AI Knowledge Platform
White-label AI knowledge assistant that transforms documents, videos, and web content into a citation-backed conversational knowledge base with zero hallucination
Every answer grounded in source material. Every source cited. Zero hallucination.
How we built a white-label AI knowledge assistant that turns any collection of documents into an intelligent, private knowledge base.
The LexaBot dashboard: real-time KPIs, active jobs, and production status at a glance
The LexaBot dashboard: real-time KPIs, active jobs, and production status at a glance
The Problem
Knowledge was scattered across hundreds of PDFs, hours of lecture videos, web pages, and internal documents. Finding a specific answer meant scrubbing through hours of video or searching hundreds of pages. Generic AI chatbots hallucinated freely and pulled from the open internet — unusable for education or business where accuracy is non-negotiable.
Critical knowledge buried across PDFs, videos, web pages, and internal docs with no unified search
Generic AI chatbots hallucinated and pulled from the open internet — useless where accuracy matters
No way to control who could access which content within the same organization
Zero audit trail for how people interacted with the knowledge base
What We Built
A white-label RAG-powered knowledge platform that ingests PDFs, videos, YouTube, web pages, and text — then delivers citation-backed answers grounded exclusively in the uploaded source material. Multi-tier security controls who sees what, and a full admin dashboard provides complete audit visibility.
RAG-Powered Chat
Students and employees ask questions in natural language and get streaming, citation-backed answers drawn exclusively from uploaded materials. Every response includes source names, page ranges, and video segments so users can verify and go deeper.
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The Results
0
Hallucination rate — grounded in source material only
5
Content formats ingested automatically
24/7
Always-on knowledge assistant
The Transformation
Before
- Finding answers meant scrubbing through hours of video or searching hundreds of PDF pages
- Generic AI chatbots hallucinated and referenced information from the open internet
- No way to control which users could access which content within the same deployment
- Zero visibility into how students or employees interacted with knowledge materials
After
- Natural language questions return citation-backed answers with specific page and video references
- RAG pipeline ensures every response is grounded exclusively in uploaded source material
- Admin-controlled content visibility with role-based access and Row-Level Security
- Full conversation audit trail with per-user chat logs, search, and analytics
LexaBot replaced the guesswork. Students and employees get accurate, cited answers in seconds instead of digging through hours of material. Admins see exactly what questions are being asked and which sources are being referenced. The platform runs across education and business deployments with complete tenant isolation — every organization gets their own private knowledge brain.
More from the Platform
Beyond the core modules. Here's what else LexaBot runs on every day.
How We Got Here
Week 1 to 2
Discovery
Mapped the knowledge retrieval problem across education and business use cases. Defined the RAG architecture and security model.
Week 3 to 6
Build
Built the full-stack platform: ingestion pipelines for 5 content formats, vector search across Pinecone namespaces, streaming chat with Claude Haiku, and the admin dashboard.
Week 7 to 8
Launch
Deployed first education instance. Uploaded full semester curriculum and onboarded students. Validated citation accuracy against source materials.
Ongoing
Optimize
New content formats added as use cases expand. Prompt injection detection updated against emerging attack patterns.
Week 1 to 2
Discovery
Mapped the knowledge retrieval problem across education and business use cases. Defined the RAG architecture and security model.
Week 3 to 6
Build
Built the full-stack platform: ingestion pipelines for 5 content formats, vector search across Pinecone namespaces, streaming chat with Claude Haiku, and the admin dashboard.
Week 7 to 8
Launch
Deployed first education instance. Uploaded full semester curriculum and onboarded students. Validated citation accuracy against source materials.
Ongoing
Optimize
New content formats added as use cases expand. Prompt injection detection updated against emerging attack patterns.
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