| Head of Product Design
Productising expert-led onboarding into a scalable self-serve system
Cut onboarding time in half by building hiring expertise into the product.
FOCUS: Product strategy | AI integration | Workflow redesign | Cross-functional alignment
Product context
Expert-led onboarding was slowing client's time to value. Based on onboarding audits, sales call reviews, and stakeholder interviews, three structural tensions emerged:
1. Business reality
Intertru’s onboarding process relied heavily on internal hiring experts to manually configure organisational values and behavioural criteria for each new client. This ensured quality, but created a dependency on experts.
New client setup typically took 2 to 4 hours and required guided consultation before customers could experience value through their first candidate interview.
2. Friction signals
During sales calls, prospects frequently asked:
“How long does it take to get set up?”
This question exposed a deeper concern on client's time to value.
Clients also struggled to translate abstract organisational values into the structured behavioural inputs required by the system. What was intuitive to internal experts was unfamiliar to customers.
3. System constraint
Compounding this friction, the platform required value weightings to total 100%, forcing users into precision-based configuration early in the process. This increased cognitive overhead and further delayed client's activation.
At the same time, internal debates about whether values shape culture or culture shapes values made it harder to align on product direction.
Key insight
The bottleneck wasn’t how complex the setup was, but the need for expert involvement.
Intertru’s differentiation came from embedded hiring expertise (HireOS) but that expertise lived in people rather than the product. If that knowledge could be structured and embedded into the product, onboarding could shift from guided consultation to guided self-serve without lowering assessment quality.
Expert-led onboarding was slowing client's time to value.
Product decisions
1. Shift from manual configuration to guided intelligence
Clients were asked to translate abstract organisational values into structured behavioural criteria. This was something internal experts could do intuitively, but customers could not.
Instead of simplifying form fields, I restructured the workflow.
The system now generates structured behaviour indicators from natural language input, shifting users from creation to review.
Click through the workflow from values to behaviours below:
Why it matters:
Simplified the workflow and built in expert logic, cutting onboarding time without adding operational overhead.
2. Remove percentage constraint without rewriting the backend
The platform required all organisational value weightings to total 100%, forcing users into precision-based arithmetic early in onboarding. Adjusting one value required recalculating others, increasing cognitive overhead before users fully understood the system.
Rather than rewriting backend logic, I introduced a 10-point relative scale that allowed values to be configured independently. The system automatically normalized scores to 100%.
This preserved system integrity while while removing unnecessary complexity.
Why it matters:
Improved usability within engineering constraints and sped up configuration without breaking the scoring logic.
3. Use design artifacts to align the team
There were internal debates about whether values shape culture or culture shapes values, which made it unclear how hiring logic should be reflected in the product.
Instead of continuing abstract discussions, I introduced simple mapping exercises and system diagrams to connect organisational values to behaviours and scoring rules.
This gave product, engineering, and leadership a shared understanding before we started building.
Why it matters:
Design became a convergence mechanism, aligning stakeholders and preventing downstream rework.
Product impact
Operational shift
Onboarding time was reduced by 50%, enabling clients to move from setup to first candidate interview significantly faster.
Activation shifted from expert-led consultation to guided self-serve, reducing reliance on internal hiring specialists and eliminating manual translation of organisational values into structured behaviours.
Backend scoring logic remained intact, avoiding engineering rework while improving front-end simplicity.
Business leverage
Embedding hiring expertise directly into the product strengthened Intertru’s positioning as an intelligent hiring system rather than a services-supported platform.
The shift improved sales clarity by making the platform’s differentiation visible during conversations, supporting stronger articulation of value and competitive advantage.
The solution enabled scalable onboarding without increasing headcount, aligning product experience with long-term growth strategy.
Strategic takeaways
This project reinforced that friction in complex products is often structural, not visual.
At first glance, onboarding looked like a usability issue. In reality, it depended heavily on internal hiring experts. When key decisions live in people instead of in the product, scaling requires more headcount.
The real shift was moving hiring expertise into the workflow itself. By turning expert judgment into structured system logic, we reduced onboarding friction while preserving scoring integrity and existing backend constraints.
The result wasn’t just a cleaner interface, it was a move from expert-supported onboarding to guided self-serve.
