AI
Arabic RAG chatbot with private deployment
A regulated fintech team needed Arabic retrieval and bilingual answer quality without moving sensitive data to external infrastructure.
Private deployment with bilingual answer quality
/ RAG Development
RAG development for LLMs that know your business.
Retrieval-augmented generation — done right. Chunking, embeddings, hybrid search, reranking and evals — so your AI answers from your data, not from the internet's guesses.
Shipped in USA · Europe · Middle East · Pakistan
SaaSHealthcareFintechEcommerceDeveloper toolsInternal platforms
/ In short
RAG (retrieval-augmented generation) development is the engineering of AI systems that combine a large language model with a search over your own data — using embeddings, vector databases, hybrid retrieval and reranking — so the model answers grounded in your documents.
/ What this service includes
What we deliver with RAG Development Services.
01
Document RAG
PDFs, Notion, Confluence, Drive, SharePoint, SQL.
02
Hybrid retrieval
Vector + BM25 + reranker — not just cosine similarity.
03
Evals & quality
Golden sets, regression tests, human review loops.
04
Multi-tenant RAG
Per-tenant indexes with strict isolation.
05
On-prem RAG
Fully local with open models and local vector DBs.
/ Is this right for you?
Honest fit check.
A plain answer up front. We'd rather not sell you something you don't need.
✓ Yes if
- Your LLM answers need to come from your documents, not the internet
- You need sources cited on every answer
- You care about accuracy evals on a real golden set
× Not a fit if
- You only need creative writing — RAG isn't what you need
- You want fine-tuning only — see LLM Integration or ML
- You won't do quality review or eval work — it's how RAG stays good
/ Technologies
Our stack, battle-tested.
OpenAIAnthropicLlama 3MistralpgvectorPineconeWeaviateQdrantLlamaIndexLangChain
/ Comparison
RAG vs fine-tuning vs long context
Your need
Recommended
Knowledge that changes often
RAG
Consistent tone / format
Fine-tuning
One huge document per query
Long-context LLM
Private data, must stay on-prem
RAG with open models
Best overall for support/KB bots
RAG (often + light fine-tune)
/ Pricing & timeline
Typical range
Custom quote after scoping
Timeline
5 – 16 weeks
Team shape
1 AI lead · 1–2 engineers · 1 domain expert (client-side)
Pricing is quoted after discovery based on scope, team shape and delivery timeline. On-prem deployments with open models are scoped separately from SaaS-LLM builds.
/ Why us
What makes us different.
01
Senior engineers stay on the work
The people you meet in discovery stay involved through architecture, delivery and launch.
02
Search, performance and accessibility are built in
Metadata, schema, page performance and semantic markup are part of delivery, not a post-launch add-on.
03
Architecture is explained in writing
Tradeoffs, integrations and scope changes are documented so your team can audit decisions later.
04
Your team owns the output
Repos, infra, analytics and documentation live in your accounts from the beginning.
/ Relevant proof
Related case studies for this page.
Real delivery examples tied to this service area, so buyers can move from claims to shipped work.
AI
AI knowledge platform rebuilt on Next.js + RAG
A product team replaced a brittle Python knowledge surface with a grounded Next.js and RAG stack to improve onboarding and support resolution.
Stronger onboarding and lower support load
AI
AI operations workflow automation for document and ticket triage
An operations team automated intake, classification and escalation across email, documents and support queues without trying to remove humans from quality-sensitive decisions.
Less repetitive handling with quality-sensitive review preserved
/ Client signals
What clients noticed about this kind of work.
USA
“The difference was that Cuibit treated retrieval quality, evals and guardrails as part of the product, not as cleanup after launch. That is why the system earned trust internally.”
AF
Aisha Farooq
Head of Platform · Knowledge operations team
EU
“The automation worked because Cuibit did not try to remove judgment from the wrong places. The workflow got faster, but the team still kept control where quality really mattered.”
CM
Clara Mendez
Operations Director · Shared services team
/ Further reading
Related insights and buying guides.
Supporting articles that help buyers understand the tradeoffs, architecture choices and implementation details behind this service area.
AI Systems
What Makes Content Retrieval-Ready for AI Systems
Retrieval-ready content is structured, specific, self-contained, and easy for search systems, RAG pipelines, and LLM tools to extract accurately.
Read insight →
AI Development
What RAG Development Actually Includes
RAG development is more than connecting documents to a chatbot. It includes content preparation, retrieval design, evaluation, security, UX, and maintenance.
Read insight →
AI Development
How to Choose an AI Development Agency in 2026: RAG, LLM Integration, Web and Mobile Delivery
Choosing an AI development agency in 2026 is no longer just about prompt engineering. The right partner should be able to design retrieval pipelines, tool integrations, context-aware agents, and the web or mobile product layer that makes AI usable in the real world. This guide explains what to evaluate, which architecture patterns matter, and how to tell whether an agency can deliver production-grade RAG development and LLM integration.
Read insight →
/ FAQ
Frequently asked questions
RAG for knowledge that changes. Fine-tuning for style, format or tight latency. Often both.
Usually: bad chunking, embedding-only retrieval (no BM25, no reranker), no evals, no source-attribution. Fixable.
Yes — Llama / Mistral + local vector DB + your own GPUs or CPU-only for smaller models.
We test multiple strategies — fixed-size, recursive, semantic and document-aware chunking — and pick the one that scores highest on your golden eval set. There is no universal best approach.
Yes — we ingest PDFs, Word docs, Notion, Confluence, Google Drive, SharePoint, SQL databases and structured APIs into a unified retrieval layer.
Separate vector indexes or strict metadata filtering per tenant so each customer's data is isolated, searchable only by their users, and never cross-contaminated.
/ Next step
Ready to start?
Tell us about your project. A senior strategist replies within one business day — with a written first take.
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