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LLM Integrations & RAG Systems
We connect language models to the knowledge your team already relies on, making answers more accurate and more useful.
◆ When you need this
From vector search to retrieval pipelines, we design systems that let AI ground itself in your documents, products, and internal sources.
When you need this
Teams who need AI to answer from the truth inside the business instead of generic public knowledge.
Talk to us about LLM Integrations & RAG Systems →What we deliver
◆ Scope
What this service covers.
01
OpenAI Integration
02
Claude Integration
03
Gemini Integration
04
Llama & Open Source Models
05
RAG Pipelines
06
Vector Databases
07
Enterprise Search
08
Knowledge Base Development
◆ How we work
How we work
Model selection
We choose the model — OpenAI, Claude, Gemini, or an open-source option — based on data sensitivity, latency, and budget.
Architecture
We design the retrieval pipeline: how documents are chunked, embedded, and indexed so answers stay grounded in your content.
Vector setup
We stand up the vector database and connect it to your real knowledge base, not a demo dataset.
Evaluate
We test retrieval accuracy and answer quality against real questions before anything ships.
Deploy
We deploy with monitoring on retrieval quality, so drift gets caught early instead of after users notice.
◆ Proof
LLM Integrations & RAG Systems in the field
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AI Calorie, Heart Rate Tracker
A daily wellness companion for calories, heart rate, hydration, and eye-rest routines.
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◆ FAQ
Common questions
We choose based on the use case, privacy needs, latency, and budget — not hype.
◆ Ready to build
Ready to build with LLM Integrations & RAG Systems?
Higher confidence answers, better search, and a more useful AI layer across product and operations.