RAG chatbot for
customer support.
A custom retrieval-augmented chatbot trained on your help center, product docs, SOPs, and past resolved tickets. Every answer is bounded to what you've actually published and cites the exact source — so tier-1 tickets get deflected, your agents get context on the ones that make it through, and no customer is ever told about a feature or policy you don't have.
$500 deposit to start. $5,000 setup + $600/mo once scope is confirmed. Live in 3 to 4 weeks. Refundable if your corpus proves too sparse for retrieval and we'll show you exactly why.
Published · By Nolola
CS orgs choosing between
headcount and hallucinations.
Heads of CX, VPs of support, and CS ops leads at Series-A to Series-C SaaS, e-commerce, fintech, and B2B services where tier-1 volume is up and hiring is capped.
- Your rules-based bot dead-ends 60%+ of the time — customers rage-click through to a human anyway
- You tried ChatGPT / a generic LLM widget and it hallucinated product features you don't have
- Tier-1 ticket volume is up 30-80% YoY but headcount hiring is frozen
- Your docs are good but scattered across a help center, Notion, Confluence, and a private wiki
- You want deflection metrics you can defend to finance — not vibes about “time saved”
Not a customer-support use case? See the general RAG chatbot page or browse the micro-pages hub for internal-knowledge-base and other variants.
Not a generic LLM widget.
A working support agent.
Four workflows that turn a knowledge-base bot into a working piece of your CS stack. Everything uncertain routes to a human with full context.
Public help-widget · trained on your docs
Embedded chat widget on your help center, product, or in-app UI. Answers from your corpus (help center, product docs, SOPs, KB articles, past resolved tickets) — not the open web, and not general LLM knowledge. What is RAG in practice: retrieve first, generate second, cite the retrieval — so answers stay bounded to what you've published.
Ticket deflection · answer before the ticket exists
Sits in front of your Zendesk / Intercom / Freshdesk web forms and chat entry points. Tries to answer the question with citations before the ticket is created. If it can answer, no ticket is opened. If it can't (or the user still wants a human), the ticket is created with the transcript + attempted citations prefilled so the agent starts with context, not a cold read.
Multilingual answering
Retrieves from your source-language corpus, answers in the customer's language. English docs → Spanish / French / German / Portuguese / Japanese / Simplified Chinese answers with the same citations. Add or remove languages during setup; every language stays anchored to the same source of truth so you don't drift into contradictory answers across regions.
Human handoff triage
Recognizes uncertainty (low retrieval confidence, missing source, contradictory docs), complexity (billing edge cases, refund escalations), or emotional escalation (frustrated tone, repeated rephrasing). Routes to the right team in your help desk with the full transcript, retrieved sources, and confidence score — so the agent doesn't re-ask what the bot already covered.
Bounded to YOUR corpus
Retrieval scoped to your published help-center, product docs, SOPs, KB articles, and (optionally) redacted resolved tickets. The model can't invent a feature or policy that isn't there — because it's only seeing what you published.
Citation-anchored answers
Every response links to the exact source doc it used. Customers verify claims themselves; your support ops team audits patterns and rewrites the doc pages that keep failing the customer.
Deflection reporting you can defend
Weekly deflection numbers with a source-doc breakdown. Raw event stream exported to Segment / Amplitude / webhook so you can validate against your help-desk metrics — not just a vendor dashboard.
Sits inside your
actual support stack.
Native integrations for the help desks, doc systems, and analytics platforms customer-support teams actually use. If yours exposes an API or webhook, we can wire it — custom integrations get scoped and priced in the setup fee up-front, no surprise line items later.
- Zendesk
- Intercom
- Freshdesk
- Front
- HubSpot Service Hub
- Salesforce Service Cloud
- Help Scout
- Kustomer
- Gorgias (e-commerce)
- Notion / Confluence / GitBook
- Slack (agent handoff + internal Q&A)
- Segment / Amplitude (deflection analytics)
- OpenAI / Anthropic (model layer, your choice)
- Zapier / n8n (custom source ingestion)
RAG chatbot,
rules-based bot, or more hires.
RAG chatbot (this)
- Deliverable
- Citation-anchored answers over YOUR corpus, help-desk integrated, deflection reporting, human handoff on uncertainty
- Right fit
- You want measurable tier-1 deflection with an audit trail on every answer
Rules-based / decision-tree bot
- Deliverable
- Flowcharts you maintain by hand · dead-ends when a user goes off-script
- Right fit
- You have a small, static set of questions and a team to maintain the flows
Hire more tier-1 support agents
- Deliverable
- More humans · limited hours, high attrition, tribal knowledge
- Right fit
- You have consistent daytime volume beyond your current team and can hire
Most 20-50 agent CS orgs recover the $5,000 setup within 2-3 months from deflected tier-1 tickets alone.
Deflected tickets.
Not fabricated policies.
