Grounded, Not Guessed
Answers retrieved from your real documents instead of generated from generic training data.
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Call: +971 52 668 4165
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Mail: [email protected]
Knowledge Agents That Answer From Your Real Documentation
A model guessing from generic training data will eventually make something up. Codelabs builds retrieval-augmented agents that pull answers from your actual documentation, pricing and policies — and track exactly which sources are ready before the agent goes live.
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Source Readiness You Can See
Our own CodeLabs BOT runs with 2 of 2 knowledge sources ready — that same tracking ships with yours.
Why Choose Codelabs For Knowledge Agents
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Responses sourced from your real documents, reducing the risk of confident-sounding fabrication.
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See exactly which knowledge sources are indexed and ready before the agent goes live.
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Update a source document and the agent's answers update with it — no retraining a model.
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Common questions answered instantly from documentation, freeing your team for harder cases.
Real Work, Real Results
Agentic AI
Knowledge Retrieval
CodeLabs BOT
CodeLabs BOT runs with 2 of 2 knowledge sources ready, grounding its answers about Codelabs' own services and pricing in real documentation rather than guessing — the same retrieval setup available to clients.
Read Full Case Study
Our Knowledge Agent Process
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We gather and structure your real documentation, pricing and policies for retrieval.
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Sources indexed so the agent retrieves the right passage before generating an answer.
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Live rollout with a simple process for keeping source documents current.
What's Included
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Document & Source Ingestion
Your real documentation, pricing and policies structured for retrieval.
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Retrieval-Augmented Answers
Responses grounded in actual source material, not generic model output.
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Source Readiness Tracking
Visibility into which knowledge sources are indexed and ready.
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Accuracy Testing
Answers validated against real questions before going live.
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Easy Knowledge Updates
Update a source document and the agent's answers stay current automatically.
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Multilingual Retrieval
Source material retrieved and answered in the language the customer uses.
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Usage & Cost Dashboard
Live visibility into lookups, conversations and AI spend.
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Ongoing Monitoring
Retrieval quality reviewed after launch to catch gaps in source coverage.
What Does It Cost?
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Growth$199/mo
10 agents with $60 of monthly AI credit — enough for a well-stocked knowledge base.
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Scale$499/mo
50 agents with $175 of monthly AI credit for large, frequently-updated knowledge bases.
Technologies We Use
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LlmLLM APIs
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VdbVector Database
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RagRAG Pipeline
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DocDocument Parsing
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EmbEmbeddings
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ClCloud Hosting
RAG & Knowledge Agents FAQs
Common questions about retrieval-augmented AI agents.
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What is RAG, in plain terms?
Retrieval-Augmented Generation means the agent looks up relevant passages from your actual documents before answering, instead of relying purely on what a model was trained on. It's how you keep answers accurate and current.
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What kinds of documents can the agent use as sources?
PDFs, help center articles, pricing pages, internal wikis and structured data can all be ingested as sources, depending on your setup.
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How do we know the agent's sources are actually up to date?
Source readiness is tracked and visible — our own CodeLabs BOT shows 2 of 2 sources ready, and yours gets the same tracking so you're never guessing what the agent actually knows.
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Does this eliminate hallucinated answers entirely?
It significantly reduces the risk by grounding answers in real source material, but no system is 100% immune. We test against real questions before launch and monitor conversations afterward to catch and fix weak spots.
Let's Connect With Our Experts
Get valuable consultation from our professionals to discuss your project. We are here to help you with all of your queries.
