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AI Portfolio - Elsa Bakiu

Portfolio of AI product work focused on turning ambiguous opportunities into user-facing outcomes, with clear scope, delivery tradeoffs, and measurable value.

Who This Is For

  • Hiring managers evaluating AI PM / AI consultant profiles
  • Product and engineering leaders assessing execution depth from framing to delivery
  • Teams seeking practical examples of AI systems with real tradeoff handling

Portfolio Index

1) Investora-AI (investora-ai)

Investora-AI is an AI-powered investment decision-support product that turns fragmented market data into personalized, prioritized insights so users can make faster, more confident weekly decisions.

  • Problem: investors face fragmented signals, slow weekly review cycles, and low confidence in what to prioritize.
  • Product bet: unify data and AI reasoning into one workflow that turns market noise into personalized, actionable insights.
  • User value: faster analysis, clearer priorities, and better decision confidence through profile-aware recommendations.
  • Delivery focus: phased execution with reliability guardrails (contracts, tests, observability) to balance speed and quality.

2) Personal Branding Copilot (personal-branding-copilot)

AI content product concept for professionals who need consistent thought-leadership output without losing voice, with a focus on brand fit, quality control, and review efficiency.

3) News Summarizer (news-summarizer)

AI summarization product aimed at helping users process high-volume news faster and act on the most relevant developments with less cognitive overload.

4) Podcast Studio (podcast-studio)

AI-assisted content workflow for accelerating podcast production from idea to draft assets, improving speed and consistency for creators and teams.

5) AI Product Playbook (ai-product-playbook)

Practical operating toolkit for AI product discovery, prioritization, and execution, built for cross-functional teams delivering under real constraints.

What I Can Do for Clients and Teams

  • Frame AI opportunities into scoped, outcome-driven product bets
  • Design MVP-to-production roadmaps balancing speed, risk, and quality
  • Translate between business, product, and engineering stakeholders
  • Build delivery plans with measurable quality gates (tests, contracts, observability)
  • Lead AI feature lifecycle: discovery, prototyping, validation, rollout, iteration

Delivery Style

  • Pragmatic: focused on user and business outcomes, not AI novelty alone
  • Structured: explicit decisions, tradeoffs, and risk handling
  • Hands-on: technical depth where needed to unblock execution and quality

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AI Product Management portfolio showcasing end-to-end AI projects, LLM-powered workflows, and production-aware system design with a strong focus on usability and real-world constraints.

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