Pain № 01
Information asymmetry.
Coaches struggled with roles where they lacked domain expertise. They couldn't provide the context clients needed for unfamiliar industries or occupations.
Enterprise · GovTech · 2024
CC4 Rework.
A year-long rework of Workforce Singapore's career coaching platform — co-created with coaches, powered by AI, and validated through quarterly usability cycles.
The Brief
01A platform for the coaches.
CC4.0, a collaboration between WSG and GovTech, serves as a platform for career coaches — offering tailored career matching services for job seekers at different career stages. It needed a major rework to meet increasing needs.
“We launched a major rework that included 2 new core features — the Role Informer Tool and the 360 Report — built through co-creation with coaches.
70%
Case completion improvement
3.3→4.4
Ease of case creation rating
03
Key phases of rework
The Problem
02Coaches were drowning in admin.
Career coaches struggled with tedious workflows, information asymmetry, and tools that didn't reflect how they actually worked. The platform needed to evolve from a data-entry system into a genuine coaching assistant.
How might we rework the CC4 platform so that career coaches can spend less time on administrative tasks and more time helping job seekers find meaningful employment?
Pain № 02
Tedious data entry.
Manual data entry consumed time that should have been spent coaching. Multiple systems required duplicate entries with no data extraction between them.
Pain № 03
No task management.
Coaches managed multiple clients simultaneously with no overview of their workload. Ensuring follow-through on the case management process was a constant challenge.
Pain № 04
No actionable insights.
The platform tracked cases but didn't help coaches. There were no data-driven recommendations, no predictive models, no way to diagnose a client's employability.
The Process
03Three phases of a rework.
We started with the coaches — not with screens. Co-creation workshops, atomic research, and a consolidated IA gave us the blueprint for a 12-month execution roadmap.
Step 01 →
Co-create.
Empathy & collaboration
Co-design workshops with career coaches. Printed wireframes, drew, made notes, voted on ideas — directly involving users in the design of their own tools.
WorkshopsCo-designEmpathy
Step 02 →
Synthesise.
Clarity & conviction
Atomic research framework stores learnings. A research dashboard makes insights accessible — vital when improvements take 12–16 months to implement.
ResearchRepositoryInsights
Step 03 →
Concept.
Ambition & focus
Core themes extracted from coach "artwork" inform a consolidated IA. A north-star redesign presented to WSG group director secures buy-in for 2 more years.
IABlueprintBuy-in
Step 04 ◉
Execute.
Momentum & impact
Three pillars — solve information asymmetry, streamline workflow, deliver actionable insights — guide a 12-month execution roadmap from concept to production.
PillarsExecutionProduction
From concept → execution
Three pillars, one vision.
01
Intelligence · AI + Data
Solve the information asymmetry.
Combine SERP API and LLM capabilities to help coaches research unfamiliar roles in seconds — providing up-to-date, real-time job insights without the risks of raw LLM output.
02
Workflow · Integration
Streamline the workflow.
Integrate external systems (FormSG, ABS) for client self-service. Extract data from existing sources to eliminate duplicate entry. Reduce cognitive load at every touchpoint.
03
Insights · Analytics
Deliver actionable insights.
Build the Employability Index — a predictive model using 60+ variables to determine likelihood of employment within 90 days. Turn data into coaching recommendations.
The Solution
04Two features, three pillars.
The Role Informer Tool and 360 Report became the centrepieces of the rework — each mapping back to a pillar, each validated through quarterly usability testing.
25% Completed
Recommended Actions
Feature 01 · Role Informer Tool
Research any role in seconds.
Coaches were often struggling with roles they lacked domain expertise in. The Role Informer Tool combines SERP API with LLM capabilities to provide up-to-date, contextual job research — reducing manual research time to near-zero.
- ALLM + SERP API chain. Real-time job searches using Google's search API combined with language model analysis for structured insights.
- BRole context. Salary ranges, required skills, industry trends, and adjacent opportunities — all surfaced in one view.
- CWhite-box explanations. Transparent, auditable recommendations that coaches can trust and share with clients.
Impact Generated leads for onboarding from NTU and SGEnable career coaches. Demonstrated that AI augments coaching rather than replacing it.
Feature 02 · 360 Report + Task Management
See the whole picture.
The 360 Report consolidates all client data into a comprehensive view, while new task management features ensure coaches never lose track of follow-ups. The Employability Index uses 60+ variables to predict employment outcomes.
- AUpcoming Tasks. A workload overview the moment coaches log in — with direct entry points to complete each task.
- BEmployability Index. A predictive model determining likelihood of employment within 90 days, dissecting contributing factors.
- CBanner system. A/B tested, refined through unmoderated usability testing on Maze — aiding task clarity without cognitive load.
Impact One organisation went from 30% case completion to 70%. Ease of case creation rated 4.4/5, up from 3.3.
Feature 03 · Industry & Occupation Insights
Data-driven coaching.
By combining LLM capabilities with data analytics from data.gov and MOM statistics, we created a tool that provides white-box explanations for every recommendation — turning opaque AI outputs into transparent coaching tools.
- AEmployment trends. Data from data.gov and mom.stat surfaces industry-level insights coaches couldn't access before.
- BLLM + analytics fusion. Data handles cognitive tasks and analytics; LLMs generate natural-language recommendations from those signals.
- CTransparent recommendations. Every suggestion is traceable to data — coaches can explain the "why" behind every recommendation.
Insight The data used for the Employability Index, while useful, is skewed — limited to WSG and E2i clients. Public data sources add crucial industry-level context.
