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2
2
1 year of experience
I’m still learning to code, mainly starting with Python, but I can already handle data cleaning, sorting, and organizing (Excel/Google Sheets). I also work with design tools like Figma for UI/UX mockups and can create presentations, pitch decks, and visual reports on tools like Canva, Adobe Express, Powerpoint, Google slides. as far as development is concerned I don’t have direct experience with web development yet, but I’m eager to learn and can support with structuring content(textual & visual), coordinating the team, and making sure our project is well presented and user friendly.

PakAI Capital is an enterprise-grade autonomous AI Investment Intelligence Platform that replaces traditional chartered financial analysts for Pakistan Stock Exchange (PSX) investors. Built with Google Gemini 2.0 Flash, the system deploys a coordinated team of six specialized agents: Research Analyst Agent — Performs fundamental analysis using RAG over company filings Macro Economist Agent — Evaluates SBP policy, PKR, inflation, and global signals Sentiment Analyst Agent — Processes real-time news from Dawn, Business Recorder, and more Risk Manager Agent — Calculates volatility, position sizing, and risk scores Portfolio Manager Agent — Synthesizes all signals into clear BUY/HOLD/SELL recommendations Governance & Compliance Agent — Audits every recommendation for hallucinations, bias, and missing disclosures Key Highlights: Real-time PSX data ingestion + news scraping ChromaDB / Pinecone RAG pipeline for grounded analysis LangGraph orchestration for reliable multi-agent workflow Beautiful Next.js dashboard with live agent reasoning feed Strong focus on explainability and AI governance — directly aligned with DeepMind’s enterprise AI priorities This submission targets Track 2: AI Agents with Google AI Studio and Track 1: Agent Security & AI Governance.
19 May 2026

PakAI Capital is an AI-assisted algorithmic trading platform designed to explore how intelligent systems can analyze market data and execute trades autonomously. The platform collects real-time market information, evaluates trading indicators, and generates trading signals that can be executed programmatically through Kraken CLI connected to the infrastructure of Kraken. The system combines data ingestion, signal analysis, and automated execution into a unified trading workflow. Market data and indicators are processed by the backend engine, which evaluates trading opportunities using rule-based strategies and sentiment signals. Once a valid trading opportunity is detected, the execution layer triggers secure commands through Kraken CLI to simulate or perform trades. To provide transparency and monitoring, the platform includes a real-time dashboard that visualizes trading signals, execution logs, portfolio activity, and performance metrics. This interface allows users to track the behavior of the trading system, monitor trade decisions, and evaluate system performance over time. The goal of PakAI Capital is to demonstrate how AI-assisted trading systems can combine automated analysis, real-time data processing, and programmatic execution to support intelligent financial decision-making. By integrating modern cloud deployment, a modular backend architecture, and Kraken’s trading infrastructure, the project showcases how autonomous financial systems can be built using scalable tools and APIs.
12 Apr 2026