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RAG DEVELOPMENT
Retrieval-augmented generation that lets AI give accurate, cited answers from your documents, wikis, tickets and databases.
Answer accuracy
THE CHALLENGE
Confident, wrong answers.
Docs spread across many tools.
Out-of-date information.
Seeing what you shouldn't.
Hours spent hunting for answers.
No way to check sources.
Time searching for info
OUR SOLUTION
We build pipelines that ingest, chunk, embed and retrieve your content, then keep checking accuracy as you go.
WHAT'S INCLUDED
All your knowledge sources.
Tuned for accuracy.
Semantic + keyword.
Every answer has a source.
Respects permissions.
Measure and improve.
BENEFITS
Accuracy
Search time
Cited answers
Daily
Index refresh
HOW IT WORKS
01
Map workflows, data and goals.
02
A working pilot, quickly.
03
AI ready for production.
04
Accuracy, safety, rollout.
TOOLS & TECHNOLOGY
Cl
Claude
Op
OpenAI
Ge
Gemini
Ll
Llama
pg
pgvector
Pi
Pinecone
We
Weaviate
El
Elasticsearch
USE CASES
Ask company docs.
Grounded answers.
Policy lookup.
Product answers.
Literature search.
New hire Q&A.
INDUSTRIES
HOW WE WORK
Goals, users and limits.
Scope, roadmap and KPIs.
Sprints with a demo every week.
QA and launch support.
05
CASE STUDY
RAG
“Consultants find answers in seconds — with sources they can trust.”
94%
30 hrs
Saved/week
12k
Docs indexed
FAQS
Can’t find what you need? Our team is happy to help.
Still have questions?
Talk to a real person, not a bot.
AI that looks up the relevant content before it answers, so every response is based on facts and cited.
Drive, SharePoint, Confluence, Notion, databases, tickets and more.
We measure it with evaluation sets. It's typically 90%+.
KEEP EXPLORING
Chat interfaces.
Content generation.
Into your tools.
Book a free AI opportunity workshop. You’ll leave with ranked use cases and clear next steps.
Talk to Arjun & the team
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