LLM Endpoint
Azure OpenAI, Anthropic Claude, AWS Bedrock, Databricks Mosaic AI, Snowflake Cortex, or self-hosted open-source models
Enterprise AI RAG Chatbots, Grounded in Your Own Knowledge
Beyond Key designs, builds, and operates production-grade RAG-based chatbots that retrieve answers from your documents, databases, and live systems — and cite every source.
No hallucinations. No retraining cycles. Just trusted, on-demand answers for customer service, internal support, and regulated workflows.
85–95%
answer accuracy on enterprise RAG deployments
6–12 wks
From discovery to production rollout
30–60%
Tier-1 ticket deflection on customer service bots
Built for enterprises that can’t afford wrong answers
A standard LLM chatbot answers from frozen training data and guesses when it doesn’t know. A Beyond Key RAG based chatbot retrieves the right passage from your trusted sources, generates a grounded reply, and shows its work. That difference is the line between an AI demo and an AI system you can put in front of customers, employees, and auditors.
Pipelines that ingest from SharePoint, Confluence, Salesforce, ServiceNow, SQL, Snowflake, Databricks, and custom APIs — kept fresh on schedule, with no model retraining required.
Each response links back to the document, page, or record it was generated from. Auditable by design and trusted by support, legal, and compliance teams.
Vector + keyword (BM25) search with a reranker on top — so acronyms, SKUs, and policy IDs surface alongside semantically similar content. Production-grade accuracy, not toy demos.
Retrieval is filtered by user identity, group membership, and document classification. The chatbot never returns content a user is not entitled to see.
Every answer ship with a confidence score. Below threshold, the bot escalates to a human, hands off to a ticket, or says “I don’t know” — which is far better than a confident hallucination.
PII redaction, prompt-injection defenses, full audit logging, and EU AI Act / GDPR / HIPAA / SOC 2 alignment baked in by Beyond Key’s AI Governance Consulting team.
Retrieval is filtered by user identity, group membership, and document classification. The chatbot never returns content a user is not entitled to see.
Stop LLM hallucinations. Get source-linked responses from your internal wikis, SharePoint, and CRM.
Request a Consultation CallWhere our RAG chatbots earn their keep
We deploy RAG-based chatbots wherever the cost of a wrong or outdated answer is high — and where the right answer already exists somewhere in your organization.
Customer Service
RAG customer service chatbots that resolve product, billing, and policy questions from your live help center — 24/7, in multiple languages, with citations.
IT Helpdesk
Tier-1 deflection bots grounded in runbooks, KB articles, and past resolutions. Cuts ticket volume and frees engineers for real incidents.
HR & Policy
Internal assistants that answer “How many sick days do I get in Singapore?” from country-specific handbooks, with the source PDF one click away.
Sales Enablement
Reps get the right pricing sheet, case study, or competitive battlecard the moment they need it — no more digging through SharePoint mid-call.
Insurance & Claims
Underwriting and claims assistants grounded in policy wordings, regulator guidance, and historical decisions — proven across our insurance practice.
Research & Legal
Document assistants that summarize, compare, and cite passages from contracts, filings, and research libraries with full source attribution.
Slash Support Tickets by 60% with RAG Customer Service.
Schedule a DemoRAG chatbot vs. standard LLM chatbot
If your bot needs to speak about anything proprietary, regulated, or recently changed, RAG is not optional — it’s the architecture.
| RAG-based chatbot | Standard LLM chatbot |
|---|---|
|
✅
Retrieves from your trusted sources before answering
|
⚠️️
Answers only from frozen training data
|
|
✅
Cites the document, page, or record behind every answer
|
⚠️️
No source attribution available
|
|
✅
Stays current via re-indexing — no model retraining
|
⚠️️
Knowledge frozen at the model’s training cutoff
|
|
✅
Role-based filtering — users only see what they’re entitled to
|
⚠️️
No native concept of document-level permissions
|
|
✅
Confidence scoring + graceful fallback to humans
|
⚠️️
Tends to hallucinate when uncertain
|
The Beyond Key RAG architecture
We engineer every layer of the RAG pipeline against your data residency, latency, and compliance constraints — and we’re vendor-fluent across the major enterprise stacks.
