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Hi, I'm Mirwaise Khan, an AI engineer building practical tools with large language models.
This is a project I built — an Enterprise AI Document Assistant. You upload any PDF, and it indexes the content using ChromaDB and vector embeddings.
Then you can ask it questions in plain English, and it retrieves the exact relevant section and gives you a grounded answer — with the source cited, so you always know where the information came from.
I build systems like this — RAG pipelines, AI chatbots, and document automation tools — using Python, LLMs, and vector databases.
If you need something like this for your business, let's talk.
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Designed an AI research workspace where the agent plans the work and fills a sheet with cited sources as it goes.
Same screen, two modes. Which one would you work in all day? 👀
13 voted
72%
5 voted
28%
18 votes
Closed
Keeping the research plan and cited results side by side is a useful way to make the agent’s work easier to inspect. I’d lean toward light mode for reading the table in daylight. How do you distinguish a verified source from one that still needs review?
Looks clean. Is this live yet?
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When a CRM-to-database sync fails halfway, how do you handle it: idempotent retries, or a reconciliation pass afterward? That's usually where integrations start to drift.