Generative AI
Representative SolutionAI Knowledge Assistant
Built a secure organizational assistant capable of searching policies, documentation and internal knowledge.
Seconds
To find internal knowledge
This is a representative solution describing the type of system LT Lab delivers. It does not describe a specific named client engagement.
The challenge
What wasn't working
Staff in a multi-department organization relied on colleagues and long document searches to find policies, procedures and prior decisions. Knowledge was spread across shared drives, intranet pages and email.
Public AI tools were not an option because the content was confidential and access varied by role.
Approach
How the solution was built
Step 01
Curate the knowledge sources
Identified authoritative documents, retired outdated versions and agreed ownership for keeping content current.
Step 02
Build a permission-aware index
Documents were parsed, chunked and embedded with metadata that mirrors existing access rights.
Step 03
Design the assistant
A conversational interface that answers in plain language, cites its sources and declines when it cannot find support.
Step 04
Evaluate and monitor
A test set of real staff questions, answer-quality reviews and usage analytics to guide ongoing improvement.
Architecture
System overview
- Document stores
- Ingestion pipeline
- Vector index + ACLs
- Retrieval and LLM
- Assistant UI
- Evaluation
Outcomes
What changed
- Answers to routine policy questions in seconds, with source links
- Reduced interruptions for subject-matter experts
- Confidential content kept within the organization's controls
- Clear visibility of knowledge gaps from unanswered questions
Technologies
Python / FastAPI / PostgreSQL + pgvector / LLM APIs / Next.js / Microsoft 365
Services involved