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 HydraDB 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 ⬇️show more

If your AI agents keep retrieving the wrong context, explore HydraDB: Grateful to our investors who backed this vision: @Sky9Capital, @JeffDean, @gokulr, @shyamalanadkat, @klyap_, @0xJsum, @laura_yao, @caldbeckj, @anshulbhide, and @SeanZCai. @ashishkakran, @missionstcap, @prateeks, @MartinGTobias, @PickensAllison, @wadefoster, aekyi, @PuriSid, @mattsechrest, @CryogenicPlanet

Congrats!

Open source version:

We’re expanding our team - let’s chat?

DM'ed.

> The project "strawberry" and the fruit "strawberry" are the same word, but completely different contexts. VectorDBs cannot tell the difference. Except they can? The vector db simply abstracts fast storage and similarity computation via HNSW with a convenient API. Sure if you use static word embeddings they will map and be identical, but any decent text embedding model worth its salt today will be able to differentiate between these two concepts. And that has EVERYTHING to do with the embedding model and NOTHING to do with the vector db

tbh its not a vectorDB fault - its a function of how embeddings are computed and stored. different things. different problems. unfortunately no one wanted to hear about embeddings in our video.

Relational databases have existed since the advent of computers. Why do we need to reinvent the wheel every year?

RDBMS directly can't be used as context engineering infra for your AI to work with complex enterprise knowledge bases

You are saying that a competent tech team cannot reproduce what you offer simply by using sql and a good pre processing system to build the same relationships you tout?

And then on LLM requests, just show the returned relevant request into the context window ?

No error message. Just looks right. You'd never know.

No @hydra_db = ngmi

Not open-source? Nah.

if we open source will you become a customer?

if there is value in what the SAAS offers yes. If I can develop it myself with AI because it's overpriced then no. And if you don't open-source it the community will just build an OSS one anyway. So this is your chance to do that, own the repo, guide the development AND you have a ready made recruitment pool and advocate pool of developers who will be contributing, forking and adding to your product! A simple license that insists derivatives have to be open source and you can include their features in the core and you've got it all covered

This is exactly the problem I hit building an AI concierge for short-term rentals. Guest asks "where's the vacuum?" — pure vector search returns the cleaning fee policy. High similarity score. Completely wrong. So I stopped trusting embeddings alone. Built a hybrid system: → Semantic search (0.65) + full-text match (0.25) + recency bias (0.10) → Live context injection (who's checked in, which property, what time) → 3-level gatekeeper that only surfaces emergency info when the situation actually calls for it All in PostgreSQL. No separate vector DB. No $6.5M. The real unlock isn't better embeddings — it's knowing WHEN and WHY someone is asking.

we're expanding our team. let's give it a shot?

5 weeks. Solo. 128K lines of Python. Running in production right now handling real guest conversations 24/7. If that's the energy you're hiring for — DMs open

@contextkingceo Pretty sad when you can’t even write your own tweets

@contextkingceo 🤖

Finally someone brave enough to say it out loud: vector DBs are gaslighting our agents.

LETS SHOUT THIS OUT

this is why AI search across internal tools always feels slightly off

100%

LFG! Congrats guys. Was so much fun working with you on this.

I dont want my agents to reset their brains every session. Moving away from flat vector retrieval toward a structured memory tree is truly a great practical approach for solving agent continuity.

Flat embeddings + 10M docs = confident hallucinations at scale. The fix isn't a better embedding model. It's a fundamentally different retrieval layer - one that tracks relationships, ownership, and temporal state. Super proud to be building this since Day 0 Big year ahead 🚀 and ofc, congrats boss - lesss goo

yessir, can we please make @hydra_db 100x better?

@hydra_db Yessir

LLM search works great on 500 docs, dies at 10M. And you never realize until it's too late.

LLM search at demo day: genius LLM search in prod: confidently returns your CEO's lunch order as a "relevant policy document"

Finally someone did something about this, cool launch video too.

Congrats @contextkingceo! "Similar ≠ relevant" is the exact bottleneck of naive RAG rn. great to see you've built this 🤗

thank you @DataChaz! that bottleneck is exactly what pushed us to build this

so you're saying it won't go looking for my ex when i ask it to find X?

nice marketing video. where's the benchmark?

A lot of Better portcos are going to love this Nish — sending your way! Congrats on the launch. 🚀

Thank you sooo much @vaibhavbetter! So excited to have you onboard

Interesting idea. Vector search is great, but it struggles with context as datasets get larger. Adding a graph or ontology layer to capture relationships could help avoid a lot of those weird retrieval mistakes. Congrats!

exactly, and that's what we're solving for at @hydra_db!

lowk @turbopuffer is still very good but will try out ur product soon

@turbopuffer we think turbopuffer is great - we solve different problems :) if you want relations, timeline of how context has evolved, deal with messy unstructured knowledge then you need to try @hydra_db

Congrats on the launch! - x icon in footer still redirects to old handle: - site title still says cortex - the site's opengraph thing still shows cortex just wanted to share in case it helps!

thank you for pointing it out

Excited to back since day one :) Excellent anti-agi hedge, but I haven't been so excited about a vector db adjacent product since turbopuffer. There are personally some inane use cases around migrating enterprises to this context graph to better collect their reasoning traces for rl envs!

Ha anti agi hedge I love that. Grateful for your support and counsel as always!

Damn dude you got a lot of investors

grateful for their support

Did you mean $60.5m?

1. Don't want to book a demo 2. Pricing page loops to homepage 3. Several links in footer do not exist 4. Privacy Policy and Terms of Use are crossed links Lots of errors on the vibe code template site for HydraDB.

the problem statement is very real and have faced such issues during multiple client projects, would love to test out hydradb. congratulations, Nishkarsh!

this is sooo real...i feel it

Amazing - this goes in our next launch video

VectorDB are really broken and they just don't work. Excited to try out Hydra!

will wait to hear about your experience, my DMs are always open:)

Yes, going to this a try this weekend!

man with db tech to sell says other DB tech is bad ignoring the larger search problem

No 1 failure point is retrieval and most of us on X is arguing mostly about benchmarks. Lol

benchmarks won't save you when retrieval grabs the wrong client's contract

99% of AI companies are building on fundamentally broken retrieval. The 1% who figure this out first will own their vertical.

Actually useful vs the usual "10 AI tools" threads.

Every company building AI agents needs this. Some of my most gratifying moments as an investor have been hearing how much your customers love the product.

hearing things like that from customers is the best part of this whole journey 🙏

Love the idea of the librarian to capture the power of hydradb. Humans remember through a web of connections and our memory evolves as context changes. Great to see that finally AI can do the same! Congrats on the launch @contextkingceo and @hydra_db team!

@hydra_db grateful for your support always @prateeks!