Chatbots that answer from your data, not from guesswork.
Chatbots grounded in your documents, policies and systems, so they give accurate answers, show their sources and hand off to a person when unsure. We build knowledge chatbots with RAG and vector search over your own content, connected to your systems where they need to act. They stay inside your rules, protect private data and give leadership a clear view of what they say.
Why most chatbots disappoint
A chatbot that does not know your business is worse than none.
Confident, wrong answers
A generic model invents policies, prices and promises, and customers believe it.
Knowledge scattered across files
Answers sit in PDFs, wikis, tickets and spreadsheets, so the bot cannot find them and neither can your team.
No way to see what it said
Without logs and review, leaders find out about bad answers from angry customers.
Private data in the wrong place
Pasting sensitive content into a public tool puts customer data and compliance at risk.
A chatbot should reduce your support load, not add a new risk.
Grounding in your own data, clear hand-offs and full visibility turn a chatbot into something your team can trust.
Grounded answers
Why chatbots lose customer trust, and how to avoid it.
Here is what goes wrong with most chatbots and how we build ones that stay accurate.
Usually
A model with no knowledge of you.
It answers from general training data, so it cannot know your policies.
Lunova
RAG over your content.
Answers are retrieved from your documents and data, then written in plain language.
Usually
Guessing when unsure.
Instead of saying it does not know, the bot makes something up.
Lunova
Sources and fallbacks.
Answers cite their source, and the bot escalates when it is not sure.
Usually
A dead end.
The user gets stuck with no route to a real person.
Lunova
A clean hand-off.
A person receives the whole conversation, so the customer never repeats themselves.
Usually
Opaque quality.
Nobody reviews conversations, so errors repeat.
Lunova
Review and improvement.
Conversation logs and evaluation show where answers can improve.
The Journey is a seven-step process from discovery through growth, with clear outcomes at each stage.
How we work
The Journey
From vision to velocity — precision and ownership at every step.
-
DiscoveryDiscovery
Map questions and sources
We collect the questions people actually ask and the content that answers them.
A clear scope and content inventory -
IdeationIdeation
Decide what the bot should and should not do
We set the boundaries: topics, actions, tone, and when to hand off.
Rules agreed before build -
DesignDesign
Design the conversation and the knowledge
Conversation flows, retrieval design and hand-off paths, tested with real users.
Prototypes validated with real questions -
EngineeringEngineering
Build retrieval, chat and integrations
RAG pipeline, vector database, chat interface and system connections, demoed weekly.
Measured answer quality at each checkpoint -
SecuritySecurity
Test for leaks and abuse
Our security team tests for prompt injection and data leakage, and checks access to private content.
Findings fixed and retested before launch -
DeploymentDeployment
Launch with oversight
Zero-downtime rollout with logging, review tools, documentation and repository handover.
A controlled, observable go-live -
GrowthGrowth
Keep the knowledge fresh
Support, content-sync updates and quality reviews as your business changes.
A bot that stays accurate over time
How We Build Chatbots That Know Your Data
Questions & Content
What we deliver
- Question and intent analysis
- Content and data inventory
- Boundaries and hand-off rules
- Privacy review
Retrieval & Conversation
What we deliver
- RAG and vector search design
- Conversation flows
- Source citation
- Escalation paths
Bot & Integrations
What we deliver
- Ingestion and vector database
- Chat interface
- System integrations
- Evaluation set
Test, Deploy, Improve
What we deliver
- Independent security testing
- Zero-downtime deployment
- Conversation review tools
- Priority post-launch support
/ 4
Proof in production
Built, delivered and still running.
Real systems we built and still run, chosen for what they prove about our chatbot work.
P05 · AI SaaS
NOBS Marketplace
NOBS Marketplace · Engineering led by our CTO
A marketplace where an AI agent finds, scores and contacts link prospects, with people approving what matters.
Why it matters here → RAG and a vector database ground the agent in real data, with human approval on what matters.
- AI agent
- RAG & vector DB
- MCP servers
- Stripe
Why Organizations Choose Lunova for Chatbots
RAG and vector search in production AI platforms
We use retrieval and vector databases in production, not just in demos.
Answers that cite their sources
Each answer points back to your content, so people can verify it.
Private data stays private
Access to content is permissioned, and we test for leakage before launch.
Human hand-off built in
When the bot is unsure, a person takes over with full context.
100% code ownership
Pipelines, prompts and documentation are yours at handover.
Senior engineers, direct access
You speak to the people building your bot.
FAQ
Frequently Asked Questions – Chatbots That Know Your Data
Straight answers on accuracy, privacy, what a chatbot can access and who owns it.
Still have questions? Contact us
How does a chatbot learn our data?
We do not retrain a model for every change. We index your documents and data in a vector database and retrieve the relevant parts for each question (RAG), so updates take effect when your content updates.
How do you stop it from making things up?
Answers are grounded in retrieved content and cite their source. When nothing relevant is found, the bot says so and hands off to a person.
Can it do more than answer questions?
Yes. With the right permissions it can look up records, book a slot or open a ticket, with approval steps for anything sensitive.
Where does our data go?
We design for private handling: access controls on content, enterprise-tier model APIs, and independent testing for leakage before launch.
Can you fine-tune a model on our data?
Yes, when it is the right tool. We fine-tune on private data where it improves results, and often combine it with RAG for answers that stay current.
Who owns the chatbot and its code?
You do. Code, prompts and documentation transfer to you at launch, with no lock-in.
Still have questions? Contact us →
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Fixed price after Discovery
Scope, cost and timeline in writing before build.