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AI Equity Research Tool for the Pakistan Stock Exchange
An AI system that automates equity research for PSX listed companies (starting with ENGRO, HUBC, MCB), ingesting annual reports and generating cited, analysis backed answers instead of requiring investors to read hundreds of pages manually.
Instead of a simple chatbot, this is built as an orchestrated AI pipeline: documents are ingested, chunked, embedded, and retrieved based on meaning rather than just keywords, then grounded into answers with source citations, covering things like risk factors, growth drivers, and financial performance.
What it does:
• Ingests financial reports and structures them for retrieval
• Answers natural language questions with citations back to the source document
• Uses an AI orchestration pipeline (LangGraph) rather than a single linear prompt, so it can be extended with retries and multi step reasoning
• Built for evaluation from day one, not just whether it answers, but whether the answer is accurate and grounded
Stack: Python, LangGraph, Gemini API, ChromaDB, FastAPI (in progress)
Currently in active development. The core retrieval pipeline is working end to end, with automated evaluation and a production API layer underway next.
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The Assumption of the Virgin, Bernardo Daddi, 1337–38. Tempera on wood, gold ground. The Met, Robert Lehman Collection (1975.1.58).
This panel is the top half of an altarpiece for a chapel in Prato Cathedral, built to house the city's real relic: the Virgin's girdle. In the scene she lowers it to Saint Thomas below as proof her body rose whole – the panel showing his hands catching it is lost.
We rebuilt it as a tiara. The mandorla becomes the crown's pierced opening. The angels return in glass, still in their original green and pink. A frog takes the summit – the one animal that changes bodies without dying. A poppy blooms where the Virgin sat: not a relic passed down to a man waiting below, but a small, symbolic dawn of the matriarchy still ahead of us. 🌸 💅
Original Met piece: metmuseum.org/art/collection/search/458998
Inst: https://www.instagram.com/reel/Dc8y5NMIivZ/?stkn=NHlrcXg3dTlkemJl
Video experiments: https://app.flora.ai/join-project/04c3dfff-e5db-4819-afd6-54c7a7bf7725
Your AI feature has a price tag. Make it easier to understand.
Meet AI Spend Calculator, my latest Framer component—now live.
Adjust usage, token pricing, and budgets to estimate monthly AI spend, or switch to Agent ROI to explore time savings, net value, and payback. There’s also a custom formula mode for your own calculations.
I put a lot of care into the details: Paper and Obsidian themes, tactile switches, a rotating dotted globe, and layouts for desktop, tablet, and mobile.
The video shows it in action. I’d love to hear where you’d use it!
Check it out :- https://framer.link/yKMh1wU
Your AI agent just did 9 things: pulled 412 orders, matched them to Stripe, caught a duplicate £248 charge, drafted a ledger entry, opened a ticket. Whole run cost $0.011.
Now the real design question — what does the user actually SEE?
A — The transcript. A paragraph of text and a collapsed "Used 3 tools".
B — The run canvas. Nine colour-coded rows: what ran, what it cost, what got flagged, and one step that stops and waits for you before money moves.
Same agent. Same run. Two completely different products.
Vote A or B — and tell me why. I build the B version for a living.
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