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Discovery Track

Understanding the problem space, identifying opportunities, and defining the product’s vision, considering various perspectives such as employees, users, customers, and metrics.

Identifying the Problem

  • Understand pain points from all perspectives - employees (internal processes), users (actual product usage), and customers (needs, desires).

  • Conduct user interviews with employees and customers to gather feedback.

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Tools: SurveyMonkey (gather feedback from customers and users), UserInterviews (user research and testing), Hotjar (analyzing user behavior on websites and apps)​

Conducting Market Research

  • Analyze key market metrics, trends, and customer behavior.

  • Perform competitor analysis and review industry reports.

  • Look at employee efficiency metrics and how they can be improved via the product.

 

Tools: Crunchbase (competitor analysis), CB Insights (market trend and competitor intelligence), Gartner (industry reports and market insights)

Validating Ideas

  • Collect feedback through testing, focusing on user experience (UX) and internal stakeholder feedback

  • Use metrics to validate the feasibility of ideas.

 

Tools: Lookback.io (user testing and feedback), InVision (prototyping and gathering feedback), Miro (brainstorming with employees and teams)

Defining Product Vision

  • Align the product vision with business goals, ensuring it addresses user needs, employee pain points, and customer desires.

  • Use metrics like NPS (Net Promoter Score), engagement data, and employee feedback to inform the product vision.

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Tools: Miro (collaborative vision mapping), Aha! (product vision and strategic planning), Trello (aligning team members around product goals)

Delivery Track

Turning the product concept into a working solution and launching it to the market.

Defining Roadmap

  • Create a prioritized product roadmap based on business goals, customer feedback, and development capacity.

  • Ensure alignment with company objectives and key milestones.

 

Tools: Jira (managing product roadmaps and sprints), Aha! (product roadmap planning and feature prioritization), Trello (task management and roadmap visualization)

Working with Development Teams

  • Collaborate with engineering, UX/UI, and design teams to build the product iteratively.

  • Ensure that the development team has all the necessary requirements to meet the product's vision.

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Tools: Slack (real-time communication and coordination), Confluence (documentation and collaborative knowledge sharing), Figma (design collaboration and prototyping with teams)

Managing Releases

  • Oversee the product release cycle, ensuring timely and high-quality delivery.

  • Coordinate with QA, operations, and deployment teams to ensure smooth releases.

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Tools: GitHub (version control and release management), GitLab (CI/CD pipelines and release management), Jenkins (continuous integration and automated testing)

Ensuring Quality

  • Maintain a focus on product quality, ensuring it meets user expectations and business objectives.

  • Work closely with QA teams to perform thorough testing and identify any issues before launch.

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Tools: TestRail (managing test cases and tracking results), Selenium (automated testing), Bugzilla (bug tracking and issue management)

AI Track

Designing and delivering intelligent systems using LLMs, autonomous agents, predictive models, and data pipelines to drive scalable, high-impact AI product experiences.

Generative AI

  • Build content-generating assistants and chatbots to simplify access to support and policy.

  • Design prompt flows and use retrieval-augmented generation (RAG) for accurate, safe outputs.

  • Integrate LLMs into tools that enhance user experience, reduce support load, and personalize interactions.

 

Tools: OpenAI (ChatGPT, GPT-4), Claude, Gemini, LangChain, Streamlit, Custom GPTs

Agentic AI

  • Build AI agents that operate with autonomy across HR, procurement, licensing, and internal operations.

  • Configure task chaining, memory, and triggers to reduce manual intervention and increase throughput.

  • Deploy agents in real-world public sector environments to enable intelligent, scalable workflow execution.

 

Tools: LangChain (Agents, Tools), Pinecone, Zapier, n8n, Notion API

  • Develop scoring models and forecasting tools for investment, claims, or onboarding risk assessments.

  • Integrate predictive insights into product experiences to increase user confidence and actionability.

  • Continuously optimize model performance through feedback loops and measurable KPIs.

 

Tools: Python, Scikit-learn, Amplitude, Mixpanel, Tableau, Google Analytics

Predictive AI

Data & Intelligence Enablement

  • Build data pipelines, dashboards, and semantic scoring engines to support AI decisioning.

  • Enable A/B testing, funnel analytics, and automated reporting to monitor feature success.

  • Structure data for use in downstream GenAI or ML applications, ensuring traceability and integrity.

 
Tools: SQL, PyMuPDF, PostHog, Notion, Google Analytics

Business Consultation

Want to see how these tools come together to solve real problems?

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