RAG systems break
in production.
Weak retrieval. No evaluation.
This fixes that.
Retrieval pulls wrong context. Answers go unscored. Failures can't be traced. These aren't edge cases — they're how most RAG systems actually behave in real workloads. RAG Studio was built to fix all three.
~85–90%
answer accuracy across internal eval sets
Sub-2s
response time on optimized pipelines
9
retrieval strategies
−70%
hallucination rate vs. base LLM
Why most RAG systems
fail in production.
Building a demo that works is easy. Building one that handles real queries reliably — that's where most teams get stuck.
Failure mode 01
Similarity ≠ correctness
Vector search ranks chunks by semantic similarity — not by whether they actually answer the question. You get plausible-sounding but factually wrong context passed to the model, and confident wrong answers.
Failure mode 02
No way to measure if answers are correct
There's no feedback loop to measure answer correctness over time. Every prompt tweak is a guess. You can't prove a change helped — or even know when something quietly broke in production.
Failure mode 03
Debugging in the dark
When an answer fails, you can't trace which step broke — chunking, retrieval threshold, reranking cutoff, the prompt. Without an inspector that surfaces every decision, you're always guessing instead of fixing.
RAG Studio is built to fix all three.
Hybrid retrieval, automated evaluation, and full pipeline inspection — in one platform.
Ask anything.
Get cited answers.
Or an honest "I don't know."
Click through the tabs to see three real scenarios: a policy question with sources, a graceful failure when no context exists, and a step-by-step how-to.
- Every answer links to the source document
- If it doesn't know, it says so — no hallucinations
- Inspect the full retrieval pipeline behind any response
Why your current AI setup
breaks your users' trust.
The same question. Two very different answers — and only one builds user confidence.
Without RAG
⚠ Vague. Guessed. Not from your docs. Could be wrong.
With RAG Studio
Sourced from: refund-policy.pdf · page 3
When it doesn't know — it says so. Not what it guesses.
If no relevant context is found, RAG Studio responds with a graceful fallback — not a hallucinated guess. The RAG Inspector shows exactly why: zero matching chunks retrieved, no context passed to the model.
Generic LLM:
"Employee salaries typically range from $60K–$150K depending on level..."
RAG Studio:
"I don't have compensation data in my knowledge base. Please contact HR directly."
Debug, evaluate, and improve your AI.
Not just deploy it.
Four layers of intelligence that work together to give accurate, auditable answers — and the tooling to understand every decision.
Retrieval
Hybrid Search
Stop missing critical answers buried in your docs. Dense embeddings catch meaning; BM25 catches exact terms. RRF merges both — so neither wins alone.
OpenAI text-embedding-3-small + BM25 + RRF
Ranking
Neural Re-ranking
Vector similarity ≠ actual relevance. A cross-encoder re-scores every retrieved chunk against your exact query — putting the right context first.
BAAI/bge-reranker cross-encoder
Generation
Query Expansion
Vague questions return empty results. HyDE generates a hypothetical answer to embed; multi-query creates 4 paraphrase variants. Both capture what a single query misses.
HyDE + multi-query · GPT-4o / 4o-mini
Evaluation
Automated Scoring
Guessing if your AI got better is not a strategy. Upload ground-truth Q/A pairs, run eval sets, and get retrieval + answer scores you can show — and act on.
Cosine similarity · retrieval hit-rate
RAG Inspector — see every decision
Click any response and inspect the full pipeline: rewritten query, query variants, retrieved chunks with scores, reranked order. Know exactly why a bad answer happened.
From documents to deployed bot
in three steps.
Upload your documents
PDF, DOCX, images, Markdown. GPT-4 Vision handles scanned files. Everything is chunked, embedded, and indexed automatically.
Configure retrieval
Choose chunking strategy, enable hybrid search, reranking, HyDE, multi-query. Every parameter is visible and tunable.
Deploy anywhere
Embed on your website with one script tag, or use the API. Your bot is live in minutes, not months.
Know when your AI
is wrong — before
your users do.
Upload ground-truth Q/A pairs and run automated evaluation sets against your real knowledge base. Get retrieval hit-rate and answer similarity scores you can show to stakeholders — not just "it feels better."
Run your first eval freeEvaluation run · 50 test queries
PassedRetrieval hit rate
94%
Answer relevance score
89%
Citation accuracy
97%
Hallucination rate
0%
Internal evaluation · customer support knowledge base · 50 ground-truth Q/A pairs
Tested on real-world, mixed-format document sets
Evaluated across multi-document pipelines — PDFs, DOCX, plain text, and scanned images via GPT-4o Vision. Designed for production workloads, not curated demo datasets.
A system lifecycle,
not a feature catalog.
Every capability maps to a phase: build a working system, debug failures, measure quality, and improve over time. Not a chatbot wrapper — a complete retrieval engineering platform.
Ship a working production AI system
Find the exact answer, every time
Dense vectors + BM25, merged via RRF — catches what pure semantic or keyword search alone misses. Works on messy, real-world documents.
Ask better questions automatically
HyDE + multi-query rewrites vague or ambiguous questions before retrieval. Captures context that a single embedding inevitably misses.
Go live on any website in one line of code
One script tag. No backend plumbing, no integration work — your bot is live in minutes, not days.