Vector Store
Azure AI Search, Pinecone, Weaviate, FAISS, Databricks Vector Search, pgvector
Embedding Model
OpenAI, Cohere, Azure-hosted, or open-source embedding models — selected for accuracy, cost, and data residency
Orchestration
LangChain, LlamaIndex, Microsoft Semantic Kernel, or custom Python/Node frameworks
Ingestion
SharePoint, Confluence, Salesforce, ServiceNow, SQL, Snowflake, Databricks, Power Automate, Azure Data Factory, custom REST/GraphQL
Surface
Microsoft Teams, Copilot Studio, web widget, mobile, helpdesk in-line, voice channels
Governance
PII redaction, prompt-injection defense, audit logs, evaluation harnesses, EU AI Act / GDPR / HIPAA / SOC 2 controls
Deploy a RAG AI Chatbot That Cites Every Answer
Build Your First RAG ChatbotHow we deliver — a 5-phase RAG implementation.
A proven path from “we think this could work” to a production RAG chatbot operating under SLA. Most enterprise scopes go live in 6–12 weeks.
01
Discovery & readiness
Use-case mapping, knowledge-source inventory, and a Gen AI Readiness Assessment across data, governance, and infrastructure.
02
Architecture & design
We select the right LLM, embedding model, vector store, and orchestration stack against your residency and budget constraints.
03
Pilot build
A working RAG chatbot on a focused document set so stakeholders can test answer quality, latency, and citations in a real environment.
04
Hardening & integration
Security controls, role-based retrieval, observability, evaluation harnesses, and front-end integrations into Teams, web, helpdesk, or mobile apps.
05
Scale & managed ops
Expand to additional corpora, tune chunking and reranking, and operate the platform under SLA with monthly evaluation reports and continuous improvement.
The Tangible Benefits of Agentic AI Solutions
Beyond Key AI agents consulting partnership will provide immediate and long-term value mainly through:
Production track record
Live RAG, LLM, NLP, and AI agent deployments across insurance, manufacturing, healthcare, and professional services — including voice transcription and sentiment analytics for insurers and GenAI inventory solutions for electronics manufacturing.
Microsoft Business Apps fluency
Certified engineers across Azure OpenAI, Copilot Studio, Mosaic AI, and Cortex — so we pick the right stack for your data, not the one we're locked into.
Governance from day one
Bias detection, data protection, EU AI Act readiness, and responsible-AI practices delivered through our AI Governance Consulting offering — not bolted on at the end.
Engineered for evaluation
Every project ships with a golden question set, automated evaluation, and a measurable accuracy baseline — so quality is tracked and improved, not assumed.
Managed operations available
Run-and-improve services keep your RAG chatbot implementation accurate as your knowledge base grows — with monthly tuning, drift monitoring, and content health reviews.
Deploy a RAG AI Chatbot That Cites Every Answer
Build Your First RAG ChatbotFrequently Asked Questions
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How long does a RAG chatbot take to deploy?
A focused pilot is typically live in 4–6 weeks. A fully integrated, secured, multi-source enterprise RAG chatbot lands in 8–12 weeks, depending on data quality, source-system count, and governance review.
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How accurate are RAG chatbot answers in practice?
With proper chunking, hybrid search, and reranking, well-engineered RAG chatbots routinely hit 85–95% answer accuracy on enterprise knowledge bases — measurably ahead of LLM-only bots, which hallucinate on proprietary content. Beyond Key includes a formal evaluation harness in every engagement so accuracy is tracked over time.
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Will we need to fine-tune the LLM?
Almost never. RAG keeps domain knowledge in the retrieval layer, not in model weights — you update knowledge by re-indexing, which is faster, cheaper, and safer than fine-tuning. Fine-tuning is reserved for narrow tone, formatting, or reasoning patterns where retrieval alone isn’t enough.
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Can a RAG chatbot replace human agents?
Best deployed as a force multiplier, not a replacement. Most clients see 30–60% deflection on routine queries while complex, emotional, or high-value cases route to humans — which frees agents for higher-impact work and improves CSAT.
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What does it cost to run?
Operating cost is driven by three line items: LLM API calls (per token), vector database hosting, and embedding generation for new content. A mid-size deployment of 10,000–50,000 monthly queries against a few thousand documents typically lands between a few hundred and a few thousand dollars per month, depending on LLM tier and vector store choice.
