The Data Bottleneck
In an era of "vibe coding" and instant iteration, product teams can ship beautiful UX and powerful features at lightning speed. But data is the exception. It remains a slow, siloed, and untrustworthy bottleneck that kills momentum and frustrates both your developers and your customers.
The typical in-house build follows a predictable, painful arc:
Slow Time-to-Market
What starts as a "simple" feature quickly balloons into a massive engineering effort. Building a performant, secure, and flexible analytics backend from scratch distracts your team from your core product for months, if not quarters.
Inflexible User Experiences
After all that work, the result is often a grid of static charts. Your customers can see their data, but they can't interact with it or answer their "next question." Attempts to bolt on AI Q&A fail because they aren’t built on the right foundation, with unreliable answers eroding trust.
Failure to Scale
The solution that worked for 10 customers grinds to a halt at 1,000. Scaling an analytics backend is a notoriously difficult infrastructure problem that causes dashboards to time out and queries to fail.
Skyrocketing Total Cost of Ownership
A proof-of-concept AI chatbot is easy; a production-ready, multi-tenant data product is not. Without a durable foundation, teams get stuck fixing brittle queries, tuning prompts, and rebuilding trust — long after launch.
A Modern Data Toolkit
To remove the data bottleneck, you need a toolkit that matches the speed and agility of your team. Credible provides a modern data toolkit built around a context engine — giving developers the infrastructure and guardrails to deliver trustworthy, scalable embedded analytics experiences.
Make Data Exploration Accessible and Trustworthy
Credible gives teams the foundation to build data experiences that are easy to explore and safe to trust. Governed semantic models encode shared definitions and assumptions, and Credible’s context engine delivers that meaning wherever analytics are embedded — so users can ask questions and understand results without teams re-implementing business logic.
Empower Developers with an AI-Assisted Workflow
Credible fits naturally into modern development workflows by integrating with AI-assisted coding tools like Cursor. Developers use their preferred agents, while Credible supplies governed context through MCP to assist with generating and refining Malloy queries and embedded analytics code.
Flexible Components for Analytics at Scale
Credible provides APIs, a React SDK, and MCP support to embed analytics directly into your application. Business logic lives in governed semantic models — not application code — so teams can deliver consistent, trustworthy analytics and evolve them safely as products grow, without rewriting SQL or duplicating logic across services.
A Foundation for Better Products
Our platform is fundamentally different — it’s designed to help you build, scale, and manage data products with the speed and reliability of modern software.
Enterprise-Ready by Design
Multi-tenant isolation, fine-grained access controls, and full audit logging—built in, not bolted on. Credible's Data Plane scales to heavy workloads while every query routes through versioned semantic models you control. Your customers get fast, reliable analytics; you get governance and visibility across every user and query.
A Composable Language for Richer Analytics
Built on Malloy, a powerful open-source language for data, Credible makes it easy to compose and reuse analytical patterns. Analysts, developers, and AI agents can build richer data experiences with clear, concise logic — dramatically reducing complexity and development time.
Optimized for Scale
Credible is designed for efficient scaling across customers and workloads. Intelligent query optimization and caching help control warehouse costs, while built-in usage analytics reveal what data your customers actually care about — so you can optimize for impact, not guesswork.
