Powabase
Build AI apps with Postgres, RAG, and agents
1.1K followers
Build AI apps with Postgres, RAG, and agents
1.1K followers
Powabase is a backend-as-a-service for AI-native applications, combining Postgres, RAG, agents, memory, workflows, and automation primitives in one platform. It helps agencies and in-house IT teams build new AI apps or add AI automation to existing products without stitching together fragmented infrastructure. Designed to work seamlessly with modern coding agents, Powabase helps teams ship faster while building more robust, token-efficient systems.
I love working with @Powabase and @hunter_powabase for Ryden Solutions, the first & leading life science continuous quality and compliance assessment platform simulating FDA inspectors at all times while also becoming more efficient. Hunter's platform has simplified development and set us up for long term success as we scale.
Powabase
@hunter_powabase @adam_foresman Thank you so much! Ryden Solutions has been one of the most rewarding projects we've worked on — what your team is building is exactly the kind of regulated-industry use case that pushed us to take Powabase's primitives seriously. We have learned a ton from working with you. Excited to keep building together!
Powabase
@adam_foresman Thanks a lot Adam! Your positive feedback made my day!
Powabase is perfect for use cases like Ryden. It's ideal for anything document-heavy and privacy-sensitive.
Looking forward to our continued partnership!
Congrats team, loved this tool.
Are you also planning to add some usecase over the website?
Powabase
Powabase
@vrijraj Yes, absolutely. In fact, we already have a good collection of Powabase serving as the backend for a number of projects that we undertook ourselves.
The biggest one to date is a legal research platform made for the Swiss branch of a global insurance company. The goal is to index the entirety of Swiss laws and court decisions to serve as evidence / advisory for eventual claims processing automation.
We are offering a "free MVP build" program where Powabase's forward-deployed engineers build an MVP on top of Powabase for companies with concrete app ideas, for free. You can find out more here: https://powabase.ai/free-mvp/
Would love to hear more about what apps you may be trying to build!
Powabase
@vrijraj thanks for your support!
Powabase
@vrijraj Really appreciate the support, means a lot! Curious what you're working on — if you tell me a bit about it I'm happy to help you get it running on Powabase.
ConnectMachine
We tried building something similar internally and realized maintaining embeddings, ingestion and orchestration pipelines becomes a full-time job. Good stuff.
Powabase
@syed_shayanur_rahman It is a lot of heavy lifting for sure. The process gets even more frustrating as you build it out for multiple clients over and over.
Lancepilot
Powabase
@odeth_negapatan1 Thanks so much, really appreciate the encouragement!🙏
Great question, it's a classic trade-off: abstraction (convenience) vs. control. Powabase tries to strike the best balance here, informed by our own experience building across a lot of use cases during our dev shop days.
If you're building AI-native apps with RAG or ReAct agent orchestrations, you'll find Powabase easy to use right out of the box. But if your application needs something custom, you're not locked into the full abstractions. You can take the relevant "intermediaries" and build a custom layer on top of them to fit your design specs.
For example, say you need to supplement retrieved RAG context with metadata stored elsewhere. You can invoke the context_handler object to grab the content that normally would have been fed into the agent, then enrich it with your own logic before forwarding it along. Since everything is stateful, you have access and control over all intermediaries by directly handling the databases.
So rather than locking you in, the goal is to give you sensible defaults when you want them. It also saves you tons of coding agent tokens from having to build these abstractions from first principles or gluing them from open source frameworks.
If you share more about what you're building, we'd be happy to advise on which features would be most relevant!
RAG + agents on Postgres looks solid—how do you handle schema migrations/versioning when agent workflows evolve?
This is the first AI backend product that actually feels opinionated in a good way. Everyone keeps rebuilding the same RAG + auth + vector DB stack from scratch lol.
Powabase
@himani_sah1 Thanks a lot! We were inspired from our own experience building over 100 AI apps as a dev shop, so hopefully the lessons learned translated well into Powabase's design.
Finally someone saying AI apps need their own backend abstraction instead of another wrapper around OpenAI APIs.