Powabase ✓

Hello Powabase
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Powabase is an all-in-one backend development platform specifically designed for AI-native applications. Often described as a "Supabase for AI," it combines a Postgres database, Retrieval-Augmented Generation (RAG), and agentic workflows into a single framework, eliminating the need to stitch together fragmented infrastructure.

The platform not only supports complex, AI-based applications but also simplifies the development process via MCP and agent skill integrations with coding agents like Claude Code, Codex, Antigravity, OpenCode, etc.

Hello Powabase
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Powabase takes a document from raw upload to fully searchable knowledge with very little work on your part. When you add a document — whether it's a clean PDF, a scanned report, a Markdown file, or a web page — Powabase automatically reads and extracts its contents. For straightforward files it pulls the text directly, and for harder cases like scanned or image-heavy documents it falls back to advanced OCR and vision models, so messy real-world documents still come through cleanly. It even keeps track of the document page by page, which means answers can later point back to exactly where they came from.

Once the text is extracted, Powabase indexes it so it can be searched intelligently. Rather than forcing one rigid approach, it lets you choose the indexing strategy that fits your content — from breaking long documents into well-structured, context-aware passages, to summarizing whole documents, to building a navigable map of a document for more complex, multi-step questions. Behind the scenes it turns your content into embeddings and stores everything directly in your own database, so there's no separate vector service to set up, manage, or pay for.

When it's time to find something, Powabase supports meaning-based search, traditional keyword search, or a hybrid of both, and can refine results further with reranking and smarter query handling to surface the most relevant answers. The result is that the entire journey — extraction, indexing, and retrieval — works out of the box, giving you accurate, source-grounded answers without having to assemble or maintain a complex pipeline yourself.

Hello Powabase
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RAG — Retrieval-Augmented Generation — is the technique behind AI that can answer questions using your information rather than just what a model learned during training. Instead of relying on the AI's general knowledge (which can be outdated or simply make things up), a RAG system first retrieves the most relevant pieces of your own content, then hands them to the AI as context so its answers are grounded in real, trusted sources.

In Powabase, RAG is a built-in capability rather than something you have to assemble yourself. You bring your documents — reports, manuals, policies, web pages, knowledge bases — and Powabase handles the entire pipeline behind the scenes: reading and extracting the content, organizing it into a searchable knowledge base, and finding the right passages whenever a question is asked. When your application or AI agent needs an answer, Powabase retrieves the most relevant material and feeds it to the model, so responses stay accurate, current, and tied back to their original sources.

What makes this powerful is that everything works together out of the box. The same platform that stores your data also extracts your documents, indexes them, and powers retrieval — with flexible options for how content is organized and searched, and refinements like hybrid search and reranking to surface the best results. Your AI agents can simply tap into a knowledge base and instantly gain grounded, trustworthy knowledge.

Hello Powabase
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Powabase is designed so that developers can build intelligent applications without stitching together a dozen separate services. Everything an AI app needs — a database, authentication, file storage, knowledge, agents, and workflows — lives in one platform, accessible through a single, consistent API. You spin up a project, connect to it, and start building immediately.

A typical journey begins with your data. Developers get a real, production-grade database with built-in authentication, storage, and instant APIs — the same dependable foundation you'd expect from a modern backend. From there, you layer on intelligence. You can create knowledge bases by uploading documents and letting Powabase handle extraction, indexing, and retrieval, giving your app grounded, source-backed answers through RAG without building that pipeline yourself.

On top of that, Powabase lets you create AI agents that can reason, make decisions, and take action using tools — including your own custom tools and external integrations. When a single agent isn't enough, you can orchestrate several together in coordinated patterns, or design visual, drag-and-drop workflows that connect data, models, and actions into automated processes triggered by events, schedules, or webhooks. Results stream back in real time, so experiences feel responsive and alive.

Crucially, Powabase is fine-tuned for AI coding agents, with clean, predictable APIs that AI assistants can drive efficiently. That means developers can build much of their backend simply by describing what they want, with fewer errors and far less wasted effort.

The result is a remarkably short path from idea to production: developers focus on the unique logic and experience of their app, while Powabase provides the database, knowledge, agents, and workflows — the entire AI-native foundation — ready to use out of the box.

