hit ID at the Initiation process, reduce the time required for each review and capture scientific decisions in a structured form that the system could learn from.
A phased delivery approach was planned, beginning with an MVP focused on the core Biology review and decision-capture workflow. The Chemistry review would follow at a later stage, and the application would lay the foundations for Agentic workflows across the Stage Gates.
🌟 The platform was intended to do more than speed up the manual Human in the Loop [HITL] review process. It needed to establish the decision-capture foundation required for future agentic workflows.
03 Challenge
One of the most significant constraints on achieving the organisation’s 2025 pipeline goals (50 human-approved nominations ) was the number of manual hours Scientists (Biologists and Chemists) were required to spend approving or rejecting (Go / No-Go / Hold) the Nomination of a single Targets moving between the Initiation and Hit ID stage gates.
Scientists were spending a significant amount of time [9.7 hours at the latest review] searching, comparing, copying and validating information across Hex, Looker, Google Forms, Sheets, PubMed, DepMap, Enhanced Chat and other tools.
The challenge was not simply to optimise the existing process. It was to fundamentally improve how the Biology and Chemistry reviews were supported, captured and completed. Aside from saving the business time, and speeding up the review process, other challenges this agentic approach intended to solve were:
Fragmented workflows
Evidence was distributed across multiple applications, creating repeated searching, copying and validation.
Evidence was distributed across multiple applications, creating repeated searching, copying and validation.
Weak decision traceability
Reasons for stopping, starting, pausing or advancing programmes were not captured consistently.
Reasons for stopping, starting, pausing or advancing programmes were not captured consistently.
Inconsistent review outcomes
Prior knowledge and reviewer experience influenced how evidence was interpreted and whether a nomination progressed.
Prior knowledge and reviewer experience influenced how evidence was interpreted and whether a nomination progressed.
Late feasibility risks
Chemistry constraints could surface only after substantial Biology effort had already been invested.
Chemistry constraints could surface only after substantial Biology effort had already been invested.
The challenge was not simply to generate scientific content via an Agent. It was to reduce avoidable manual effort while preserving human judgement, improving decision traceability and creating trustworthy contextual data for future agent-enabled workflows.
04 Role and contribution
I joined after the original opportunity and early vision had been identified. I acted as the AI Product Strategy Lead for the initiative, translating an early AI opportunity into a tangible product direction through workflow discovery, cross-functional facilitation, vision prototyping, MVP definition and user validation.
Discover
Understand the problem and scientific workflow
→ Mapped the as-is Nomination workflow, tools and handoffs
→ Identified pain points, workarounds and decision capture gaps
→ Led discovery interviews with Scientists across Biology and Chemistry
→ Explored Trust, Usability, Traceability and Adoption barriers
→ Gathered evidence from current processes and existing tools
Develop
Explore, prototype and shape the solution
→ Facilitated the cross-functional Design sprint
→ Guided ideation, solution sketching and concept selection
→ Translated the product vision into a tangible Figma prototype
→ Designed the Human in the loop [HITL] review experience
→ Explored generated content, citations, risk assessment and decision capture
Define
Frame the opportunity and strategic direction
→ Synthesised research into prioritised problem and opportunity themes
→ Defined core user and business needs
→ Reframed the opportunity beyond simple content generation
→ Identified immediate value and longer term agentic foundations
→ Established Design sprint goals and key HMW (How Might We) questions
Deliver and validate
Define a bounded MVP and test the direction
→ Co-authored the MVP scope, capabilities and success metrics
→ Co-authored the PRD
→ Separated immediate delivery from longer term agentic ambition
→ Led moderated usability testing and post session survey research
→ Synthesised triangulated findings into strategic capability themes and product recommendations
05 Objectives
The MVP was designed to create measurable near-term value while establishing the foundations for future agent-enabled scientific workflows
01 Reduce review effort
Reduce time spent on manual Biology nomination reviews.
02 Support pipeline goals
Improve review efficiency so throughput between Initiation and Hit could increase without increasing staff headcount
03 Standardise decision capture
Capture Scientific decisions, risk ratings and rationale more consistently within the workflow
04 Test generated content value
Reduce time spent on manual Biology nomination reviews.
05 Establish an Agentic foundation
Create structured contextual data that could support future agent-enabled workflows
06 Product strategy
Strategic principle: create immediate workflow value while capturing the contextual decision data required for more capable agentic workflows later.
06 MVP hypothesis and success metrics
MVP hypothesis
If Voyager could generate a credible first draft of the Biology review and allow scientists to validate, edit and capture their decision within the same workflow, it could reduce manual effort while creating contextual decision data for future agentic capabilities.
If Voyager could generate a credible first draft of the Biology review and allow scientists to validate, edit and capture their decision within the same workflow, it could reduce manual effort while creating contextual decision data for future agentic capabilities.
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