RAGPilot is a light, efficient RAG engine that gives your LLMs precise context from your data platform — Airflow DAGs, Oracle procedures, DDL, and docs — without the overhead.
No spam. Early access invite when we launch.
Data engineers spend hours answering "what does this proc do?" — grepping through thousands of SQL files, DAGs, and stale docs. There's no fast path to answers.
Enterprise data platforms have thousands of stored procedures, DAGs, and DDL files. No context window handles that. Retrieval must be surgical.
Off-the-shelf RAG pipelines don't understand SQL lineage or DAG dependencies. They retrieve the wrong chunks and generate confident wrong answers.
New engineers spend weeks just understanding existing pipelines. Tribal knowledge lives in Slack threads and the heads of people who've left.
You can't trust an answer you can't verify. Every response from RAGPilot links back to the exact file, procedure, or DAG it drew from.
Point RAGPilot at your Git repos, Oracle database, and Confluence. It ingests DAGs, stored procedures, DDL, and documentation automatically.
RAGPilot parses SQL ASTs and DAG graphs to extract table-to-procedure-to-pipeline relationships — not just flat text chunks.
Ask in plain English. RAGPilot retrieves the most relevant context across your entire platform and grounds every answer with source citations.
Hybrid retrieval keeps latency low as your codebase grows. Deploy on-prem or in your VPC — your data never leaves your environment.
Semantic + keyword + graph retrieval, auto-blended per query. Gets the right chunk even when terminology varies across teams.
Understands that TABLE_X is populated by PROC_A, triggered by DAG_B. Answers trace dependencies automatically.
Every answer links to the exact file, line, and version it came from. No hallucinations that slip through unnoticed.
Run fully on-prem or in your VPC. Your code and schema never leave your firewall — critical for regulated industries.
Web UI, VS Code extension, and Slack bot. Ask questions without leaving your workflow.
Efficient chunking and indexing keeps retrieval fast across millions of lines of code. Sub-100ms p95 latency at enterprise scale.
WORKS WITH YOUR STACK
We're onboarding a small cohort of data teams to shape the product.
No commitment — just early access and a direct line to the founders.
ragpilot.ai · Built for data engineers, by data engineers.