Enterprise LLM Chatbot — RAG-Powered AI Assistants
We'd design enterprise LLM assistants that answer from your company documents, product catalog, and FAQ knowledge base — supporting customer service 24/7 across languages.
An enterprise LLM chatbot is no longer the "ask AI, get an answer" pattern. A well-built assistant uses your live corporate corpus as a real-time source — contracts, product catalog, FAQs, internal wiki, CRM records — produces citations, knows which question to escalate to which operator, and runs on infrastructure where cost, latency, and accuracy are all measurable. The gap between "open ChatGPT and write a prompt" and a production-grade enterprise chatbot starts right here. We'd build that infrastructure with the discipline of RAG (Retrieval-Augmented Generation): vector search, re-ranking, prompt versioning, an eval harness, and full observability — not optional add-ons, because without them a chatbot works as a demo and silently degrades in production.
The Business Problems We Solve with Enterprise LLM Chatbots
Customer support teams answer the same 100 repetitive questions hundreds of times per day; ticket queues keep growing, and nights and weekends produce a backlog nobody resolves.
Employees lose 30 to 60 minutes a day searching procedures, contracts, or product docs; Confluence, SharePoint, and Notion have not caught up with what LLM-era search should feel like.
Website visitors leave the product catalog without finding the feature they want; the abandonment when live support is offline translates directly into lost revenue.
Multi-language customer support means hiring local staff in every market; a Turkish SaaS company providing real 24/7 support in 5 languages otherwise needs a 25-to-40-person global team — a huge cost.
FAQ documents go stale on every product update; a wrong answer in the knowledge base reaches the user, and the feedback loop wears the team out.
Our Approach
Every enterprise chatbot engagement starts with the same question: where does the answer come from? If the model guesses from its own parameters, hallucination is unavoidable; the only way to get a correct answer is to ground the model on your document corpus. That is why we make RAG our default architecture: chunk your documents and produce embeddings, load them into a vector database, retrieve the top 5 to 10 chunks for each query, re-rank them with Cohere or BAAI re-rankers, and feed that context to the LLM along with the question. Every answer comes back with citations; the user can see exactly which paragraph of which document was the source.
This is not a lab pattern. A reference architecture we can point to: an agentic system we designed for a sales organisation, in which the same infrastructure layer could be turned into a 24/7 internal assistant running ICP research, scoring prospects, drafting first-touch emails, and surfacing regulatory notes. A RAG-plus-tool-use foundation built right scales across customer-facing, internal, and active-sales use cases on the same base. For us, the real job is not "building a chatbot" — it is making your knowledge layer model-accessible.
Process
Document Indexing
PDFs, Word, Confluence, Notion, SharePoint, CRM records — we map the source inventory and pick a chunking strategy (token-based, semantic, hybrid) that fits each document's structure. The wrong chunking caps everything downstream.
Vector DB Setup
We select an embedding model (OpenAI ada-3, Cohere embed-v4, or open-source BGE) and build the index on Pinecone, Weaviate, or self-hosted pgvector. Metadata filtering (user role, date, language) is part of the design from day one.
Retrieval + Re-ranking
Initial retrieval is wide (top 20), then narrowed to the 3-to-5 most relevant chunks via Cohere Rerank 3 or BAAI bge-reranker. Without re-ranking, RAG accuracy lands 15 to 25 points lower — we have measured this many times.
Prompt Engineering + Eval
We write the system prompt, citation format, and low-confidence behaviour rules; a Ragas or LangSmith eval harness runs over a gold dataset and every change is regression-tested. Prompts are versioned in Git like any other code.
Production + Citation UI
Streaming responses, citation chips, clickable source links, human handoff on low confidence. Step-level tracing in LangSmith, latency and cost-per-query dashboards in Grafana — we never ship without observability.
Our Preferred Technology Stack
We typically reach for the following — adapted per project to your privacy posture and use case.
Sıkça Sorulan Sorular
Let's Talk About Your Enterprise Chatbot Project
Book a 15-to-30-minute discovery call — free, no commitment. We learn your use case and tell you honestly whether RAG is the right tool for it.