Conversational AI Assistant
Enterprise Service Access via NLP
An NLP-based assistant that helps users discover services, retrieve knowledge and look up documents through natural language — reducing dependence on call centers and manual navigation.
Self-serve
Access
Semantic
Retrieval
Grounded
Answers
Azure
Cloud
Business challenge
People struggle to find the right service or document across sprawling enterprise and public-sector systems, defaulting to call centers and manual navigation that are slow and expensive.
What I built
I designed a search-and-retrieval pipeline where user questions are understood with NLP, matched against a knowledge base and document corpus via semantic search, and answered with grounded results and links — deployed on Azure. Created a roadmap for deeper integrations and analytics.
Why it matters
Every call and manual lookup has a cost. A natural-language assistant that reliably points users to the right service or document reduces call-center load and improves access — especially for resident and enterprise support.
How it's designed
Interface
How the intelligence flows
- 01Understand — parse the user's natural-language request (intent + entities).
- 02Retrieve — semantic search over knowledge base and documents.
- 03Ground — assemble grounded answers with source links.
- 04Respond — service discovery and document lookup in conversation.
How data is modeled
- Vector index over the knowledge base and document corpus.
- Metadata store mapping documents to services and access paths.
- Query + feedback logging for retrieval-quality improvement.
Key endpoints
/api/askAnswer a natural-language question with grounded results./api/services/searchDiscover services by natural-language query./api/documents/{id}Retrieve a document with source context.Technologies
Key features
- Natural-language service discovery.
- Knowledge retrieval with grounded answers.
- Document lookup with source links.
- Resident/user support flows.
- Azure-based, scalable retrieval pipeline.
Challenges & trade-offs
Grounding over guessing
Answers are grounded in retrieved documents with source links, so the assistant points to authoritative content rather than fabricating.
Mapping language to services
Users describe needs in their own words — the retrieval layer bridges everyday language to the correct service or document.
Code snippet
@app.post("/api/ask")
async def ask(q: Query) -> Answer:
intent = nlp.understand(q.text) # intent + entities
hits = await vectors.search(q.text, k=5) # semantic retrieval
if not hits:
return Answer(text="No matching service found.", sources=[])
return Answer(
text=compose(intent, hits), # grounded response
sources=[h.document_url for h in hits], # always cite sources
)How it ships
- Azure-hosted retrieval services with a chat front-end.
- Indexing pipeline for the knowledge base and documents.
Security
- — Access-controlled documents and scoped retrieval.
- — Query logging with privacy-aware handling.
Scalability
- — Vector search scales with corpus size.
- — Stateless API for horizontal scaling.