The Reliable Alternative to RAG
TL; DR: Captain delivers the most accurate general-purpose knowledge search engine ever built.
If you need to search through large text or multimodal files, and:
- Don’t have time for a tedious RAG sprint -or-
- Your current search pipeline lacks accuracy
We should talk. → runcaptain.com/sales or founders@runcaptain.com
https://www.youtube.com/shorts/RxMPHqou94o
In a world where 90% of enterprise knowledge cannot be stored in traditional databases, this ‘unstructured data’ is an untapped goldmine for decision-making.
The issue is that current RAG solutions have poor retrieval quality overall and are only performant on pre-optimized question types.
Captain unlocks better responses thanks to an effectively Infinite Context window. We distribute it across many LLMs in parallel + some embeddings sprinkled in, and then ‘Map-Reducing’ responses down to a single output.
Without context limits, we have great freedom to optimize our retrieval engine for maximal accuracy. We can now play with a truly dynamic top-k or just run the LLM exhaustively (if a full knowledge audit is needed).
We’re two lifelong builders obsessed with data!
- Lewis (right) solved hallucinations for code generation through his previous startup.
- Edgar (left) researched NLP+AI and has built production RAG pipelines for the past 3 years.
We’ve seen the pains and limits of these systems from the start, and the industry is primed for a more accurate alternative.
This past summer, we met with every engineer we could at Snowflake and Databricks, and we kept hearing the same thing again and again:
There’s no good scalable unstructured data search.
Until today.
Captain abstracts the engineering of retrieval entirely. Connecting files is all that’s needed, and we’ll beat your RAG's accuracy. Guaranteed.
Our Ask
If you know a CTO or Head of AI at a mid-market or enterprise AI-native (or are building one yourself), we would love an introduction!
Check us out at RunCaptain.com
or reach us directly through founders@runcaptain.com
Happy Shipping! 🚢
