We've raised $6.5M to kill vector databases.
Every system today retrieves context the same way: vector search that stores everything as flat embeddings and returns whatever "feels" closest.
Similar, sure. Relevant? Almost never.
Embeddings can’t tell a Q3 renewal clause from a Q1 termination notice if the language is close enough.
A friend of mine asked his AI about a contract last week, and it returned a detailed, perfectly crafted answer pulled from a completely different client’s file.
Once you’re dealing with 10M+ documents, these mix-ups happen all the time.
VectorDB accuracy goes to shit.
We built @hydra_db for exactly this.
HydraDB builds an ontology-first context graph over your data, maps relationships between entities, understands the 'why' behind documents, and tracks how information evolves over time.
So when you ask about 'Apple,' it knows you mean the company you're serving as a customer. Not the fruit.
Even when a vector DB's similarity score says 0.94.
More below ⬇️
Just heard this line by Ankur Warikoo and it hit hard:
“I’m not who you think I am.
I’m not who I think I am.
I am what I think you think I am.”
Basically most of us aren’t living as our real selves, we’re living as a version shaped by how we believe others see us.
Life isn’t a race, it’s a garden.
Some flowers bloom early, some bloom late. What feels like delay is often depth. The time spent learning, healing, and struggling quietly builds roots others can’t see. When your season comes, you won’t just grow, you’ll stand tall.