Hello Powabase
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Powabase brings together two layers of backend services in a single platform: the dependable foundation you'd expect from any modern backend, plus an AI layer with sensible out-of-the-box abstractions.

At the foundation is a real, production-grade Postgres database — open, powerful, and fully yours. On top of it, Powabase automatically generates secure APIs for your data, so the moment you define your tables, your backend is live. It also includes built-in authentication for managing users and sign-ins, file storage for documents and media, and realtime capabilities so your app can react instantly to changes as they happen. Fine-grained access controls (with Row-Level Security) keep everything secure, ensuring users only ever see the data they're allowed to.

Where Powabase truly stands apart is the AI layer built right alongside these services. It offers knowledge bases that handle document extraction, indexing, and retrieval to power grounded, source-backed answers. It provides AI agents that can reason and take action using built-in, custom, or external tools, along with the ability to orchestrate multiple agents together for more complex tasks. And it includes workflows — visual, automated pipelines that connect your data, models, and actions, triggered by events, schedules, or webhooks.

All of these services share the same project, the same data, and the same consistent API surface, with streaming support so experiences feel responsive in real time. The result is a complete backend — database, auth, storage, realtime, knowledge, agents, and workflows — unified in one place, so developers can build and run AI-native apps without assembling a stack of separate tools.

Hello Powabase
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Powabase treats retrieval as a configurable, multi-stage pipeline rather than a single similarity lookup — and almost all of the tuning lives in a knowledge base's retrieval config, so you can change behavior at query time without re-indexing.

At the foundation, embeddings are stored in Postgres with a pgvector HNSW index for fast semantic search, paired with a BM25 keyword index for exact matches like IDs and error codes. The recommended default, hybrid search, runs both and fuses the rankings, with a weight you can tilt toward meaning or keywords.

On top of that, several query-time refinements raise answer quality. Reranking pulls a wider pool of candidates and re-scores them with a cross-encoder for sharper precision. Query enrichment uses an LLM to rewrite the query — expanding synonyms and folding in recent conversation turns so follow-ups like "what about pricing?" resolve correctly. And metadata enrichment tags content with structured fields you can filter on at search time.

Powabase also handles documents that text-only systems struggle with: multimodal retrieval can return the original rendered page image instead of plain text, preserving tables, charts, and handwriting. Finally, retrieval adapts to the shape of your content through indexing strategies — from simple chunking to hierarchical tree search for long PDFs and graph expansion for cross-referenced corpora.

The payoff is that developers start with sensible defaults and incrementally layer on reranking, enrichment, metadata filtering, or multimodal retrieval — each a small config change, none requiring a re-index — to tune precision for their own documents and queries.

Hello Powabase
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Powabase can ingest a wide range of document types and turn them into searchable, AI-ready knowledge. On the document side it handles PDFs, Word documents, PowerPoint slides, and Excel spreadsheets, along with plain text, Markdown, and CSV files. It also processes images — PNG, JPEG, WebP, GIF, and TIFF — using OCR to pull out their text, and it can crawl and import web content directly from URLs.

It's especially capable with difficult real-world documents. Scanned PDFs and image-heavy files are run through OCR, and when standard extraction produces poor results, Powabase can fall back to stronger OCR models to recover the content. For complex layouts — tables, charts, forms, stamps, even handwriting — it can preserve the original page images so an AI model can reason over them visually rather than relying on text alone.

Beyond ingesting documents, Powabase works with your structured data too, since every project is backed by a full Postgres database with storage and instant APIs. The result is that whether your information lives in uploaded files, scanned images, web pages, or database tables, Powabase can process it and make it available to your AI applications.

Hello Powabase
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Powabase keeps the agents separate from the layer that coordinates them, which makes mixing different LLMs straightforward. Each agent is configured on its own and can run any model you like — Claude, GPT, Gemini, and others — so a single orchestration might pair a Claude agent for analysis with a GPT agent for synthesis and another model for routing.

Building one is simple: create the agents you want, group them into an orchestration, and choose how they should work together. Powabase offers three coordination styles — a supervisor that intelligently routes work to the right agent and combines the results, a sequential pipeline where each agent builds on the previous one's output, and a parallel mode where agents tackle the same task independently and their answers are merged.

Because the model is just a per-agent setting and the orchestration is a thin layer on top, combining multiple LLMs comes down to configuration: pick the best model for each role, choose a strategy, and Powabase handles the delegation, sequencing, or merging between them.

