Accelerate Enterprise GenAI
from Prototype to Production
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from Prototype to Production
Why ragbits
ragbits combines production-proven architecture with modular, swappable components, allowing teams to move fast.
Faster discovery
○ Go from idea to a working demo in days
○ Make discovery tangible with real data, real UX, and real constraints
Enterprise delivery
○ End-to-end GenAI architecture
○ Production-first design
○ Agentic, RAG, and LLM-native
Modular by design
○ Easily swap LLMs, retrieval strategies, etc.
○ Avoid vendor lock-in and costly rewrites as models and platforms evolve
Security & monitoring
○ Mitigate hallucinations
○ Systematically evaluate and optimize the prompt
○ Ensure complete observability for reliable AI performance
Reducing business risk
○ Enable early business and technical decisions
○ Backed by our experience delivering production GenAI systems
Proven in Production
ragbits powers production-grade GenAI for enterprises, enabling agentic workflows, RAG platforms, and copilots with faster delivery and lower risk.
project
From Manuals to Answers: Fast, Accurate Tech Support via RAG-Powered Chatbot
In just 4 weeks, we delivered a pilot using ragbits, our in-house GenAI framework, to build a chatbot that answers user questions by extracting data directly…
case-study
AI-Driven Report Generation – Boosting Efficiency with 98% Recall
The system boosted report generation efficiency, achieving 98% recall in retrieving insights. It provides structured recommendations, auto-generates report entries, and reduces manual…
project
Revolutionizing Geospatial Tech with AI-Powered Assistant
Our solution enhanced the client’s tool, improving user experience, streamlining onboarding, and extending accessibility to non-technical users, making complex data easier…
project
Accelerating GenAI Development with ragbits
ragbits enables developers to bypass repetitive tasks, jumpstarting projects with pre-built components.
From Discovery to Production, Without Rebuilds or Dead Ends
We take GenAI from early validation to production-ready systems through a structured path, reducing risk, accelerating delivery, and ensuring every phase builds toward scalable, enterprise-grade outcomes.
Discovery & Proof of Value
Fast validation without committing to fragile architectures
We rapidly turn GenAI ideas into working prototypes to validate business value, technical feasibility, and user experience, before scaling investment or complexity.
MVP & Production Deployment
From first measurable value to reliable enterprise operations
We take validated GenAI solutions into production, expose them to real users and traffic, prove business impact, and harden them into reliable, scalable systems.
Scale & Evolve Over Time
Continuous improvement without rebuilds or re-architecture
We scale GenAI solutions across teams and use cases while continuously improving performance, cost efficiency, and capabilities, without disrupting operations or rebuilding foundations.
How We Accelerate GenAI
with ragbits
ragbits is our open-source framework for building production GenAI systems. Our Engineers use it to take you from the first prototype to scaled deployment – working embedded with your team, on your data, against your actual problems.
Discovery & Proof of Value
Full-stack PoC out of the box
ragbits ships with a Chat UI, authentication, and API layer. No infrastructure work required – deploy a working system and put it in front of stakeholders immediately.
Connect to your data in hours
Built-in ingestion for 20+ file types, connectors to S3, GCS, and Azure, and plug-and-play support for Qdrant, Weaviate, and PgVector. Test RAG on your actual enterprise data, not synthetic samples.
Validate use cases
Pre-built modules for document search, conversations, and structured querying mean you evaluate whether GenAI solves your problem – without first building the infrastructure to ask the question.
MVP & Production Deployment
Production-tested architecture
Agent orchestration, multi-step workflows, hybrid search, and retrieval pipelines – implemented and validated across 10+ enterprise deployments. You build on patterns that already work in production, not prototypes.
Observability from day one
Tracing, monitoring, and evaluation are embedded into the framework via OpenTelemetry, Logfire, and Grafana. Debug in real time. Ship with audit trails already in place.
Evaluate before you deploy
Built-in evals let you systematically test prompts, measure retrieval quality, and catch regressions before they reach users. Not after.
Scale & Evolve Over Time
Swap anything, break nothing
Models, retrieval strategies, and integrations are independently replaceable. Support for 100+ LLMs via LiteLLM and any vector store. No vendor lock-in – your architecture outlives any single provider.
Scale linearly
Distributed, stateless components with Ray parallel processing. The same code that powers your PoC handles production traffic. No rearchitecture required.
Optimize from real usage
Collect user feedback, run hyperparameter tuning, benchmark cost against quality – systematically, in production. Every deployment cycle makes the system better.
Enterprise-Grade GenAI, Proven in the Open
ragbits is a production-ready GenAI framework backed by real engineering experience and an active open-source community, with 1,600+ GitHub stars and 130+ forks.
Explore the documentation to understand the design principles, or get started in minutes with a single CLI command to spin up a working GenAI application.
Watch the Technical Demo
In this session, we break down what it really takes to move from a quick RAG demo to a scalable, reliable system. Without duct tape and fragile hacks.
Frequently Asked Questions
What is ragbits?
ragbits is an open-source framework for building GenAI apps, RAG apps, and agentic apps quickly and efficiently. It provides modular building blocks for ingestion, vector stores, chat UIs, and now agentic workflows, so developers can focus on business logic instead of infrastructure.
How does ragbits help build agentic GenAI apps?
With ragbits v1.1, you can turn any RAG application into an agentic app. Define simple Python functions as tools, combine them with LLMs and prompts, and build AI agents that reason, act autonomously, and call external tools – all with minimal code.
Can I use ragbits to implement an open-source RAG app easily?
Yes. ragbits was designed to help developers implement open-source RAG apps in hours instead of days, providing ready-to-use modules for document ingestion, retrieval, and chat pipelines.
Is ragbits suitable for production GenAI deployments?
Absolutely. ragbits has been used in production across multiple client projects, with teams reporting faster deployment, stable performance, and simplified agentic workflows. It also has over 1.4k stars on GitHub, confirming its reliability and developer adoption.
Which vector stores does ragbits support for my RAG app?
ragbits integrates with Weaviate, Qdrant, PgVector, and other popular vector databases, allowing flexible storage and retrieval solutions for your RAG pipelines.
How do I implement an open-source agentic app with ragbits?
You can start by installing the ragbits-agents package, defining your agent tools as Python functions, and using the built-in Agent API to create agentic workflows. The documentation provides step-by-step guidance for building agentic apps easily.
Does ragbits support streaming outputs in GenAI apps?
Yes, ragbits supports streaming outputs by default. This feature improves user experience by delivering partial results instantly instead of waiting for full completion.
Is ragbits free to use?
Yes, ragbits is fully open source under the MIT license, allowing integration, modification, and commercial deployment without restrictions.
Where can I see a demo of ragbits in action?
Check out our demo section for a walkthrough of building agentic and RAG apps using ragbits, or read our case studies to see how teams are using ragbits in production.