“We're a Series B SaaS. Tier-1 tickets deflected went from ~15% on our old rules-bot to 58% within 90 days on the RAG stack Nolola built. Every answer cites the doc it came from, so our support ops lead uses the ‘bot answered but customer still filed’ report as a signal for which help-center pages need rewriting.”
“Our previous vendor's bot hallucinated a refund policy that doesn't exist and I spent two weeks unwinding the tickets it created. Nolola's citation-anchored setup means every answer either points to a real doc or hands off to a human. Zero hallucinated policy claims in production so far.”
“We ship a lot of product changes and I was scared of docs going stale. The quarterly retrain plus the ‘questions asked but not answered’ dashboard means our docs actually get better every quarter instead of rotting. Median first response is under 3 seconds now.”
Common questions
RAG stands for retrieval-augmented generation. Instead of asking a large language model to answer from its training data (which is where hallucinations come from), the system first retrieves the most relevant chunks from YOUR documents, then asks the model to answer using only that retrieved context. For customer support that means every answer is bounded to what you've actually written — no invented refund policies, no fabricated features, no advice about products that don't exist. Every response can link back to the exact source doc so your support ops team can audit answers and improve the underlying content.
$500 refundable deposit to secure a kickoff slot. Once scope is confirmed at the discovery call: $5,000 one-time setup + $600/month. Setup covers corpus ingestion, embedding + retrieval tuning, prompt engineering, help-desk integration, and 30 days of post-launch tuning. Monthly covers hosting, model inference, quarterly retraining as your docs change, and deflection reporting. Most 20-50 agent CS orgs recover the setup within 2-3 months from deflected tier-1 tickets alone.
A traditional chatbot follows a decision tree (“press 1 for billing”). A large-language-model chatbot answers from training data — flexible but hallucinates. An AI agent has tools and can take actions (open a ticket, issue a refund, update a record). What Nolola builds for support is a RAG-grounded conversational agent: it can retrieve from your docs (RAG), it can take specific bounded actions in your help desk (create ticket, tag with topic, escalate to a specific team), and it hands off to a human on anything uncertain. Not a pure Q&A bot, not a fully autonomous agent — the practical middle that actually works in production support.
Three things. First, retrieval is scoped to YOUR documents — the model can't invent a feature you don't have because it's only seeing what your docs actually say. Second, every answer carries citations, so your support ops team can audit output and improve the underlying content instead of chasing invisible failures. Third, we wire in your help desk (Zendesk / Intercom / Freshdesk / Front / HubSpot Service Hub) so uncertain conversations become tickets with full context, not dead ends. Generic LLM widgets skip all three and produce the hallucinations that got customer-support AI a bad name in 2023-2024.
Your public help center, product docs, changelog, SOPs, KB articles, and (optionally) redacted past resolved tickets that show how a good CS agent phrases answers. Sources connect via direct integrations (Notion, Confluence, GitBook, Zendesk KB, Intercom Articles, Salesforce Knowledge) or via a scheduled sync from any URL / feed / file drop. Every source has a freshness rule so stale content is deprioritized in retrieval. When docs change, the index updates within an hour. Quarterly retraining is included in the monthly retainer.
Native integrations for Zendesk, Intercom, Freshdesk, Front, HubSpot Service Hub, Salesforce Service Cloud, Help Scout, Kustomer, and Gorgias (for e-commerce). Also Slack for internal Q&A over the same corpus and for agent handoff notifications. Anything with an API or webhook we can wire — custom integrations get scoped and priced in the setup fee up-front, no surprise line items later.
Every conversation is one of four outcomes: (1) resolved with citation, no ticket created — deflected; (2) resolved but customer still filed a ticket — half-credit deflection + signal that a doc needs rewriting; (3) handed off to a human with full context — assisted, not deflected; (4) hard failure — logged for review. Numerator = category 1 + half of category 2. Denominator = total conversations. Reported weekly with a source-doc breakdown of which pages carried the load and which topics failed. You get the raw event stream via Segment / Amplitude / webhook so you can validate against your help-desk metrics — not just a dashboard that we made up.
3 to 4 weeks. Discovery call within 2 business days of deposit. Corpus ingestion + retrieval tuning in week 1-2 (this is the longest step and depends on how organized your docs are). Prompt engineering + help-desk integration in week 2-3. Soft-launch behind a feature flag for 10-20% of traffic in week 3 with your support ops team reviewing every answer. Full rollout once you're happy. 30 days of tuning included after that.
$500 deposit is fully refundable if we can't confirm scope or commit a kickoff date within 7 days. If we take on the build and your corpus turns out to be too sparse or contradictory to reach usable retrieval quality, we surface it in week 1 and refund the deposit — we don't take the setup fee if we can't make retrieval work. If you cancel post-launch, you own the corpus, the transcripts, the deflection data, and the prompt templates — we export on request and shut off billing the following month. No multi-year contract, no exit fee.