RAG copilot over your docs with cited answers & evals by Sunesh AgarwalRAG copilot over your docs with cited answers & evals by Sunesh Agarwal
RAG copilot over your docs with cited answers & evalsSunesh Agarwal
Cover image for RAG copilot over your docs with cited answers & evals
Most AI assistants answer every question, even when they shouldn't. In legal, compliance, support or internal knowledge, a confident wrong answer is worse than none. I build RAG assistants over your documents that cite their sources and say "I don't know" when the evidence isn't there.
What you get
An agent pipeline that plans, retrieves, reranks, generates and critiques, with hard caps on retries and token spend.
Citations checked against the sources the model actually saw. Invented ones are removed, not trusted.
A golden question set built with you, with faithfulness, recall and abstention measured before handover.
A web app or API your team can use, deployed on your cloud or low-cost hosting.
Proof from my own build. A compliance copilot over the EU AI Act and GDPR made 36 of 37 correct answer-or-abstain decisions with 0 false answers, 0.99 faithfulness and 0.97 recall@5, running on $0/month hosting.
Stack is Python, FastAPI, LangGraph, Postgres with pgvector and Next.js, adaptable to your tools.
Starting at$2,500
Duration3 weeks
Tags
Anthropic
LangChain
LangGraph
LLM Evaluation
Next.js
Python
RAG
Service provided by
Sunesh Agarwal Bengaluru, India
1
Followers
RAG copilot over your docs with cited answers & evalsSunesh Agarwal
Starting at$2,500
Duration3 weeks
Tags
Anthropic
LangChain
LangGraph
LLM Evaluation
Next.js
Python
RAG
Cover image for RAG copilot over your docs with cited answers & evals
Most AI assistants answer every question, even when they shouldn't. In legal, compliance, support or internal knowledge, a confident wrong answer is worse than none. I build RAG assistants over your documents that cite their sources and say "I don't know" when the evidence isn't there.
What you get
An agent pipeline that plans, retrieves, reranks, generates and critiques, with hard caps on retries and token spend.
Citations checked against the sources the model actually saw. Invented ones are removed, not trusted.
A golden question set built with you, with faithfulness, recall and abstention measured before handover.
A web app or API your team can use, deployed on your cloud or low-cost hosting.
Proof from my own build. A compliance copilot over the EU AI Act and GDPR made 36 of 37 correct answer-or-abstain decisions with 0 false answers, 0.99 faithfulness and 0.97 recall@5, running on $0/month hosting.
Stack is Python, FastAPI, LangGraph, Postgres with pgvector and Next.js, adaptable to your tools.
$2,500