Service
RAG Application Development
Let AI answer from your knowledge, with sources. We build Retrieval-Augmented Generation (RAG) applications that search your documents and data first, then generate answers grounded in what they find — with citations, permissions, and quality checks.
AI
Milvus
Node.js
MongoDB
RAG Application Development
What this engagement is for
A generic chatbot guesses. A RAG system retrieves: your PDFs, help centre, tickets, or database records are chunked, embedded, and stored in a vector database; each question searches that index and the model answers from the retrieved passages. We build the ingestion pipeline, the retrieval quality work, and the evaluation set that tells you whether answers are actually improving.
Technologies
- AI
- Milvus
- Node.js
- MongoDB
- Best for
- Teams whose answers live in documents: support knowledge bases, internal wikis, manuals, policies, and product data.
- Typical timeline
- 5–10 weeks for a first grounded Q&A system
Documents flowing into a grounded RAG knowledge graph
Outcomes
What you leave with
We measure the work by the product decisions and assets you can use next — not by a slide deck.
Answers grounded in your content, with sources users can check
An ingestion pipeline that stays current as documents change
Permissions so users only retrieve what they may see
An evaluation set measuring answer quality over time
How we work
A short path from conversation to something real.
- 1
Audit the knowledge
We review your sources, formats, and access rules, and pick the first question set the system must answer well.
- 2
Build retrieval first
We tune chunking, embeddings, and search before polishing generation, because retrieval quality caps answer quality.
- 3
Evaluate and ship
We score answers against a fixed question set, fix the weak spots, then release with monitoring.
Deliverables
What we produce
- Document ingestion and chunking pipeline
- Vector index and retrieval layer (including Milvus where suitable)
- Chat or search interface inside your product
- Evaluation set, logging, and cost/latency monitoring
Questions
Before you start
PDFs, web pages, help-centre articles, tickets, and structured records are common. Messy or duplicated sources need cleanup first — we will tell you what is usable.
It depends on scale and hosting. We work with managed vector services and self-hosted Milvus; the choice follows your data volume, latency needs, and operations capacity.
Grounding, citations, permission filters, and an evaluation set reduce errors, but no system is perfect. High-stakes answers keep a human review step.
Sample documents, the questions users actually ask, who may see which content, and someone who can judge whether sample answers are right.
Related
Services teams often combine
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