Hello Powabase
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Powabase's visual workflows let you compose how your AI and data operations run as a connected graph of blocks rather than as hand-written orchestration code. That shift brings a few concrete advantages.

The biggest is that complex logic becomes something you can see and reason about. A workflow is a graph of blocks wired together, where each step passes its output to the next, so the flow of data and decisions is laid out visually instead of buried in code. You can branch on conditions, fan out into parallel paths, loop in external APIs, and run custom code — all as blocks on the same canvas — which makes even intricate pipelines easy to understand and to hand off to others.

It's also a natural composition layer for everything else in the platform. Existing agents, multi-agent orchestrations, knowledge base searches, and database or storage operations all drop in as blocks, so workflows become the glue that ties your AI capabilities together into a repeatable process — no reimplementation required.

Workflows are flexible in how they're triggered, too. The same graph can be run on demand via the API, fired by an incoming webhook, or put on a schedule (interval or cron), so one design covers everything from user-facing actions to background automation and recurring jobs.

They're built for production operation. Workflows can be deployed for unlimited live use or "armed" for controlled one-shot testing, and every run records per-block logs, so when something goes wrong you can see exactly which step failed and why — turning debugging into inspection rather than guesswork.

Finally, there's a lower barrier to building them. A natural-language Copilot can generate and edit a workflow's blocks and edges straight from a chat description, so you can describe what you want and refine the graph visually — meaning you don't have to assemble the whole thing by hand to get started.

In short, visual workflows give you clarity, reusability, flexible triggering, production-grade observability, and a faster path from idea to working automation — all while reusing the agents, knowledge, and data already in your project.

Hello Powabase
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Every Powabase project gets its own complete, self-contained stack — its own Postgres database (with pgvector), API gateway, authentication, storage, realtime service, and AI worker — reachable at its own dedicated URL and secured by its own set of keys. This isolation exists to serve a few key purposes.

The first is tenant separation and security. Because each project runs on its own database and gateway behind its own credentials, one project's data, users, and AI resources are walled off from every other project's. A leaked or rotated key affects only that single project, and there's no shared database where one tenant could accidentally reach another's data.

The second is a complete, independent backend per project. Rather than a project being a thin slice of shared infrastructure, it gets the full set of services — database, auth, storage, realtime, RAG, agents, and workflows — all wired together and operating as one cohesive unit. Everything a given application needs lives in one place, under one URL.

The third is predictable, self-owned infrastructure. Each project's database is directly accessible over the standard Postgres protocol for migrations, ORMs, and BI tools, and its resources scale and operate on their own. You can connect to it, manage it, and even self-host the model behind it without being entangled with other workloads.

In short, the isolated stack is what lets Powabase be genuinely multi-tenant while still giving each project the feel and guarantees of a dedicated, private backend — secure by separation, complete in capability, and fully yours to own and operate.

Hello Powabase
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Powabase supports two deployment models, and the difference comes down to who runs the infrastructure.

The first is managed cloud. This is the hosted Powabase service: you create projects through the Studio, and each one is automatically provisioned as its own isolated stack — Postgres, the API gateway, auth, storage, realtime, and the AI worker — reachable at its own dedicated project URL. The platform handles routing, scaling, and the operational plumbing for you, so you can go from creating a project to making authenticated API calls in minutes without managing any servers.

The second is self-hosting. Because Powabase is built on an open foundation, you can run the platform on your own infrastructure rather than the managed cloud — useful when you need full control over where data lives, want to meet specific compliance or residency requirements, or prefer to own the stack end to end. Self-hosted deployments do take on a bit more responsibility for the surrounding infrastructure (for example, ensuring the gateway correctly preserves the project host, which the managed cloud handles automatically).

In both models the application surface is identical — the same REST API, the same per-project isolation, the same AI capabilities — so what you build doesn't change based on where it runs. You can also bring your own model and provider keys in either case, pointing Powabase at OpenAI, Anthropic, Google, or even self-hosted models, which gives you flexibility over the AI layer independent of where the platform itself is deployed.

Yes, Powabase does support self-hosting. The platform allows for easy transfer between Powabase's services and independent hosting, giving freedom to the developers to maintain and manage their own infrastructure.