software · growth · AIKnowledge assistant
An internal assistant for policies, SOPs, product docs and past tickets.
RAG development
RAG development for AI that answers from your data.
Monocerix provides RAG development for companies that want AI to answer accurately from their own documents, wikis, tickets and databases. Retrieval-augmented generation finds the right information first, then the model answers with citations, respecting who is allowed to see what.
Planner HawkCarmicaGreet NinjaPlanner HawkCarmicaGreet Ninja
Trusted by Planner Hawk, Carmica, Greet Ninja & more
How RAG works
Find first. Then answer.
Language models do not know your policies, products or customers. RAG retrieves the most relevant passages from your sources at question time and gives them to the model, so answers are current and verifiable.
Most RAG quality problems are retrieval problems. We focus there: clean ingestion, smart chunking, hybrid search and re-ranking, measured with real questions.
- Accurate retrieval: hybrid search and re-ranking
- Citations: every answer links its sources
- Permissions: users only see what they may see
- Fresh: sources sync automatically
What you get
RAG systems we build.
Enterprise AI search
Search across Google Drive, SharePoint, Confluence, Notion and databases.
Vector database setup
pgvector, Pinecone, Weaviate or Qdrant, chosen for your scale and hosting.
Access control
Document-level permissions carried into retrieval.
Evaluation
Question sets and scoring for retrieval and answer quality.
Multilingual RAG
English and Arabic documents and questions in one system.
How we work
A working RAG system in 4 to 8 weeks.
Week 1
Sources & questions
Connect sources and collect real questions.
Week 1–4
Build
Ingestion, indexing, retrieval and answer generation.
Week 4–6
Evaluate
Score, tune and fix retrieval gaps.
Launch
Roll out
Pilot users, feedback and monitoring.
Markets we serve
Your knowledge, in your region.
Knowledge bases often contain sensitive data. We host retrieval and storage where your rules require.
UAE & Saudi
Regional hosting and Arabic document support.
UK & EU
GDPR-aligned storage and retention.
US & Canada
Single sign-on, audit logs and private deployments.
India
Search policies, catalogs and manuals in English and Hindi, with India-region hosting.
Thailand
Thai-language retrieval tuned for Thai word segmentation, with PDPA-aware access controls.
Buyer guide
Retrieval-augmented generation: what to plan for.
Data quality matters most
Outdated, duplicated or badly formatted documents produce poor answers. A short clean-up of your most important sources usually improves results more than changing models.
What it costs
A focused RAG knowledge assistant typically costs $6,000 to $25,000 to build. Enterprise AI search across many sources with permissions costs more. Running costs depend on usage and document volume.
Your quotation
A quote built for your exact scope.
Answer a few questions about your RAG system and you'll get a formal, itemized quotation with scope, timeline, team and milestones, usually within 48 hours. Quotes in USD, AED, GBP or INR.
Plan my RAG system
WE'LL ASK ABOUT
- Sources and document volumes
- Who will ask questions, and what
- Permissions and security needs
- Languages and hosting
Takes about 4 minutes. Your answers are saved as you go.
Proof
See it working for Carmica.
Carmica
CarmicaCar care studio · Pune
From “who’s best for PPF in Pune?” to the answer AI gives.
Carmica sells premium PPF, ceramic and graphene coatings. We rebuilt the site around an instant quote builder and an 85 GU gloss guarantee. Then we added local SEO and AEO, and connected an AI harness that follows up every quoted lead on WhatsApp.
4-stepWhatsApp follow-up run by an AI agent
Instantonline quotes for cars & bikes
24/7lead response, with no lead left un-followed
FAQ
RAG Development questions, answered.
Straight answers to what clients ask us most. Still curious? Ask us on WhatsApp.
What is retrieval-augmented generation?
A technique where the AI first retrieves relevant information from your sources, then writes an answer based on it, with citations.
Which vector database do you use?
Often PostgreSQL with pgvector for simplicity, or Pinecone, Weaviate or Qdrant at larger scale.
Can it respect document permissions?
Yes. Retrieval filters by the user's access so answers never include documents they cannot see.
Does it work with Arabic documents?
Yes. We use multilingual embeddings and test Arabic retrieval specifically.
Let's build something legendary
Make your knowledge instantly answerable.
Tell us about your sources and we'll send a RAG plan and quote.
Takes about 4 minutes Itemized, written quotation Reply within 2 working hours