Cross-industry
Enterprise AI Knowledge Assistant
Answers grounded in your documents
Illustrative solution blueprint — a representative build demonstrating our engineering approach, not a client engagement.
Overview
This blueprint shows how we would build a retrieval-augmented (RAG) assistant that answers employee questions from an organization's own documents — policies, runbooks, contracts, product docs — with citations back to the source. It applies across industries because the underlying problem is the same everywhere: the knowledge exists, but nobody can find it.
The design puts most of the engineering where RAG succeeds or fails: the ingestion pipeline and retrieval quality. Chunking, metadata, permission filtering, and evaluation get more attention than the chat UI, because a fluent answer without the right source behind it is worse than no answer.
The problem space
The Challenge
Organizations accumulate answers faster than anyone can find them: the same questions land in chat channels and inboxes daily while the correct document sits three folders deep in a system nobody searches. Keyword search fails because people don't use the document's vocabulary, and a naive LLM chatbot fails harder — it answers confidently without grounding, and it ignores document permissions. The real engineering problem is retrieval you can trust, with access control enforced at query time.
How we'd tackle it
Our Approach
We would build the pipeline first: connectors pull documents from source systems, a processing stage chunks them with structure-aware splitting and attaches permission and freshness metadata, and embeddings land in a vector store alongside a keyword index for hybrid retrieval. At query time, the user's identity filters the candidate set before ranking, so nobody retrieves content they cannot open at the source. The LLM answers only from retrieved passages and must cite them; an evaluation harness with a curated question set gates every change to prompts, chunking, or models.
Under the hood
Architecture
- 1
Document ingestion pipeline with connectors, structure-aware chunking, and metadata extraction
- 2
Hybrid retrieval combining vector similarity and keyword search, merged and re-ranked
- 3
Permission-aware filtering applied at query time from source-system access lists
- 4
LLM answer layer (Claude or GPT-class models) constrained to retrieved context with citations
- 5
Next.js chat interface with streaming responses and inline source previews
- 6
PostgreSQL for conversations, feedback, and pipeline state; a vector database for embeddings
- 7
Evaluation harness scoring retrieval and answer quality against a curated question set
Capability surface
Key Features
The functional scope this blueprint covers end to end.
Conversational Q&A with citations linking to source passages
Hybrid semantic and keyword search across connected repositories
Source connectors for wikis, document drives, and ticketing systems
Per-user permission enforcement inherited from source systems
Freshness handling: re-indexing on document change, stale-source flags
Feedback capture on answers, feeding the evaluation set
Admin console for corpus coverage, ingestion health, and unanswered questions
Streaming responses with honest not-in-your-documents behavior
Engineering goals
What This Blueprint Optimizes For
Design goals we engineer toward — stated as targets, not claimed results.
Every answer cites retrievable sources — no uncited generation
Retrieval respects source permissions, with no leakage across access boundaries
First streamed tokens reach the user within seconds of asking
Measured quality: changes ship only when evaluation scores hold or improve
Model-agnostic LLM layer, swappable as providers evolve
Roadmap thinking
Where We'd Take It Next
Agentic workflows for multi-step tasks such as drafting from policy
Slack and Teams integrations for answers inside daily workflows
Knowledge-gap reports generated from clusters of unanswered questions
Domain-tuned embeddings where corpus vocabulary is highly specialized
Tech Stack
The technologies this blueprint is designed around.
- Next.js
- TypeScript
- Python
- Anthropic Claude
- OpenAI APIs
- LangChain
- RAG
- Vector Databases
- PostgreSQL
Related Services
Curious What This Would Look Like for You?
We'll map your requirements against a blueprint like this — architecture, stack, and roadmap — before you commit to anything.