AI IntelligenceInstant Knowledge Access
RAG-Based Knowledge Base
Company documents, SOPs, aur manuals se AI-powered search — employees ko instant accurate jawab milein.
شروع از
PKR 95,000
RAG-Based Knowledge Base کیا ہے؟
A RAG-based knowledge base ingests your documents, chunks and embeds them for semantic search, and answers questions with citations pointing to the exact source passages. Hybrid retrieval combines keyword and vector search so acronyms and SKU codes still match. Access controls mirror your folder permissions so confidential HR or finance docs never surface to unauthorized users. Re-indexing runs on a schedule or webhook when source files change.
جب یہ سروس مناسب نہیں
- Knowledge that changes hourly without any stable source document.
- Organizations unwilling to classify documents by sensitivity level.
- Use cases requiring real-time data from transactional databases without a sync layer.
- Tiny FAQ sets under twenty pages where keyword search already suffices.
موزوں استعمال کے cases
- Internal employee handbook and HR policy Q&A with role-based visibility.
- Customer support grounded strictly in approved public help articles.
- Field service teams querying equipment manuals and troubleshooting trees on mobile.
- Legal or compliance teams searching contract clauses across archived PDFs.
- Product managers asking natural-language questions over research notes and specs.
یہ سروس کن مسائل حل کرتی ہے
- Staff waste time searching across Confluence, Drive, and email for the same policy answers.
- New hires cannot find updated SOPs because filenames and folder structures are inconsistent.
- Generic LLM chat gives plausible but wrong answers about internal procedures.
- Subject matter experts interrupt their work to answer repeat Slack questions.
- Compliance audits struggle to prove which document version was authoritative on a given date.
دریافت اور عمل درآمد کے مراحل
1. Corpus audit & access model
We classify documents by sensitivity, identify duplicates and outdated versions, and define which collections each user group retrieves from.
2. Ingestion & chunking pipeline
Connectors pull text from PDFs, slides, and wikis. Tables and headings inform chunk boundaries to keep answers coherent.
3. Retrieval evaluation
Held-out questions measure recall and citation accuracy. Hybrid weights tuned until acronym and paraphrase queries both succeed.
4. Interface deployment
Slack bot, web widget, or internal portal goes live with logging. Users see cited snippets before expanded answers.
5. Re-index & governance handover
Scheduled re-index verified after source updates. Admins trained on purge workflows when documents retire.
کیا شامل ہے
✓Ingestion connectors for agreed source systems and file formats
✓Chunking strategy documentation with overlap and metadata rules
✓Vector index with hybrid keyword + semantic retrieval
✓Answer UI or API returning citations with page or section anchors
✓Access control mapping from source permissions to retrieval filters
✓Re-index job with change detection and failure alerting
✓Evaluation report against held-out question set from your team
✓Administrator guide for adding collections and purging retired docs
سیکیورٹی اور پرائیویسی
- Embeddings inherit document ACLs enforced at query time
- Query logs optionally anonymized or disabled for sensitive collections
- Source credentials rotated through secrets manager
- No cross-tenant index sharing in multi-team deployments
- Right-to-erasure workflow removes chunks when source files delete
قبولیت کے معیار
- Evaluation set achieves agreed citation accuracy on approved questions
- Unauthorized role cannot retrieve chunks from restricted collection in penetration test
- Re-index completes within defined window after sample document update
- Answers include at least one source link matching human-verified ground truth
- Hybrid search retrieves acronym-specific doc when vector-only search misses
سروس فیصلہ گائیڈ
| فیصلہ عنصر | یہ طریقہ | متبادل | نوٹس |
|---|---|---|---|
| Grounding & citations | Mandatory retrieval step with snippet citations before answer synthesis | ChatGPT Enterprise upload with manual file refresh | Manual uploads drift; automated ingestion keeps answers tied to live sources. |
| Hybrid retrieval | Keyword + vector fusion tuned on your acronym and SKU queries | Embedding-only search index | Pure vectors miss exact-match identifiers common in ops docs. |
| Permission enforcement | Query-time filters synced from source ACLs or SSO groups | Single shared index for all staff | Shared indexes leak salary bands and unreleased product specs. |
| Re-indexing operations | Change detection jobs with failure alerts and partial re-ingest | Full manual re-upload quarterly | Quarterly manual cycles leave weeks of stale answers in fast-moving teams. |
انضمام کی dependencies
- Read access to source repositories with stable API or export paths
- Embedding model API or self-hosted embedding endpoint decision finalized
- Vector database hosting choice aligned with your infra (cloud or VPC)
- Identity provider groups if retrieval must match SSO roles
لانچ کے بعد سپورٹ
- Quarterly retrieval quality reviews with new question samples
- Connector maintenance when source APIs change
- Chunk strategy adjustments when new doc templates introduced
- Optional managed re-index and deprecated doc purge service
RAG-Based Knowledge Base اکثر پوچھے جانے والے سوالات
ہماری ai intelligence سروس کے بارے میں عام سوالات۔
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