Service

AI Chatbots Grounded in Your Own Knowledge

We build retrieval-augmented chatbots and assistants that answer from your documents, policies, and data — with citations and a clear human handoff, not guesswork.

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From Your Documents to a Grounded Answer

1Your Sources
2Ingest & Index
3Retrieve Relevant Passages
4Grounded, Cited Answer
5Human Handoff

§ 01 — Overview

What we do.

MetroNova Labs builds retrieval-augmented generation (RAG) chatbots and AI assistants that answer from your own content — documents, policies, product data, and knowledge bases. We index your sources, ground every answer in the relevant passages, cite where each answer came from, respect your access controls, and hand off to a person when the assistant isn't confident.

The difference it makes.

§ 02 — Why it matters

General-purpose chatbots make things up because they don't know your business. A RAG assistant answers only from your approved content and shows its sources — so people get accurate, trustworthy answers instead of confident guesses.

Grounded in your content

Answers come from your documents and data, with citations, so responses are accurate and verifiable.

Fewer hallucinations

Retrieval constrains the model to your approved sources instead of the open internet or its training data.

Respects who sees what

Permission-aware retrieval means the assistant only surfaces content a given user is allowed to see.

On-message, around the clock

Consistent, sourced answers any time — with a human handoff when confidence is low.

Common challenges this addresses.

§ 03 — Problems

Knowledge buried in documents and PDFs
Staff answering the same questions repeatedly
Generic chatbots that hallucinate
Answers with no source or citation
Sensitive content exposed to the wrong users
Content scattered across wikis and drives
Slow onboarding and ramp-up
AI pilots that never reach production

What we commonly build.

§ 04 — Use cases

Internal knowledge assistant

Let staff ask questions across policies, SOPs, and wikis and get sourced answers.

Customer support chatbot

Deflect common tickets with answers grounded in your help content and docs.

Policy & HR Q&A

Answer benefits, leave, and policy questions from the current handbook.

Product & sales enablement

Surface accurate product and specification answers for reps in the moment.

Document & contract search

Ask natural-language questions across large document and contract sets.

Website & product assistant

An on-site assistant that answers from your real content, not guesses.

What you get with this engagement.

§ 05 — Included

Source discovery & content assessment
Ingestion & chunking pipeline
Embeddings & vector search
Retrieval-augmented answer generation
Inline citations & source links
Permission-aware / access-controlled retrieval
Guardrails & human-handoff fallback
Evaluation & accuracy testing
Chat UI, widget, or API integration
Monitoring, feedback & content refresh

A clear path from first call to launch.

§ 06 — Process

Step 01
We identify your sources and the questions the assistant must answer.
Step 02
We build a pipeline to ingest, chunk, and index your content.
Step 03
We ground answers in retrieved passages and add citations.
Step 04
We evaluate accuracy and tune retrieval, prompts, and guardrails.
Step 05
We deploy in your channel, then monitor, gather feedback, and refresh content.

Grounded, cited, and honest about limits

RAG substantially reduces hallucination by grounding answers in your approved content and citing sources, but no AI assistant is perfect. We build in citations, confidence-based human handoff, access controls, and evaluation — and we design with privacy and applicable regulations in mind.

Capabilities

Technologies & capabilities.

Large language models
Vector databases (pgvector, etc.)
Embeddings & semantic search
Retrieval-augmented generation
Hybrid & keyword search
Evals & guardrails
Private / self-hosted deployment
API & CRM integration

Industries

Industries we serve.

Healthcare
Professional services
Finance & insurance
SaaS & technology
Education
Government & public sector

§ 07 — Questions

Frequently asked questions.

What is a RAG chatbot?

RAG stands for retrieval-augmented generation. Instead of answering from its training data alone, the chatbot first retrieves relevant passages from your own content, then generates an answer grounded in — and citing — those passages.

How is this different from a general chatbot like ChatGPT?

General assistants answer from broad training data and can confidently invent details about your business. A RAG assistant answers only from your approved documents and data, and shows the sources behind each answer.

Will it make things up?

Grounding answers in your content and citing sources greatly reduces hallucination, and we add guardrails and a human handoff for low-confidence questions. No system is perfect, so we also evaluate accuracy before launch.

Can it respect our access permissions?

Yes. We can implement permission-aware retrieval so the assistant only surfaces content a given user is allowed to see.

What content can it use?

Documents, PDFs, wikis, knowledge bases, product data, and databases — where access is available. We assess your sources and build an ingestion pipeline for them.

Can it run privately or self-hosted?

Yes. Depending on your privacy and compliance needs, we can deploy with private or self-hosted models and keep your data within your environment.

How do you keep answers up to date?

We set up content refresh so the index stays current as your documents change, and we monitor questions and feedback to improve retrieval over time.

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Every project is different. Contact us to discuss your requirements, goals, timeline, and technical needs. We review each request carefully to determine the right solution and whether the project is a good fit.

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