@faizanxkites: 👍🏻

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Saturday 03 October 2026 17:37:33 GMT
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shamraizali74
Shamraiz Ali :
price
2026-10-04 09:56:55
1
anas.babu30
♛KîNG888🧿♠ :
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2026-10-04 11:10:00
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bashirao
💠🇦🇪🇵🇰Bashi Rao🇦🇪🇵🇰💠 :
khubsurat
2026-10-04 18:49:15
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hadiinteriorofficial1
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2026-10-04 19:50:21
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fareed_butt56
Abdullah butt :
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2026-10-04 19:35:46
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qasimbutt042
Qasimbutt :
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2026-10-04 18:47:35
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buttabdulahad07
buttabdulahad07 :
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2026-10-04 19:20:27
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theycallmedani91
Daniyal Waseem :
ki rate ey
2026-10-04 14:05:37
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black.magic8993
BLACK ! MAGIC :
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2026-10-04 11:06:43
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arslanbutt.arslan80
Arslanbutt Arslanbutt :
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2026-10-04 09:50:33
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pti.khanproomax
Khan pro :
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2026-10-04 08:04:38
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amirpathan8so4
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2026-10-04 13:36:54
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chaudhry_013
chaudhry_013 :
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2026-10-04 05:46:34
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ahmadmahgear
احمد ماجر ،،🥀 :
[Heartwarming][Heartwarming][Heartwarming]
2026-10-04 08:24:52
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mianali440
ali :
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2026-10-04 19:03:22
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ahsanrajpoots577
Ahsan Rajpoot :
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2026-10-03 17:48:09
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How to build a company second brain that never makes things up (RAG, every step) A company AI that answers from 18,000 documents, shows the exact page, pulls up the right diagram, and says so when it can't prove something. Here's how it's built, step by step. The idea (RAG): the AI looks things up before it answers. Search by meaning works like a map: every chunk of every document is a pin, and similar meanings sit on the same street. Part numbers need exact-word search, so you run both. The loader (a program that gets documents onto the map), built with Claude Code: Read the documents properly. Docling (free) keeps tables whole and cuts out every figure. Claude describes each diagram and reads its part numbers. Dry run on 50 documents first. Label every chunk (metadata). Which document, which version, current or replaced, which model, which plant, who can open it. The AI can only pick from approved lists, and anything that doesn't match goes to a human. Access is enforced in code before the search runs. Chunk by headings. About 500 tokens per chunk, a little overlap, tables and procedures kept whole, the heading path stamped on every chunk, plus one line of context. Fill the index in Pinecone.
How to build a company second brain that never makes things up (RAG, every step) A company AI that answers from 18,000 documents, shows the exact page, pulls up the right diagram, and says so when it can't prove something. Here's how it's built, step by step. The idea (RAG): the AI looks things up before it answers. Search by meaning works like a map: every chunk of every document is a pin, and similar meanings sit on the same street. Part numbers need exact-word search, so you run both. The loader (a program that gets documents onto the map), built with Claude Code: Read the documents properly. Docling (free) keeps tables whole and cuts out every figure. Claude describes each diagram and reads its part numbers. Dry run on 50 documents first. Label every chunk (metadata). Which document, which version, current or replaced, which model, which plant, who can open it. The AI can only pick from approved lists, and anything that doesn't match goes to a human. Access is enforced in code before the search runs. Chunk by headings. About 500 tokens per chunk, a little overlap, tables and procedures kept whole, the heading path stamped on every chunk, plus one line of context. Fill the index in Pinecone. "Search by both meaning and exact words." Keep it current (n8n): every 15 minutes, ask SharePoint what changed. New versions go in first, old versions come out second. Answering (the courtroom): security at the door, a clerk that asks when something's missing, an evidence room (both searches plus a reranker), a first ruling (not enough evidence means "not in approved documents" and a ticket to the owner), a lawyer that cites every sentence, and a judge that checks every claim and number. Prove it before launch: 300 real questions per department, including ones it must refuse. Leaks must be zero. #aiautomation #microsoftcopilot #claudecode #aiemployee #aiagents

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