Team Collaboration
Role-based access for your whole team. Owners, admins, members, viewers — everyone works from the same knowledge base.
Trace every failure to its root cause
See exactly why your AI got it wrong
Inspect every retrieved chunk, reranked score, query variant, and reranking cutoff behind any response. Not guess — trace. Know the exact step that failed.
Know when it's working — with numbers, not feelings
Know when your AI is wrong before users do
Upload ground-truth Q/A pairs, run eval sets, and get retrieval + answer scores you can show clients. Not 'it feels better' — actual numbers.
See what's breaking in production
Query failures, token costs, top questions, context quality — measured and actionable, not just logged. Know where to fix first.
Get better over time, not just bigger
Turn bad answers into better retrieval
Every thumbs-down is a retrieval failure worth diagnosing. User reactions become a signal — not just noise.
Roll back when a new prompt breaks things
Label, compare, and restore system prompt versions without losing what worked. Never ship a regression you can't undo.
See exactly why your AI
gave a wrong answer.
Not your best guess.
The RAG Inspector exposes the full pipeline behind every response — the rewritten query, every variant generated, which chunks were retrieved and why, how they were re-ranked, and what the model actually saw. 90% of tools don't give you this.
Retrieved chunks with scores
See every chunk that was pulled — with its vector and BM25 score, before and after reranking.
Query variants
Inspect the HyDE hypothesis and multi-query paraphrases generated from the original question.
Post-rerank ordering
Understand exactly which chunks made it into context — and why others were cut.
Context quality signal
High / low / none — so you know instantly whether retrieval succeeded before reading the answer.
Query rewrite
"What is the refund policy for annual subscriptions?"
→ HyDE hypothesis generated · 4 multi-query variants
Retrieved chunks · post-rerank
Chunk #3 cut — below rerank threshold (0.50)
Context passed to model
2 chunks · 847 tokens · High quality ✓
Built like infrastructure.
Not like a demo.
Every feature is accessible via REST API. Deploy as an embeddable widget, integrate into your stack, or build your own UI on top. Full control, no lock-in.
REST API
Full CRUD + streaming chat endpoints. JWT-authenticated. OpenAPI spec included.
Embeddable widget
One <script> tag. Drop into any HTML page, React app, or CMS. Zero config required.
Webhooks & event hooks
Document status, chat events, eval completions. Build automation on top.
Public token rotation
Rotate your widget's public token any time. Old embeds invalidate instantly.
<!-- Add to any page -->
<script
src="https://yourapp.com/widget.js"
data-chatbot-token="your-public-token"
></script>
/* That's it. Bot is live. */Works in any HTML page · React · Vue · Next.js · WordPress
Streaming chat API
Built for teams running
production AI. Not demos.
SaaS Founders
Add AI features your users will actually trust
Building AI into your product means your users will notice every wrong answer. RAG Studio gives you the evaluation and debugging tools to ship with confidence — not just fingers crossed.
- Measure answer quality before launch
- Cited responses users can verify
- Embeddable in any stack via API or widget
Support Teams
Deflect 60–80% of tickets without lying to users
Generic LLMs hallucinate policy details. RAG Studio answers only from your docs — and says "I don't know" when it can't. No wrong answers, no angry customers.
- Answers grounded in your actual docs
- Graceful fallback on out-of-scope questions
- Reduces queue without adding risk
AI Engineers
Build RAG systems you can explain and improve
You know the theory. What you need is the infrastructure — hybrid retrieval, reranking, evaluation pipelines, and debugging tools — already built and ready to tune.
- Full control over every retrieval parameter
- Pipeline-level traceability via RAG Inspector
- REST API for custom integrations
Real deployments,
not just experiments.
Pick your use case and ship a working, evaluated bot in under an hour.
Customer Support
Deflect 60–80% of repetitive tickets
Train your bot on help center docs, FAQs, and product manuals. Customers get instant, accurate answers — your team handles only what matters.
- Instant ticket deflection
- Accurate policy answers
- Hallucination-free responses
Internal Knowledge Base
Onboard new hires 3× faster
Give your team instant access to SOPs, HR policies, engineering runbooks, and internal docs — without digging through Confluence or Notion.
- Instant policy lookups
- Reduces Slack noise
- Always up-to-date answers
Documentation Q&A
Cut developer support tickets in half
Let developers ask questions about your API docs, SDKs, and changelogs in plain English. No more re-reading 200-page manuals.
- Natural language API queries
- Cited, verifiable answers
- Fewer support escalations
Start free. Scale when ready.
Experiment
FreeBest for founders and engineers prototyping their first production RAG system.
Full retrieval features — hybrid search, reranking, evaluation, RAG Inspector. Not a stripped demo.
- 3 projects · all features
- Hybrid search + reranking
- Evaluation suite + RAG Inspector
Production
ProBest for teams running live AI systems who need scale, seats, and support.
For teams shipping real workloads. Analytics, team seats, priority support.
- Unlimited projects
- 5 team seats + priority support
- Advanced analytics + all features
Need more? See all plans including Enterprise →
Build a RAG system
that actually works.
Upload your docs. Inspect every retrieval decision. Run evaluation sets against ground truth. Debug failures before your users find them — free plan, no credit card needed.