RAG Systems
Retrieval-augmented generation over your documents, with citations, access control, and a path to keep the index fresh.
AI & Automation
What we deliver
RAG at XiteHub is a search and answer layer over the files your company already trusts. We ingest PDFs, wikis, tickets, and structured records, then retrieve the passages that should ground a reply. Access control follows the source system so a contractor does not see finance folders. Evaluation is run on questions your staff actually ask, and every answer can show the excerpt it used.
Fixed-price projects, dedicated teams, or an ongoing retainer, scoped around outcomes rather than billable hours.
Book a consultation80+
Projects delivered
2021
Founded in Karachi
10+
Countries served
Benefits
Why teams choose this service
- 01
Chunking and metadata tuned to your document types, not a one-size splitter
- 02
Citations back to file, page, or ticket so people can verify the answer
- 03
Permission filters at query time, copied from SharePoint, Drive, or your IAM
- 04
Hybrid search (keywords plus vectors) when product codes and names matter
- 05
Scheduled re-ingest so a policy update is not stuck in last quarter’s index
- 06
An evaluation set you keep, so a model or prompt change has a score, not a vibe
Process
How we deliver
A disciplined path from first workshop to production support. Senior engineers stay on the work after launch.
01
Source audit
List the systems, formats, and who is allowed to see each collection.
02
Chunking plan
Choose split size, overlap, and metadata so retrieval hits the right passage.
03
Index build
Embed, store, and smoke-test recall on a held-out question set.
04
Answer quality
Tune prompts and rerankers until citations match what a specialist would open.
05
Product embed
Put Q&A in Slack, a portal, or your existing app with the same ACL.
06
Govern
Set owners for stale sources and a review of failed questions each month.
Stack
Technologies we ship with
- Python
- LangChain
- pgvector
- Pinecone
- OpenAI
- FastAPI
- Unstructured
- PostgreSQL
- Redis
- Next.js
FAQ
Questions, answered
Typical timelines, engagement models, and how we work with your team on RAG Systems.
Continue
More in AI & Automation
- 01
AI Development
Applied machine learning for ranking, forecasting, extraction, and generation, with evaluation before production.
- 02
AI Agents
Agents that call your tools, follow a written policy, and complete multi-step work with a human checkpoint when needed.
- 03
Chatbot Development
Support, sales, and internal bots on web, WhatsApp, and helpdesk tools, grounded in your content and handoff rules.
- 04
Automation Solutions
Workflow and integration work that removes copy-paste between the tools your operations team already uses.
Ready to Start Your RAG Systems Project?
Book a free consultation with our solutions architects. We'll map requirements and share a clear roadmap within 48 hours.